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<link rel="import" href="../paper-checkbox/paper-checkbox.html"> | |
<link rel="import" href="../paper-input/paper-input.html"> | |
<link rel="import" href="../core-icons/core-icons.html"> | |
<link rel="import" href="../paper-icon-button/paper-icon-button.html"> | |
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<link rel="import" href="../paper-input/paper-input.html"> | |
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<polymer-element name="my-element"> | |
<template> |
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public static void main(String[] args) throws SQLException { | |
Connection connection = getConnection(DB_NAME); | |
System.out.println(connection.getCatalog()); | |
} | |
private static Connection getConnection(String databaseName) throws SQLException { | |
return DriverManager.getConnection(URL + databaseName, USER_NAME, PASSWORD); | |
} |
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// Tune hyper-parameters with K-fold Cross-Validation on the split training set. | |
int[] pSet = new int[] {1, 2}; | |
int[] maxDeepSet = new int[] {1, 2, 3, 4, 5, 10, 20}; | |
int bestP = 1; | |
int bestMaxDeep = 1; | |
double avg = Double.MIN_VALUE; | |
for (int p : pSet) { | |
for (int maxDeep : maxDeepSet) { |
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// Tune hyper-parameters with K-fold Cross-Validation on the split training set. | |
int[] pSet = new int[] {1, 2}; | |
int[] maxDeepSet = new int[] {1, 2, 3, 4, 5, 10, 20}; | |
int bestP = 1; | |
int bestMaxDeep = 1; | |
double avg = Double.MIN_VALUE; | |
for (int p : pSet) { | |
for (int maxDeep : maxDeepSet) { |
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CrossValidation<DecisionTreeNode, Integer, Vector> scoreCalculator | |
= new CrossValidation<>(); | |
ParamGrid paramGrid = new ParamGrid() | |
.withParameterSearchStrategy(new BruteForceStrategy()) | |
.addHyperParam("p", normalizationTrainer::withP, new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}) | |
.addHyperParam("maxDeep", trainerCV::withMaxDeep, new Double[] {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0}) | |
.addHyperParam("minImpurityDecrease", trainerCV::withMinImpurityDecrease, new Double[] {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}); | |
scoreCalculator |
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Topic | Feature | Ignite ML 2.9 | Spark ML 3.0 | Note Specific to Spark | |
---|---|---|---|---|---|
Classification | Binomial logistic regression | yes | yes | ||
Classification | Decision tree classifier | yes | yes | ||
Classification | Linear SVM | yes | yes | ||
Classification | Random Forest classifier | yes | yes | ||
Classification | Gradient-boosted tree classifier | yes | yes | ||
Classification | Multilayer perceptron classifier | yes | yes | ||
Classification | KNN | yes | no | ||
Classification | Weighted KNN | yes | no | ||
Classification | Indexed KNN | yes | no |
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Number of rows, | Training time, ms, | Training time, minutes, | |
---|---|---|---|
1000, | 5709, | 0.09 | |
10000, | 19862, | 0.3 | |
100000, | 93825, | 1.5 | |
300000, | 202718, | 3.37 | |
1000000, | 694007, | 11.5 | |
2000000, | 1544073, | 25.7 | |
5000000, | 4155318, | 69 | |
10000000, | 8433875, | 140 ( 2h, 20)m |
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Number of rows | Number of features | Training time, ms | Training time, minutes | Throughput, ms/row | |
---|---|---|---|---|---|
10000 | 10 | 3611 | 0,06 | 0,3611 | |
10000 | 100 | 4910 | 0,08 | 491 | |
10000 | 1000 | 9137 | 0,15 | 0,9137 | |
100000 | 10 | 3221 | 0,05 | 0,03221 | |
100000 | 100 | 3615 | 0,06 | 0,03615 | |
100000 | 1000 | 6771 | 0,11 | 0,06771 | |
1000000 | 10 | 3174 | 0,05 | 0,003174 | |
1000000 | 100 | 3942 | 0,07 | 0,003942 | |
1000000 | 1000 | 13878 | 0,23 | 0,013878 |
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Number of rows | Number of features | Training time, ms | Training time, minutes | Throughput, ms/row | |
---|---|---|---|---|---|
1000 | 10 | 3366 | 0,06 | 3,366 | |
1000 | 100 | 4002 | 0,07 | 4,002 | |
1000 | 1000 | 14877 | 0,25 | 14,877 | |
10000 | 10 | 2894 | 0,05 | 0,2894 | |
10000 | 100 | 5718 | 0,10 | 0,5718 | |
10000 | 1000 | 21858 | 0,36 | 2,1858 | |
100000 | 10 | 10893 | 0,18 | 0,10893 | |
100000 | 100 | 12733 | 0,21 | 0,12733 | |
100000 | 1000 | 87573 | 1,46 | 0,87573 |
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