diff --git a/src/main/java/org/jpmml/translator/regression/RegressionModelTranslator.java b/src/main/java/org/jpmml/translator/regression/RegressionModelTranslator.java index 5e64519..c4cd40f 100644 --- a/src/main/java/org/jpmml/translator/regression/RegressionModelTranslator.java +++ b/src/main/java/org/jpmml/translator/regression/RegressionModelTranslator.java @@ -18,6 +18,7 @@ */ package org.jpmml.translator.regression; +import java.util.ArrayList; import java.util.Arrays; import java.util.Collection; import java.util.Collections; @@ -462,15 +463,31 @@ public Number apply(FunctionInvocationPredictor tfTerm){ JVar coefficientVar = context.declare(context.ref(Number.class), "coefficient", coefficientsVar.invoke("get").arg(indexVar)); + List factorVars = new ArrayList<>(); + factorVars.add(coefficientVar); + if(weightsVar != null){ JVar weightVar = context.declare(context.ref(Number.class), "weight", weightsVar.invoke("get").arg(indexVar)); - valueBuilder.update("add", coefficientVar, weightVar, frequencyVar); - } else + factorVars.add(weightVar); + } - { - valueBuilder.update("add", coefficientVar, frequencyVar); + TextIndex.LocalTermWeights localTermWeights = textIndex.getLocalTermWeights(); + switch(localTermWeights){ + case BINARY: + break; + case TERM_FREQUENCY: + factorVars.add(frequencyVar); + break; + case LOGARITHMIC: + JVar logFrequencyVar = context.declare(context.ref(Double.class), "logFrequency", context.staticInvoke(Math.class, "log10", JExpr.lit(1).plus(frequencyVar))); + factorVars.add(logFrequencyVar); + break; + default: + throw new UnsupportedAttributeException(localTextIndex, localTermWeights); } + + valueBuilder.update("add", factorVars.toArray(new JVar[factorVars.size()])); } finally { context.popScope(); } diff --git a/src/test/java/org/jpmml/transpiler/Algorithms.java b/src/test/java/org/jpmml/transpiler/Algorithms.java index 61764ba..4014826 100644 --- a/src/test/java/org/jpmml/transpiler/Algorithms.java +++ b/src/test/java/org/jpmml/transpiler/Algorithms.java @@ -25,6 +25,7 @@ public interface Algorithms { String GRADIENT_BOOSTING = "GradientBoosting"; String ISOLATION_FOREST = "IsolationForest"; String LIGHT_GBM = "LightGBM"; + String LINEAR_DISCRIMINANT_ANALYSIS = "LinearDiscriminantAnalysis"; String LINEAR_REGRESSION = "LinearRegression"; String LINEAR_SVC = "LinearSVC"; String LOGISTIC_REGRESSION = "LogisticRegression"; diff --git a/src/test/java/org/jpmml/transpiler/ClassificationTest.java b/src/test/java/org/jpmml/transpiler/ClassificationTest.java index 7258f94..efb53cd 100644 --- a/src/test/java/org/jpmml/transpiler/ClassificationTest.java +++ b/src/test/java/org/jpmml/transpiler/ClassificationTest.java @@ -68,6 +68,11 @@ public void evaluateXGBoostAuditNA() throws Exception { evaluate(XGBOOST, AUDIT_NA, excludeFields(AUDIT_PROBABILITY_FALSE), new FloatEquivalence(8)); } + @Test + public void evaluateLinearDiscriminantAnalysisSentiment() throws Exception { + evaluate(LINEAR_DISCRIMINANT_ANALYSIS, SENTIMENT); + } + @Test public void evaluateLinearSVCSentiment() throws Exception { evaluate(LINEAR_SVC, SENTIMENT); diff --git a/src/test/resources/csv/LinearDiscriminantAnalysisSentiment.csv b/src/test/resources/csv/LinearDiscriminantAnalysisSentiment.csv new file mode 100644 index 0000000..418cae9 --- /dev/null +++ b/src/test/resources/csv/LinearDiscriminantAnalysisSentiment.csv @@ -0,0 +1,1001 @@ +Score,probability(0),probability(1) +0,0.9995152145117355,0.0004847854882644347 +1,1.47597115085496e-06,0.9999985240288491 +1,0.00020972525518370144,0.9997902747448163 +0,0.9998801754618063,0.00011982453819373994 +1,0.30019300414906525,0.6998069958509348 +0,0.9999821061698884,1.7893830111623567e-05 +0,0.9996066750734541,0.0003933249265458369 +1,0.009709434046696552,0.9902905659533034 +0,0.9998669901438361,0.00013300985616388923 +0,0.9997096673552504,0.0002903326447496108 +1,1.3236612205758114e-05,0.9999867633877942 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+0,0.9836091465453559,0.016390853454644087 +0,0.5989813330454639,0.40101866695453614 +0,0.9189242649994533,0.0810757350005466 +0,0.9999734447326646,2.6555267335385663e-05 +0,0.9955942225236345,0.004405777476365521 +0,0.9611866642112646,0.03881333578873535 +0,0.9905588706774894,0.009441129322510658 diff --git a/src/test/resources/main.py b/src/test/resources/main.py index cae0670..30fe641 100644 --- a/src/test/resources/main.py +++ b/src/test/resources/main.py @@ -1,6 +1,7 @@ from lightgbm import LGBMClassifier, LGBMRegressor from mlxtend.preprocessing import DenseTransformer from pandas import DataFrame +from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.ensemble import AdaBoostRegressor, GradientBoostingClassifier, GradientBoostingRegressor, IsolationForest, RandomForestClassifier, RandomForestRegressor, VotingRegressor from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.feature_selection import f_classif @@ -156,8 +157,9 @@ def build_sentiment(classifier, transformer, name, with_proba = True, **pmml_opt if "Sentiment" in datasets: pmml_textindex_args = dict(analyzer = "word", preprocessor = None, strip_accents = None, dtype = numpy.float64) + build_sentiment(LinearDiscriminantAnalysis(), TfidfVectorizer(tokenizer = Splitter(), ngram_range = (1, 3), norm = None, **pmml_textindex_args), "LinearDiscriminantAnalysisSentiment") build_sentiment(LinearSVC(random_state = 13), CountVectorizer(tokenizer = Splitter(), ngram_range = (1, 2), **pmml_textindex_args), "LinearSVCSentiment", with_proba = False) - build_sentiment(LogisticRegression(multi_class = "ovr"), TfidfVectorizer(stop_words = "english", tokenizer = Matcher(), ngram_range = (1, 3), norm = None, **pmml_textindex_args), "LogisticRegressionSentiment") + build_sentiment(LogisticRegression(multi_class = "ovr"), TfidfVectorizer(stop_words = "english", tokenizer = Matcher(), ngram_range = (1, 3), binary = True, norm = None, **pmml_textindex_args), "LogisticRegressionSentiment") build_sentiment(RandomForestClassifier(max_depth = 8, min_samples_leaf = 10, n_estimators = 31, random_state = 13), CountVectorizer(ngram_range = (1, 2), **pmml_textindex_args), "RandomForestSentiment") build_sentiment(XGBClassifier(objective = "binary:logistic", ntree_limit = 31, random_state = 13), CountVectorizer(tokenizer = Matcher(), **pmml_textindex_args), "XGBoostSentiment") diff --git a/src/test/resources/pmml/LinearDiscriminantAnalysisSentiment.pmml b/src/test/resources/pmml/LinearDiscriminantAnalysisSentiment.pmml new file mode 100644 index 0000000..8da78c0 --- /dev/null +++ b/src/test/resources/pmml/LinearDiscriminantAnalysisSentiment.pmml @@ -0,0 +1,4047 @@ + + +
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