public static TwoDimTable createCenterTable(
KMeansModel.KMeansOutput output, boolean standardized) {
String name = standardized ? "Standardized Cluster Means" : "Cluster Means";
if (output._size == null
|| output._names == null
|| output._domains == null
|| output._centers_raw == null
|| (standardized && output._centers_std_raw == null)) {
TwoDimTable table =
new TwoDimTable(
name,
null,
new String[] {"1"},
new String[] {"C1"},
new String[] {"double"},
new String[] {"%f"},
"Centroid");
table.set(0, 0, Double.NaN);
return table;
}
String[] rowHeaders = new String[output._size.length];
for (int i = 0; i < rowHeaders.length; i++) rowHeaders[i] = String.valueOf(i + 1);
String[] colTypes = new String[output._names.length];
String[] colFormats = new String[output._names.length];
for (int i = 0; i < output._domains.length; ++i) {
colTypes[i] = output._domains[i] == null ? "double" : "String";
colFormats[i] = output._domains[i] == null ? "%f" : "%s";
}
TwoDimTable table =
new TwoDimTable(name, null, rowHeaders, output._names, colTypes, colFormats, "Centroid");
for (int j = 0; j < output._domains.length; ++j) {
boolean string = output._domains[j] != null;
if (string) {
for (int i = 0; i < output._centers_raw.length; ++i) {
table.set(i, j, output._domains[j][(int) output._centers_raw[i][j]]);
}
} else {
for (int i = 0; i < output._centers_raw.length; ++i) {
table.set(i, j, standardized ? output._centers_std_raw[i][j] : output._centers_raw[i][j]);
}
}
}
return table;
}
private TwoDimTable createModelSummaryTable(KMeansModel.KMeansOutput output) {
List<String> colHeaders = new ArrayList<>();
List<String> colTypes = new ArrayList<>();
List<String> colFormat = new ArrayList<>();
colHeaders.add("Number of Rows");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Number of Clusters");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Number of Categorical Columns");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Number of Iterations");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Within Cluster Sum of Squares");
colTypes.add("double");
colFormat.add("%.5f");
colHeaders.add("Total Sum of Squares");
colTypes.add("double");
colFormat.add("%.5f");
colHeaders.add("Between Cluster Sum of Squares");
colTypes.add("double");
colFormat.add("%.5f");
final int rows = 1;
TwoDimTable table =
new TwoDimTable(
"Model Summary",
null,
new String[rows],
colHeaders.toArray(new String[0]),
colTypes.toArray(new String[0]),
colFormat.toArray(new String[0]),
"");
int row = 0;
int col = 0;
table.set(
row,
col++,
Math.round(_train.numRows() * (hasWeightCol() ? _train.lastVec().mean() : 1)));
table.set(row, col++, output._centers_raw.length);
table.set(row, col++, output._categorical_column_count);
table.set(row, col++, output._iterations);
table.set(row, col++, output._tot_withinss);
table.set(row, col++, output._totss);
table.set(row, col++, output._betweenss);
return table;
}
private TwoDimTable createScoringHistoryTable(KMeansModel.KMeansOutput output) {
List<String> colHeaders = new ArrayList<>();
List<String> colTypes = new ArrayList<>();
List<String> colFormat = new ArrayList<>();
colHeaders.add("Timestamp");
colTypes.add("string");
colFormat.add("%s");
colHeaders.add("Duration");
colTypes.add("string");
colFormat.add("%s");
colHeaders.add("Iteration");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Avg. Change of Std. Centroids");
colTypes.add("double");
colFormat.add("%.5f");
colHeaders.add("Within Cluster Sum Of Squares");
colTypes.add("double");
colFormat.add("%.5f");
final int rows = output._avg_centroids_chg.length;
TwoDimTable table =
new TwoDimTable(
"Scoring History",
null,
new String[rows],
colHeaders.toArray(new String[0]),
colTypes.toArray(new String[0]),
colFormat.toArray(new String[0]),
"");
int row = 0;
for (int i = 0; i < rows; i++) {
int col = 0;
assert (row < table.getRowDim());
assert (col < table.getColDim());
DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
table.set(row, col++, fmt.print(output._training_time_ms[i]));
table.set(row, col++, PrettyPrint.msecs(output._training_time_ms[i] - _start_time, true));
table.set(row, col++, i);
table.set(row, col++, output._avg_centroids_chg[i]);
table.set(row, col++, output._history_withinss[i]);
row++;
}
return table;
}
private TwoDimTable createModelSummaryTable(SharedTreeModel.SharedTreeOutput _output) {
List<String> colHeaders = new ArrayList<>();
List<String> colTypes = new ArrayList<>();
List<String> colFormat = new ArrayList<>();
colHeaders.add("Number of Trees");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Model Size in Bytes");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Min. Depth");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Max. Depth");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Mean Depth");
colTypes.add("double");
colFormat.add("%.5f");
colHeaders.add("Min. Leaves");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Max. Leaves");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Mean Leaves");
colTypes.add("double");
colFormat.add("%.5f");
final int rows = 1;
TwoDimTable table =
new TwoDimTable(
"Model Summary",
null,
new String[rows],
colHeaders.toArray(new String[0]),
colTypes.toArray(new String[0]),
colFormat.toArray(new String[0]),
"");
int row = 0;
int col = 0;
table.set(row, col++, _output._treeStats._num_trees);
table.set(row, col++, _output._treeStats._byte_size);
table.set(row, col++, _output._treeStats._min_depth);
table.set(row, col++, _output._treeStats._max_depth);
table.set(row, col++, _output._treeStats._mean_depth);
table.set(row, col++, _output._treeStats._min_leaves);
table.set(row, col++, _output._treeStats._max_leaves);
table.set(row, col++, _output._treeStats._mean_leaves);
return table;
}
/**
* Print a summary table
*
* @return String containing ASCII version of summary table
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
if (get_params()._diagnostics && !get_params()._quiet_mode) {
if (get_params()._sparsity_beta > 0) {
for (int k = 0; k < get_params()._hidden.length; k++) {
sb.append("Average activation in hidden layer ")
.append(k)
.append(" is ")
.append(mean_a[k])
.append(" \n");
}
}
createSummaryTable();
sb.append(summaryTable.toString(1));
}
return sb.toString();
}
/**
* Create a summary table
*
* @return
*/
TwoDimTable createSummaryTable() {
Neurons[] neurons = DeepLearningTask.makeNeuronsForTesting(this);
long byte_size = new AutoBuffer().put(this).buf().length;
TwoDimTable table =
new TwoDimTable(
"Status of Neuron Layers",
(get_params()._diagnostics ? "" : "diagnostics disabled, ")
+ (!get_params()._autoencoder ? ("predicting " + _train.lastVecName() + ", ") : "")
+ (get_params()._autoencoder
? "auto-encoder"
: _classification
? (units[units.length - 1] + "-class classification")
: "regression")
+ ", "
+ get_params()._distribution
+ " distribution, "
+ get_params()._loss
+ " loss, "
+ String.format("%,d", size())
+ " weights/biases, "
+ PrettyPrint.bytes(byte_size)
+ ", "
+ String.format("%,d", get_processed_global())
+ " training samples, "
+ "mini-batch size "
+ String.format("%,d", get_params()._mini_batch_size),
new String[neurons.length],
new String[] {
"Layer",
"Units",
"Type",
"Dropout",
"L1",
"L2",
"Mean Rate",
"Rate RMS",
"Momentum",
"Mean Weight",
"Weight RMS",
"Mean Bias",
"Bias RMS"
},
new String[] {
"int", "int", "string", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double"
},
new String[] {
"%d",
"%d",
"%s",
"%2.2f %%",
"%5f",
"%5f",
"%5f",
"%5f",
"%5f",
"%5f",
"%5f",
"%5f",
"%5f"
},
"");
for (int i = 0; i < neurons.length; ++i) {
table.set(i, 0, i + 1);
table.set(i, 1, neurons[i].units);
table.set(i, 2, neurons[i].getClass().getSimpleName());
if (i == 0) {
table.set(i, 3, neurons[i].params._input_dropout_ratio * 100);
continue;
} else if (i < neurons.length - 1) {
if (neurons[i].params._hidden_dropout_ratios == null) {
table.set(i, 3, 0);
} else {
table.set(i, 3, neurons[i].params._hidden_dropout_ratios[i - 1] * 100);
}
}
table.set(i, 4, neurons[i].params._l1);
table.set(i, 5, neurons[i].params._l2);
table.set(
i,
6,
(get_params()._adaptive_rate ? mean_rate[i] : neurons[i].rate(get_processed_total())));
table.set(i, 7, (get_params()._adaptive_rate ? rms_rate[i] : 0));
table.set(i, 8, get_params()._adaptive_rate ? 0 : neurons[i].momentum(get_processed_total()));
table.set(i, 9, mean_weight[i]);
table.set(i, 10, rms_weight[i]);
table.set(i, 11, mean_bias[i]);
table.set(i, 12, rms_bias[i]);
}
summaryTable = table;
return summaryTable;
}
private TwoDimTable createScoringHistoryTable(SharedTreeModel.SharedTreeOutput _output) {
List<String> colHeaders = new ArrayList<>();
List<String> colTypes = new ArrayList<>();
List<String> colFormat = new ArrayList<>();
colHeaders.add("Timestamp");
colTypes.add("string");
colFormat.add("%s");
colHeaders.add("Duration");
colTypes.add("string");
colFormat.add("%s");
colHeaders.add("Number of Trees");
colTypes.add("long");
colFormat.add("%d");
colHeaders.add("Training MSE");
colTypes.add("double");
colFormat.add("%.5f");
if (_output.isClassifier()) {
colHeaders.add("Training LogLoss");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.getModelCategory() == ModelCategory.Binomial) {
colHeaders.add("Training AUC");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.getModelCategory() == ModelCategory.Binomial
|| _output.getModelCategory() == ModelCategory.Multinomial) {
colHeaders.add("Training Classification Error");
colTypes.add("double");
colFormat.add("%.5f");
}
if (valid() != null) {
colHeaders.add("Validation MSE");
colTypes.add("double");
colFormat.add("%.5f");
if (_output.isClassifier()) {
colHeaders.add("Validation LogLoss");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.getModelCategory() == ModelCategory.Binomial) {
colHeaders.add("Validation AUC");
colTypes.add("double");
colFormat.add("%.5f");
}
if (_output.isClassifier()) {
colHeaders.add("Validation Classification Error");
colTypes.add("double");
colFormat.add("%.5f");
}
}
int rows = 0;
for (int i = 1; i < _output._scored_train.length; i++) {
if (!Double.isNaN(_output._scored_train[i]._mse)) ++rows;
}
TwoDimTable table =
new TwoDimTable(
"Scoring History",
null,
new String[rows],
colHeaders.toArray(new String[0]),
colTypes.toArray(new String[0]),
colFormat.toArray(new String[0]),
"");
int row = 0;
for (int i = 1; i < _output._scored_train.length; i++) {
if (Double.isNaN(_output._scored_train[i]._mse)) continue;
int col = 0;
assert (row < table.getRowDim());
assert (col < table.getColDim());
DateTimeFormatter fmt = DateTimeFormat.forPattern("yyyy-MM-dd HH:mm:ss");
table.set(row, col++, fmt.print(_output._training_time_ms[i]));
table.set(row, col++, PrettyPrint.msecs(_output._training_time_ms[i] - _start_time, true));
table.set(row, col++, i);
ScoreKeeper st = _output._scored_train[i];
table.set(row, col++, st._mse);
if (_output.isClassifier()) table.set(row, col++, st._logloss);
if (_output.getModelCategory() == ModelCategory.Binomial) table.set(row, col++, st._AUC);
if (_output.isClassifier()) table.set(row, col++, st._classError);
if (_valid != null) {
st = _output._scored_valid[i];
table.set(row, col++, st._mse);
if (_output.isClassifier()) table.set(row, col++, st._logloss);
if (_output.getModelCategory() == ModelCategory.Binomial) table.set(row, col++, st._AUC);
if (_output.isClassifier()) table.set(row, col++, st._classError);
}
row++;
}
return table;
}