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Bump catboost-prediction version to 1.1 #12

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137 changes: 137 additions & 0 deletions apply_model/java/.gitignore
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@@ -0,0 +1,137 @@
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11 changes: 10 additions & 1 deletion apply_model/java/pom.xml
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Expand Up @@ -12,7 +12,16 @@
<dependency>
<groupId>ai.catboost</groupId>
<artifactId>catboost-prediction</artifactId>
<version>0.2.4</version>
<version>1.1</version>
</dependency>
<dependency>
<groupId>ai.catboost</groupId>
<artifactId>catboost-common</artifactId>
<version>1.1</version>
</dependency>
</dependencies>
<properties>
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
</properties>
</project>
28 changes: 15 additions & 13 deletions apply_model/java/src/main/java/CatBoost4jPredictionTutorial.java
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import java.io.IOException;

import static java.util.Objects.requireNonNull;

public class CatBoost4jPredictionTutorial {
private CatBoostModel adultModel = null;

public CatBoost4jPredictionTutorial() throws CatBoostError, IOException {
// Load "adult.cbm" model that we trained withing Jupyter Notebook
adultModel = CatBoostModel.loadModel(ClassLoader.getSystemResourceAsStream("models/adult.cbm"));
adultModel = CatBoostModel.loadModel(requireNonNull(ClassLoader.getSystemResourceAsStream("models/adult.cbm")));

// You can also try to load your own model just comment out the line above and uncomment two lines below while
// replacing "foo/bar" with path to your model that classifies data from UCI Adult Dataset.
Expand All @@ -35,7 +37,7 @@ public void playWithModelForAdultDataset() throws CatBoostError {

System.out.print("Adult dataset model metainformation\n");

System.out.print(String.format("tree count: %d\n", adultModel.getTreeCount()));
System.out.printf("tree count: %d\n", adultModel.getTreeCount());

// In our case we were solving a binary classification problem (weather person makes over 50K a year), so the
// dimension of the prediction will be 1, it will return probability of the object to belong to the positive
Expand All @@ -46,16 +48,16 @@ public void playWithModelForAdultDataset() throws CatBoostError {
//
// For most of cases prediction dimension will be 1 (for regression and for ranking), it can be N for cases of
// multiclassification, where N is a number of classes.
System.out.print(String.format("prediction dimension: %d\n",adultModel.getPredictionDimension()));
System.out.printf("prediction dimension: %d\n",adultModel.getPredictionDimension());

// Take a note, number of numeric features used by the model may be less than number of numeric features
// that were present in a training dataset. This may happen if, for example, when traing dataset contained
// constant features, they do not carry any information for classifier, so training process will ignore them.
System.out.print(String.format("used numeric feature count: %d\n", adultModel.getUsedNumericFeatureCount()));
System.out.printf("used numeric feature count: %d\n", adultModel.getUsedNumericFeatureCount());

// Number of categoric features used by the classifier may also be less than number of categoric feature present
// in training dataset, for the same reasons as for numeric features.
System.out.print(String.format("used categoric feature count: %d\n", adultModel.getUsedCategoricFeatureCount()));
System.out.printf("used categoric feature count: %d\n", adultModel.getUsedCategoricFeatureCount());

// Ok now lets try to use our model for prediction. We'll look at the test part of Adult dataset. You will need
// to download it [1] from UCI repository. Look for "adult.test", "adult.name" will also be useful because it
Expand Down Expand Up @@ -91,15 +93,15 @@ public void playWithModelForAdultDataset() throws CatBoostError {
// CatBoost doesn't compute "probability", to turn CatBoost prediction into a probability we'll need to apply
// sigmoid function.
final double personAMakesOver50KProbability = sigmoid(personAPrediction.get(0, 0));
System.out.print(String.format("Person A make over 50K a year with probability %f\n", personAMakesOver50KProbability));
System.out.printf("Person A make over 50K a year with probability %f\n", personAMakesOver50KProbability);

// When we were training CatBoost we used a default classification threshold for AUC which is equal to 0.5,
// this means that our formula is optimized for this threashold, though we may change threshold to optimize some
// other metric on a different dataset, but we won't do it in this tutorial.
final double classificationThreshold = 0.5;

final boolean personAMakesOver50K = personAMakesOver50KProbability > classificationThreshold;
System.out.print(String.format("Person A %s\n", answer(personAMakesOver50K)));
System.out.printf("Person A %s\n", answer(personAMakesOver50K));

// Now lets find an example with missing features and income greater than 50K a year. At line 40 of "adult.test"
// we can find following line:
Expand All @@ -124,8 +126,8 @@ public void playWithModelForAdultDataset() throws CatBoostError {
final CatBoostPredictions personBPrediction = adultModel.predict(personBNumericFeatures, personBCategoricFeatures);
final double personBMakeOver50KProbability = sigmoid(personBPrediction.get(0, 0));
final boolean personBMakesOver50K = personBMakeOver50KProbability > classificationThreshold;
System.out.print(String.format("Person B make over 50K a year with probability %f\n", personBMakeOver50KProbability));
System.out.print(String.format("Person B %s\n", answer(personBMakesOver50K)));
System.out.printf("Person B make over 50K a year with probability %f\n", personBMakeOver50KProbability);
System.out.printf("Person B %s\n", answer(personBMakesOver50K));

// There is also a batch interface for model application, e.g. you can apply model to multiple objects at once.
//
Expand All @@ -151,10 +153,10 @@ public void playWithModelForAdultDataset() throws CatBoostError {
System.out.print("Using batch interface\n");

// Predictions should be same as above
System.out.print(String.format("Person A make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[0]));
System.out.print(String.format("Person A %s\n", answer(personsABMakeOver50K[0])));
System.out.print(String.format("Person B make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[1]));
System.out.print(String.format("Person B %s\n", answer(personsABMakeOver50K[1])));
System.out.printf("Person A make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[0]);
System.out.printf("Person A %s\n", answer(personsABMakeOver50K[0]));
System.out.printf("Person B make over 50K a year with probability %f\n", personsABMakeOver50KProbabilities[1]);
System.out.printf("Person B %s\n", answer(personsABMakeOver50K[1]));

// TODO(yazevnul): add examples with hashed categorical features when `CatBoostModel.hashCategoricalFeatures`
// will become public.
Expand Down