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MonadicContextualClassification.m
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(*
Monadic contextual classification Mathematica package
Copyright (C) 2017 Anton Antonov
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Written by Anton Antonov,
antononcube @ gmail.com,
Windermere, Florida, USA.
*)
(*
Mathematica is (C) Copyright 1988-2020 Wolfram Research, Inc.
Protected by copyright law and international treaties.
Unauthorized reproduction or distribution subject to severe civil
and criminal penalties.
Mathematica is a registered trademark of Wolfram Research, Inc.
*)
(* :Title: MonadicContextualClassification *)
(* :Context: MonadicContextualClassification` *)
(* :Author: Anton Antonov *)
(* :Date: 2017-06-05 *)
(* :Package Version: 1.0 *)
(* :Mathematica Version: *)
(* :Copyright: (c) 2017 Anton Antonov *)
(* :Keywords: *)
(* :Discussion:
## Introduction
This package provides functions for classification with classifiers with contexts.
That is achieved by extending the functions of a State monad generated by the package
"StateMonadCodeGenerator.m", [1], with functions specific to classification work flow.
See [2] for explanations on monad code generation and extension.
Here is an example of a pipeline created with the functions in the package:
res =
ClCon[ds, <||>] ⟹
ClConSplitData[0.75] ⟹
ClConMakeClassifier["NearestNeighbors"] ⟹
ClConEchoFunctionContext[Information[#["classifier"]] &] ⟹
ClConClassifierMeasurements[{"Accuracy", "Precision", "Recall"}] ⟹
ClConEchoValue ⟹
(If[#1["Accuracy"] > 0.7, None, ClCon[#1, #2]] &) ⟹
ClConMakeClassifier["RandomForest"] ⟹
ClConEchoFunctionContext[Information[#["classifier"]] &] ⟹
ClConClassifierMeasurements[{"Accuracy", "Precision", "Recall"}] ⟹
ClConEchoValue;
## Contexts
The classifier contexts are Association objects. The pipeline values can have the form:
ClCon[ val, context:(_String|_Association) ]
see the explanations in [1] for more details.
Some of the specific functions set or retrieve values from contexts for the keys:
"trainingData", "testData", "classifier".
## Error messages
The error messages are print-outs with `Echo`. They can be easily changed to use `Message` instead.
(`Echo` is used since it fits the monadic pipeline "culture.")
## Examples
### Data
Assume we have the Titanic data as our working dataset:
dataName = "Titanic";
ds = Dataset[Flatten@*List @@@ ExampleData[{"MachineLearning", dataName}, "Data"]];
varNames = Flatten[List @@ ExampleData[{"MachineLearning", dataName}, "VariableDescriptions"]];
ds = ds[All, AssociationThread[varNames -> #] &];
### Complete usage example
TBD...
### Complete usage example with string contexts
TBD...
## References
[1] Anton Antonov, StateMonadCodeGenerator.m, 2017, MathematicaForPrediction at GitHub.
URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MonadicProgramming/StateMonadCodeGenerator.m
[2] Anton Antonov, "Monad code generation and extension", 2017, MathematicaForPrediction at GitHub.
URL: https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/Monad-code-generation-and-extension.md
## End matters
This file was created by Mathematica Plugin for IntelliJ IDEA.
Anton Antonov
Windermere, FL, USA
2017-06-05
*)
(*
TODO:
1. Add examples explained in detail.
2. CANCELED Make a true package.
3. DONE Add classifier ensemble handling.
4. Give examples of tracking symbols.
*)
(**************************************************************)
(* Importing packages (if needed) *)
(**************************************************************)
If[Length[DownValues[MathematicaForPredictionUtilities`RecordsSummary]] == 0,
Echo["MathematicaForPredictionUtilities.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MathematicaForPredictionUtilities.m"]
];
If[Length[DownValues[StateMonadCodeGenerator`GenerateStateMonadCode]] == 0,
Echo["StateMonadCodeGenerator.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/MonadicProgramming/StateMonadCodeGenerator.m"]
];
If[Length[DownValues[ClassifierEnsembles`EnsembleClassifierMeasurements]] == 0,
Echo["ClassifierEnsembles.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/ClassifierEnsembles.m"]
];
(* Loaded in ClassifierEnsembles.m . *)
(*If[Length[DownValues[ROCFunctions`ROCPlot]] == 0,
Echo["ROCFunctions.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/ROCFunctions.m"]
];*)
If[Length[DownValues[VariableImportanceByClassifiers`AccuracyByVariableShuffling]] == 0,
Echo["VariableImportanceByClassifiers.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/VariableImportanceByClassifiers.m"]
];
If[Length[DownValues[CrossTabulate`CrossTabulate]] == 0,
Echo["CrossTabulate.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/CrossTabulate.m"]
];
If[Length[DownValues[SSparseMatrix`SSparseMatrixToTriplets]] == 0,
Echo["SSparseMatrix.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/SSparseMatrix.m"]
];
If[Length[DownValues[OutlierIdentifiers`HampelIdentifierParameters]] == 0,
Echo["OutlierIdentifiers.m", "Importing from GitHub:"];
Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/OutlierIdentifiers.m"]
];
(**************************************************************)
(* Package definition *)
(**************************************************************)
BeginPackage["MonadicContextualClassification`"];
$ClConFailure::usage = "Failure symbol for the monad ClCon.";
ClConSplitData::usage = "ClConSplitData[fr_?NumberQ] splits the pipeline value into training and test parts. ";
ClConRecoverData::usage = "ClConRecoverData joins split data from context or the current pipeline value into the pipeline value. \
The Association values of \"trainingData\", \"testData\", \"validationData\" are combined/joined into one.";
ClConMakeClassifier::usage = "ClConMakeClassifier[methodSpec_?MethodSpecQ] makes a classifier with the specified method \
Using Association values of \"trainingData\", \"testData\", \"validationData\". \
The obtained classifier object is put as the result pipeline value; also in the context under the key \"classifier\". \
The Association values of \"trainingData\", \"testData\", \"validationData\" are put in the context too, if taken from \
the current pipeline value. ";
ClConTrainClassifier::usage = "Synonym of ClConMakeClassifier.";
ClConClassifierMeasurements::usage = "ClConClassifierMeasurements[measures : (_String | {_String ..})] \
computes the specified measurements for the classifier in the context. (Does not modify the context.)";
ClConClassifierMeasurementsByThreshold::usage = "ClConClassifierMeasurementsByThreshold[measures : (_String | {_String ..}), clLbl->th_?NumberQ] \
computes the specified measurements for the classifier in the context \
using the threshold th for the specified class label clLbl. \
(Does not modify the context.)";
ClConAccuracyByVariableShuffling::usage = "ClConAccuracyByVariableShuffling[opts : OptionsPattern[]] computes \
the variable importance. (Does not modify the context.)";
ClConSummarizeData::usage = "Summarizes the data in long form. Does not modify the context. \
Echoes the result with the default option values.";
ClConEchoDataSummary::usage = "Echoes results of data summarization.";
ClConSummarizeDataLongForm::usage = "Summarizes the data in long form. Does not modify the context. \
Does not echo the result.";
ClConToNormalClassifierData::usage = "Non-monadic function. Converts data of different forms into record-label rules. \
I.e. in the form { (rec:{___}->lbl_)..} .";
ClConSetData::usage = "Sets the data argument to be the pipeline value. (Synonym of ClConSetValue.)";
ClConSetTrainingData::usage = "Sets the training data in the context. Does not change the pipeline value.";
ClConSetTestData::usage = "Sets the test data in the context. Does not change the pipeline value.";
ClConSetValidationData::usage = "Sets the validation data in the context. Does not change the pipeline value.";
ClConSetClassifier::usage = "Sets the classifier in the context. Does not change the pipeline value.";
ClConSetVariableNames::usage = "Sets the variable names in the context. Does not change the pipeline value.";
ClConTakeTrainingData::usage = "Takes the training data in the context.";
ClConTakeTestData::usage = "Takes the test data in the context.";
ClConTakeValidationData::usage = "Takes the validation data in the context.";
ClConTakeData::usage = "Recovers the data and gives it as a non-monadic value.";
ClConTakeClassifier::usage = "Gives the classifier as non-monadic value.";
ClConTakeROCData::usage = "Gives the ROC data as non-monadic value.";
ClConTakeVariableNames::usage = "Finds the variable names and returns them as a non-monadic value.";
ClConGetVariableNames::usage = "Finds the variable names and puts them as the pipeline value. Does not modify the context.";
ClConEchoVariableNames::usage = "Finds and echoes the variable names. Does not modify the context.";
ClConROCData::usage = "Computes the ROC data using the classifier and test data in the context. \
The obtained ROC data is put as the result pipeline value; also in the context under the key \"rocData\".";
ClConROCPlot::usage = "Makes a ROC plot and echoes it. The result pipeline value is the plot.";
ClConROCListLinePlot::usage = "Makes ListLinePlot over specified ROC functions and echoes it. The result pipeline value is the plot.";
ClConSuggestROCThresholds::usage = "Suggest thresholds based on ROC data.";
ClConAssignVariableNames::usage = "Puts a value for \"variableNames\" in the context in correspondence to \"trainingData\" in the context. \
If an empty list is given the variable names are automatically derived.";
ClConOutlierPosition::usage = "Find outlier positions in the data.";
(*ClConFindOutliersPerClassLabel::usage = "Find outlier positions in the data per class label."*)
(*ClConDropOutliersPerClassLabel::usage = "Find and from outliers in the data per class label."*)
ClConReduceDimension::usage = "Applies dimension reduction with SVD. \
(If the non-label parts of the training data and test data can be converted numerical matrices.)";
(*ClConDeleteMissing::usage = "Deletes records with missing data values."*)
Begin["`Private`"];
Needs["MathematicaForPredictionUtilities`"];
Needs["StateMonadCodeGenerator`"];
Needs["ClassifierEnsembles`"];
Needs["ROCFunctions`"];
Needs["VariableImportanceByClassifiers`"];
Needs["CrossTabulate`"];
Needs["SSparseMatrix`"];
Needs["OutlierIdentifiers`"];
(* The definitions are made to have a prefix "ClCon" . *)
(**************************************************************)
(* Generation *)
(**************************************************************)
(* Generate base functions of ClCon monad (ClassifierWithContext) *)
GenerateStateMonadCode["MonadicContextualClassification`ClCon", "FailureSymbol" -> $ClConFailure];
(**************************************************************)
(* Infix operators *)
(**************************************************************)
(* This should be already done by GenerateStateMonadCode. *)
(*DoubleLongRightArrow[x_?ClConUnitQ, f_] := ClConBind[x, f];*)
(**************************************************************)
(* General functions *)
(**************************************************************)
Clear[ClConToNormalClassifierData];
Options[ClConToNormalClassifierData] = {"DeleteMissing"->True, "ClassLabelColumn" -> Automatic };
(* Here we use MathematicaForPredictionUtilities`DataArrayRulesForClassifyQ *)
ClConToNormalClassifierData[___] := $ClConFailure;
ClConToNormalClassifierData[ {}, opts:OptionsPattern[] ] := {};
ClConToNormalClassifierData[ data_?DataRulesForClassifyQ, opts:OptionsPattern[] ] :=
Block[{},
If[ TrueQ[ OptionValue[ClConToNormalClassifierData,"DeleteMissing"]],
DeleteMissing[data,1,2],
data
]
];
ClConToNormalClassifierData[ data_Association, opts:OptionsPattern[] ] :=
Block[{},
If[ TrueQ[ OptionValue[ClConToNormalClassifierData,"DeleteMissing"]],
DeleteMissing[data,2,2],
data
]
] /; MatchQ[ data, Association[(_?AtomQ -> _List) ..]];
ClConToNormalClassifierData[ td_Dataset, opts:OptionsPattern[] ] :=
ClConToNormalClassifierData[ Normal[td[All, Values]], opts];
ClConToNormalClassifierData[ data_?MatrixQ, opts:OptionsPattern[] ] :=
Block[{dmVal, labelCol, trainingRange, ncols},
ncols = Dimensions[data][[2]];
dmVal = TrueQ[ OptionValue[ClConToNormalClassifierData, "DeleteMissing"] ];
labelCol = OptionValue[ClConToNormalClassifierData,"ClassLabelColumn"];
If[ TrueQ[labelCol === Automatic], labelCol = ncols ];
trainingRange = Complement[Range[ncols], {labelCol}];
If[ dmVal,
Thread[#[[All, trainingRange]] -> #[[All, labelCol]]] &@ DeleteMissing[data, 1, 2],
(* ELSE *)
Thread[#[[All, trainingRange]] -> #[[All, labelCol]]] &@ data
]
];
(**************************************************************)
(* Data splitting and recovery functions *)
(**************************************************************)
Clear[ClConSplitData];
SyntaxInformation[ClConSplitData] = { "ArgumentsPattern" -> { _., _., OptionsPattern[] } };
(* This function does not respect specified label column yet. *)
Options[ClConSplitData] = {
"TrainingFraction" -> 0.75,
"ValidationFraction" -> 0,
Method->"LabelsProportional",
"ClassLabelColumn" -> Automatic};
ClConSplitData[___][$ClConFailure] := $ClConFailure;
ClConSplitData[opts:OptionsPattern[]][xs_, context_Association] :=
Block[{fr, valFr},
fr = OptionValue[ClConSplitData, "TrainingFraction"];
If[ TrueQ[fr === Automatic], fr = 0.75];
If[ !( NumberQ[fr] && 0 <= fr <= 1 ),
Echo[
"The value of the option \"TrainingFraction\" is expected to be Automatic or a number between 0 and 1.",
"ClConSplitData:"
];
Return[$ClConFailure]
];
valFr = OptionValue[ClConSplitData, "ValidationFraction"];
If[ TrueQ[valFr === Automatic], valFr = 0];
If[ !( NumberQ[valFr] && 0 <= valFr <= 1 ),
Echo[
"The value of the option \"ValidationFraction\" is expected to be Automatic or a number between 0 and 1.",
"ClConSplitData:"
];
Return[$ClConFailure]
];
ClConSplitData[fr, valFr, opts][xs, context]
];
ClConSplitData[fr_?NumberQ, opts:OptionsPattern[]][xs_, context_Association] :=
ClConSplitData[fr, 0, opts][xs, context];
ClConSplitData[fr_?NumberQ, valFr_?NumberQ, opts:OptionsPattern[]][xs_, context_Association] :=
Block[{method, labelCol, dataLabels, indGroups, t,
trainingData, testData, validationData},
method = OptionValue[ClConSplitData, Method];
labelCol = OptionValue[ClConSplitData,"ClassLabelColumn"];
If[ ! ( TrueQ[Head[xs] === Dataset] || DataRulesForClassifyQ[xs] || MatrixQ[xs] ),
Echo["No data to split or unknown data form.", "ClConSplitData:" ];
Return[$ClConFailure];
];
Which[
method == "LabelsProportional",
dataLabels =
Transpose[{Range[Length[xs]], Normal[xs[[All, -1]]]}];
indGroups = Map[#[[All, 1]] &, GroupBy[dataLabels, Last]];
t = TakeDrop[RandomSample[#], Floor[fr*Length[#]]] & /@ indGroups;
trainingData = xs[[ Join @@ t[[All, 1]], All]];
testData = xs[[ Join @@ t[[All, 2]], All]],
True,
{trainingData, testData} = TakeDrop[RandomSample[xs], Floor[fr*Length[xs]]];
];
If[ TrueQ[valFr == 0],
t = AssociationThread[{"trainingData", "testData"} -> {trainingData, testData}],
(*ELSE*)
t = Fold[ ClConBind, ClConUnit[trainingData, <||>], { ClConSplitData[1-valFr, opts], ClConTakeValue }];
{trainingData, validationData} = { t["trainingData"], t["testData"]};
t = AssociationThread[{"trainingData", "testData", "validationData"} -> {trainingData, testData, validationData}]
];
ClConUnit[ t, Join[context, t] ]
] /; 0 < fr <= 1 && 0 <= valFr < 1;
ClConSplitData[___][xs_, context_Association] :=
Block[{},
Echo[ "One or two arguments are expected, both numbers between 0 and 1.", "ClConSplitData:" ];
$ClConFailure
];
Clear[ClConRecoverData];
ClConRecoverData[$ClConFailure] := $ClConFailure;
ClConRecoverData[][xs_, context_Association] := ClConRecoverData[xs, context];
ClConRecoverData[xs_, context_Association] :=
Block[{},
Which[
MatchQ[xs, _Association] && KeyExistsQ[xs, "trainingData"] && KeyExistsQ[xs, "testData"] && KeyExistsQ[xs, "validationData"],
ClCon[Join[xs["trainingData"], xs["testData"], xs["validationData"]], context],
KeyExistsQ[context, "trainingData"] && KeyExistsQ[context, "testData"] && KeyExistsQ[context, "validationData"],
ClCon[Join[context["trainingData"], context["testData"], context["validationData"]], context],
MatchQ[xs, _Association] && KeyExistsQ[xs, "trainingData"] && KeyExistsQ[xs, "testData"],
ClCon[Join[xs["trainingData"], xs["testData"]], context],
KeyExistsQ[context, "trainingData"] && KeyExistsQ[context, "testData"],
ClCon[Join[context["trainingData"], context["testData"]], context],
True,
Echo["Cannot recover data.","ClConRecoverData:"];
$ClConFailure
]
];
(**************************************************************)
(* Setters / getters *)
(**************************************************************)
Clear["ClConSet*Data", "ClConTake*Data", ClConTakeClassifier, ClConTakeClassLabelIndex, ClConTakeVariableNames];
Clear[ClConSetData];
ClConSetData = ClConSetValue;
ClConSetTrainingData[___][$ClConFailure] := $ClConFailure;
ClConSetTrainingData[xs_, context_Association] := $ClConFailure;
ClConSetTrainingData[data_][xs_, context_Association] :=
ClConUnit[xs, Join[ context, <| "trainingData" -> data |> ] ];
ClConSetTestData[___][$ClConFailure] := $ClConFailure;
ClConSetTestData[xs_, context_Association] := $ClConFailure;
ClConSetTestData[data_][xs_, context_Association] :=
ClConUnit[xs, Join[ context, <| "testData" -> data |> ] ];
ClConSetValidationData[___][$ClConFailure] := $ClConFailure;
ClConSetValidationData[xs_, context_Association] := $ClConFailure;
ClConSetValidationData[data_][xs_, context_Association] :=
ClConUnit[xs, Join[ context, <| "validationData" -> data |> ] ];
ClConSetClassifier[___][$ClConFailure] := $ClConFailure;
ClConSetClassifier[xs_, context_Association] := $ClConFailure;
ClConSetClassifier[cl_][xs_, context_Association] :=
ClConUnit[xs, Join[ context, <| "classifier" -> cl |> ] ];
ClConSetVariableNames[___][$ClConFailure] := $ClConFailure;
ClConSetVariableNames[xs_, context_Association] := $ClConFailure;
ClConSetVariableNames[nms_][xs_, context_Association] :=
ClConUnit[xs, Join[ context, <| "variableNames" -> nms |> ] ];
ClConTakeTrainingData[___][$ClConFailure] := $ClConFailure;
ClConTakeTrainingData[$ClConFailure] := $ClConFailure;
ClConTakeTrainingData[][xs_, context_] := ClConTakeTrainingData[xs, context];
ClConTakeTrainingData[xs_, context_Association] := context["trainingData"];
ClConTakeTestData[___][$ClConFailure] := $ClConFailure;
ClConTakeTestData[$ClConFailure] := $ClConFailure;
ClConTakeTestData[][xs_, context_] := ClConTakeTestData[xs, context];
ClConTakeTestData[xs_, context_Association] := context["testData"];
ClConTakeValidationData[___][$ClConFailure] := $ClConFailure;
ClConTakeValidationData[$ClConFailure] := $ClConFailure;
ClConTakeValidationData[][xs_, context_] := ClConTakeValidationData[xs, context];
ClConTakeValidationData[xs_, context_Association] := context["validationData"];
ClConTakeData[___][$ClConFailure] := $ClConFailure;
ClConTakeData[$ClConFailure] := $ClConFailure;
ClConTakeData[][xs_, context_] := ClConTakeData[xs, context];
ClConTakeData[xs_, context_] :=
Fold[ ClConBind, ClConUnit[xs, context], {ClConRecoverData, ClConTakeValue}];
ClConTakeClassifier[___][$ClConFailure] := $ClConFailure;
ClConTakeClassifier[$ClConFailure] := $ClConFailure;
ClConTakeClassifier[][xs_, context_] := ClConTakeClassifier[xs, context];
ClConTakeClassifier[xs_, context_Association] := context["classifier"];
ClConTakeROCData[___][$ClConFailure] := $ClConFailure;
ClConTakeROCData[$ClConFailure] := $ClConFailure;
ClConTakeROCData[][xs_, context_] := ClConTakeROCData[xs, context];
ClConTakeROCData[xs_, context_Association] := context["rocData"];
ClConTakeClassLabelIndex[___][$ClConFailure] := $ClConFailure;
ClConTakeClassLabelIndex[][xs_, context_Association] := ClConTakeClassLabelIndex[Automatic][xs, context];
ClConTakeClassLabelIndex[classLabel_][xs_, context_Association] :=
Block[{varNames},
varNames = ClConBind[ ClConUnit[xs, context], ClConTakeVariableNames ];
(* Not a good idea this to be here *)
(*If[ TrueQ[varNames === $ClConFailure],*)
(*Echo["Proceeding with automatic variable names.", "ClConTakeClassLabelIndex:"];*)
(*varNames = ClConBind[ ClConUnit[xs, context], { ClConAssignVariableNames, ClConTakeVariableNames} ];*)
(*];*)
Which[
TrueQ[varNames === $ClConFailure],
$ClConFailure,
TrueQ[classLabel===Automatic] && KeyExistsQ[context, "classLabel"] && MemberQ[varNames, context["classLabel"]],
<| context["classLabel"] -> First@Flatten@Position[varNames, context["classLabel"]] |>,
TrueQ[classLabel===Automatic] && KeyExistsQ[context, "classLabel"] && !MemberQ[varNames, context["classLabel"]],
Echo["The context value for \"classLabel\" is one of " <> ToString[varNames], "ClConTakeClassLabelIndex::"];
$ClConFailure,
TrueQ[classLabel===Automatic],
<| varNames[[-1]] -> Length[varNames] |>,
MemberQ[varNames, classLabel],
<| classLabel -> First@Flatten@Position[varNames, classLabel] |>,
True,
Echo["The specified class label "<>ToString[classLabel]<>" is one of " <> ToString[varNames], "ClConTakeClassLabelIndex::"];
$ClConFailure
]
];
ClConTakeVariableNames[][$ClConFailure] := $ClConFailure;
ClConTakeVariableNames[$ClConFailure] := $ClConFailure;
ClConTakeVariableNames[][xs_, context_] := ClConTakeVariableNames[xs, context];
ClConTakeVariableNames[xs_, context_Association] :=
Fold[ClConBind, ClConUnit[xs, context], {ClConGetVariableNames, ClConTakeValue}];
(**************************************************************)
(* Dealing with variable names *)
(**************************************************************)
Clear[DatasetWithColumnNamesQ];
DatasetWithColumnNamesQ[ds_Dataset] := AssociationQ @ Normal @ ds[1];
DatasetWithColumnNamesQ[___] := False;
Clear[ClConGetVariableNames];
ClConGetVariableNames[][$ClConFailure] := $ClConFailure;
ClConGetVariableNames[$ClConFailure] := $ClConFailure;
ClConGetVariableNames[][xs_, context_] := ClConGetVariableNames[xs, context];
ClConGetVariableNames[xs_, context_Association] :=
Block[{},
Which[
DatasetWithColumnNamesQ[xs],
ClCon[Normal[xs[1,Keys]], context],
MatchQ[xs, _Association] && KeyExistsQ[xs, "trainingData"] && DatasetWithColumnNamesQ[xs["trainingData"]],
ClCon[Normal[xs["trainingData"][1,Keys]], context],
KeyExistsQ[context, "trainingData"] && KeyExistsQ[context, "testData"] && DatasetWithColumnNamesQ[context["trainingData"]],
ClCon[Normal[context["trainingData"][1,Keys]], context],
KeyExistsQ[context, "variableNames"],
ClCon[context["variableNames"], context],
True,
Echo["Cannot find the variable names: (1) there is no context key \"variableNames\", " <>
"(2) the pipeline value is not a Dataset with named columns, and " <>
"(3) there is no \"trainingData\" key in the context or the corresponding value is not a Dataset with named columns.",
"ClConGetVariableNames:"];
$ClConFailure
]
];
Clear[ClConEchoVariableNames];
ClConEchoVariableNames[$ClConFailure] := $ClConFailure;
ClConEchoVariableNames[][$ClConFailure] := $ClConFailure;
ClConEchoVariableNames[][xs_, context_] := ClConEchoVariableNames[xs, context];
ClConEchoVariableNames[xs_, context_Association] :=
Block[{t},
t = Fold[ ClConBind, ClConUnit[xs,context], {ClConGetVariableNames, ClConTakeValue}];
If[ TrueQ[ t === $ClConFailure ],
$ClConFailure,
Echo[t,"variable names:"];
ClConUnit[xs, context]
]
];
Clear[ClConAssignVariableNames];
ClConAssignVariableNames[$ClConFailure] := $ClConFailure;
ClConAssignVariableNames[][$ClConFailure] := $ClConFailure;
ClConAssignVariableNames[xs_, context_Association] := ClConAssignVariableNames[{}][xs, context];
ClConAssignVariableNames[][xs_, context_] := ClConAssignVariableNames[{}][xs, context];
ClConAssignVariableNames[Automatic][xs_, context_] := ClConAssignVariableNames[{}][xs, context];
ClConAssignVariableNames[varNamesArg:{_String...}][xs_, context_Association] :=
Block[{varNames = varNamesArg, ncols, dsQ, mlrQ, arrQ},
If[ KeyExistsQ[context, "trainingData"],
dsQ = TrueQ[ Head[context["trainingData"]] === Dataset ];
mlrQ = DataArrayRulesForClassifyQ[ context["trainingData"] ];
arrQ = MatrixQ[context["trainingData"]];
ncols =
Which[
dsQ || arrQ, Dimensions[context["trainingData"]][[2]],
mlrQ, Dimensions[context["trainingData"][[All,1]]][[2]] + 1,
True, Missing["NA"]
];
Which[
NumberQ[ncols] && (dsQ || mlrQ || arrQ) && Length[varNames] < ncols,
varNames = Join[ varNames, Map[ ToString, Table[ i, {i, Length[varNames]+1, ncols}] ] ],
NumberQ[ncols] && (dsQ || mlrQ || arrQ) && Length[varNames] >= ncols,
varNames = Take[varNames, ncols],
True,
Echo["Unknown training data type or the specified variable names do not correspond to the \"trainingData\" dimensions.", "ClConAssignVariableNames:"];
Return[$ClConFailure]
],
(*ELSE*)
Echo["No training data in the context.", "ClConAssignVariableNames:"];
Return[$ClConFailure]
];
ClConUnit[xs, Join[context, <|"variableNames"->varNames|>] ]
];
ClConAssignVariableNames[___][xs_, context_Association] :=
Block[{},
Echo["The first argument is expected to be a list of strings or Automatic.", "ClConAssignVariableNames:"];
$ClConFailure
];
(************************************************************)
(* GetData *)
(************************************************************)
Clear[GetData];
GetData[xs_, context_] :=
Block[{},
Which[
Head[xs] === Dataset || DataRulesForClassifyQ[xs] || MatrixQ[xs],
ctData = <|Anonymous->xs|>,
MatchQ[xs, _Association] && ( KeyExistsQ[xs, "trainingData"] || KeyExistsQ[xs, "testData"] || KeyExistsQ[xs, "validationData"] ),
ctData = KeyTake[xs, {"trainingData", "testData", "validationData"}],
KeyExistsQ[context, "trainingData"] || KeyExistsQ[context, "testData"] || KeyExistsQ[context, "validationData"],
ctData = KeyTake[context, {"trainingData", "testData", "validationData"}],
True,
ctData = {}
]
];
(************************************************************)
(* ClConSummarizeData *)
(************************************************************)
Clear[ClConSummarizeData];
SyntaxInformation[ClConSummarizeData] = { "ArgumentsPattern" -> { OptionsPattern[] } };
Options[ClConSummarizeData] = Join[ {"Type" -> Automatic, "Echo"->True }, Options[DataColumnsSummary]];
ClConSummarizeData[$ClConFailure] := $ClConFailure;
ClConSummarizeData[___][$ClConFailure] := $ClConFailure;
ClConSummarizeData[xs_, context_Association] := ClConSummarizeData[][xs,context];
ClConSummarizeData[opts:OptionsPattern[]][xs_, context_] :=
Block[{ctData=GetData[xs,context], res, rsOpts, dims, type},
type = OptionValue[ClConSummarizeData, "Type"];
rsOpts = DeleteCases[{opts},("Type"->_)|("Echo"->_)];
If[ Length[ctData] == 0 || And @@ Map[ Length[#]==0 &, ctData],
Echo["Cannot find data in the context.", "ClConSummarizeData:"];
Return[$ClConFailure]
];
If[ DataRulesForClassifyQ[ctData[[1]]] && ! DataArrayRulesForClassifyQ[ctData[[1]]],
(* I.e {(_?AtomQ->_lbl)..} *)
type = "WideForm"; dims = {Length[ctData[[1]]], 1};
];
dims =
If[DataArrayRulesForClassifyQ[ctData[[1]]],
Dimensions[ ctData[[1, All, 1]] ],
Dimensions[ ctData[[1]] ]
];
If[ TrueQ[ type === Automatic] && Length[dims] > 1 && dims[[2]] < 9 ||
TrueQ[ type != "LongForm"],
(*ctData = ClConToNormalClassifierData /@ ctData;*)
(*res = RecordsSummary[#, Thread->True, DeleteCases[{opts},"Type"->_]]& /@ ctData;*)
res = RecordsSummary[#, Sequence @@ Append[rsOpts, Thread->True]]& /@ ctData;
If[ TrueQ[OptionValue["Echo"]],
ClConBind[ ClConUnit[ Normal@res, context], ClConEchoFunctionValue["summaries:", Identity] ],
ClConUnit[ Normal@res, context]
],
(* ELSE *)
ClConSummarizeDataLongForm[rsOpts][xs, context]
]
];
ClConSummarizeData[___][__] := $ClConFailure;
(************************************************************)
(* ClConSummarizeDataLongForm *)
(************************************************************)
Clear[ClConSummarizeDataLongForm];
SyntaxInformation[ClConSummarizeDataLongForm] = { "ArgumentsPattern" -> { OptionsPattern[] } };
Options[ClConSummarizeDataLongForm] = Join[ {"Echo"->True}, Options[DataColumnsSummary]];
ClConSummarizeDataLongForm[$ClConFailure] := $ClConFailure;
ClConSummarizeDataLongForm[___][$ClConFailure] := $ClConFailure;
ClConSummarizeDataLongForm[xs_, context_Association] := ClConSummarizeDataLongForm[][xs,context];
ClConSummarizeDataLongForm[opts:OptionsPattern[]][xs_, context_] :=
Block[{rsOpts, varNames, ctData, data, sMat, dataLongForm, res},
rsOpts = DeleteCases[{opts},("Type"->_)|("Echo"->_)];
ctData = GetData[xs, context];
If[ Length[ctData] == 0 || And @@ Map[ Length[#]==0 &, ctData],
Echo["Cannot find data in the context.", "ClConSummarizeDataLongForm:"];
Return[$ClConFailure]
];
varNames = ClConBind[ ClConUnit[xs,context], ClConTakeVariableNames ];
If[ TrueQ[varNames === $ClConFailure],
If[ Keys[ctData] === {Anonymous},
res = <| "trainingData" -> First @ Values @Map[Take[#,UpTo[3]]&, ctData] |>,
res = ctData
];
varNames = Fold[ ClConBind, ClConUnit[{}, res], { ClConAssignVariableNames, ClConTakeVariableNames} ]
];
If[ TrueQ[varNames === $ClConFailure], Return[$ClConFailure] ];
ctData = ClConToNormalClassifierData /@ ctData;
res =
Function[{data},
If[Length[data] == 0, {},
sMat = ToSSparseMatrix[SparseArray@(Flatten@*List @@@ data),
"ColumnNames" -> varNames,
"RowNames" -> ToString /@ Range[Length[data]]];
dataLongForm = SSparseMatrixToTriplets[sMat];
RecordsSummary[dataLongForm, {"RowID", "Variable", "Value"}, rsOpts]
]
] /@ ctData;
If[ TrueQ[OptionValue["Echo"]],
ClConBind[ ClConUnit[ Normal@res, context], ClConEchoFunctionValue["summaries:", Identity] ],
ClConUnit[ Normal@res, context]
]
];
ClConSummarizeDataLongForm[___][__] := $ClConFailure;
(************************************************************)
(* ClConEchoDataSummary *)
(************************************************************)
Clear[ClConEchoDataSummary];
SyntaxInformation[ClConEchoDataSummary] = { "ArgumentsPattern" -> { OptionsPattern[] } };
Options[ClConEchoDataSummary] = Options[ClConSummarizeData];
ClConEchoDataSummary[$ClConFailure] := $ClConFailure;
ClConEchoDataSummary[___][$ClConFailure] := $ClConFailure;
ClConEchoDataSummary[xs_, context_Association] := ClConEchoDataSummary[][xs,context];
ClConEchoDataSummary[opts:OptionsPattern[]][xs_, context_] :=
Block[{res},
res = Fold[ ClConBind, ClConUnit[xs, context], {ClConSummarizeData}];
ClConUnit[xs, context]
];
ClConEchoDataSummary[___][__] := $ClConFailure;
(**************************************************************)
(* DeleteMissing *)
(**************************************************************)
Clear[ClConDeleteMissing];
SyntaxInformation[ClConDeleteMissing] = { "ArgumentsPattern" -> { } };
ClConDeleteMissing[$ClConFailure] := $ClConFailure;
ClConDeleteMissing[xs_, context_] := ClConDeleteMissing[][xs, context];
ClConDeleteMissing[][xs_, context_] :=
Block[{data},
data = ClConBind[ ClConGetData[xs, context], ClConTakeValue];
If[ data === $ClConFailure,
$ClConFailure,
(*ELSE*)
data = DeleteMissing[data, 1, 2];
ClConUnit[ data, Join[ context, <|"data"->data|>] ]
]
];
ClConDeleteMissing[___][__] := $ClConFailure;
(************************************************************)
(* ClConMakeClassifier *)
(************************************************************)
Clear[ClConMethodQ, ClConMethodListQ, ClConResamplingMethodListQ, ClConMethodSpecQ, ClConClassifierQ];
ClConMethodQ[x_] := StringQ[x] || MatchQ[ x, {_String, _Rule..} ]; (* And check is it known by Classify. *)
ClConMethodListQ[x_] := MatchQ[ x, {_?ClConMethodQ..} ];
ClConResamplingMethodQ[x_] := MatchQ[ x, (Automatic | _Association | _String | {_String, _?NumberQ} | {_String, _?NumberQ, _Integer} | {_String, _?NumberQ, _Integer, RandomSample | RandomChoice}) ];
(* Note that is includes MethodListQ. *)
ClConResamplingMethodListQ[x_] := MatchQ[ x, { _?ClConResamplingMethodQ .. } ];
ClConMethodSpecQ[x_] := ClConMethodQ[x] || ClConResamplingMethodQ[x] || ClConResamplingMethodListQ[x];
Clear[ClConMakeClassifier];
SyntaxInformation[ClConMakeClassifier] = { "ArgumentsPattern" -> { _., OptionsPattern[] } };
ClConClassifierQ[ cl_ ] :=
MatchQ[ cl, _ClassifierFunction] ||
If[Length[DownValues[ClassifierEnsembles`EnsembleClassifierMeasurements]] > 0,
MatchQ[ cl, Association[(_ -> _ClassifierFunction) ..] ]
];
Options[ClConMakeClassifier] = Options[Classify];
ClConMakeClassifier[___][$ClConFailure] := $ClConFailure;
ClConMakeClassifier[xs_, context_Association] := ClConMakeClassifier[][xs, context];
ClConMakeClassifier[][xs_, context_] := ClConMakeClassifier["LogisticRegression"][xs, context];
ClConMakeClassifier[opts:OptionsPattern[]][xs_, context_] := ClConMakeClassifier[Automatic,opts][xs, context];
ClConMakeClassifier[methodSpecArg_?ClConMethodSpecQ, opts:OptionsPattern[]][xs_, context_] :=
Block[{cf, dataAssoc, newContext, methodSpec = methodSpecArg},
Which[
MatchQ[xs, _Association] && KeyExistsQ[xs, "trainingData"],
dataAssoc = xs; newContext = Join[context, xs],
KeyExistsQ[context, "trainingData"],
dataAssoc = context; newContext = <||>,
True,
Echo["No training data. (No changes in argument and context were made.)", "ClConMakeClassifier:"];
Return[ClCon[xs, context]]
];
(* Note that if opts has Method->_ then this setting is ignored. *)
If[ TrueQ[methodSpec===Automatic], methodSpec = "LogisticRegression" ];
Which[
ClConMethodQ[methodSpec] && ( !KeyExistsQ[dataAssoc, "validationData"] || TrueQ[dataAssoc["validationData"] === Automatic] ),
cf = Classify[ClConToNormalClassifierData[dataAssoc@"trainingData"], opts, Method -> methodSpec ],
ClConMethodQ[methodSpec] && KeyExistsQ[dataAssoc, "validationData"],
cf = Classify[ClConToNormalClassifierData[dataAssoc@"trainingData"], opts, Method -> methodSpec,
ValidationSet -> ClConToNormalClassifierData[dataAssoc@"validationData"] ],
ClConMethodListQ[methodSpec],
cf = EnsembleClassifier[ methodSpec, ClConToNormalClassifierData[dataAssoc@"trainingData"], opts ],
ClConResamplingMethodQ[methodSpec],
cf = ResamplingEnsembleClassifier[ {methodSpec}, ClConToNormalClassifierData[dataAssoc@"trainingData"], opts ],
ClConResamplingMethodListQ[methodSpec],
cf = ResamplingEnsembleClassifier[ methodSpec, ClConToNormalClassifierData[dataAssoc@"trainingData"], opts ],
True,
Echo["Unknown classifier specification.", "ClConMakeClassifier:"];
cf = None;
];
If[ ! ClConClassifierQ[cf],
Echo["Classifier making failure.", "ClConMakeClassifier:"];
$ClConFailure,
(* ELSE *)
ClCon[cf, KeyDrop[ Join[context, newContext, <|"classifier" -> cf|>], "rocData"] ]
]
];
ClConMakeClassifier[___][xs_, context_Association] :=
Block[{},
Echo[ "The first argument is expected to be a classifier ensemble specification.",
"ClConMakeClassifier:"
];
$ClConFailure
];
Clear[ClConTrainClassifier];
ClConTrainClassifier = ClConMakeClassifier;
(************************************************************)
(* ClConClassifierMeasurements *)
(************************************************************)
Clear[ClConClassifierMeasurements];
SyntaxInformation[ClConClassifierMeasurements] = { "ArgumentsPattern" -> { _., OptionsPattern[] } };
Options[ClConClassifierMeasurements] = { Method -> Automatic, "ROCRange" -> Range[0,1,0.025]};
ClConClassifierMeasurements[xs_, context_Association] := ClConClassifierMeasurements[{"Accuracy", "Precision", "Recall"}][xs,context];
ClConClassifierMeasurements[][xs_, context_Association] := ClConClassifierMeasurements[{"Accuracy", "Precision", "Recall"}][xs,context];
ClConClassifierMeasurements[___][$ClConFailure] := $ClConFailure;
ClConClassifierMeasurements[measuresArg : (_String | {_String ..}), opts:OptionsPattern[]][xs_, context_] :=