Examples with JsonObject dontweave.gson.JsonObject used on opensource projects

Example 26 with JsonObject

use of dontweave.gson.JsonObject in project h2o-2 by h2oai.

the class SpeeDRF method buildForest.

private void buildForest() {
    logStart();
    SpeeDRFModel model = null;
    try {
        Frame train = setTrain();
        Frame test = setTest();
        Vec resp = regression ? null : train.lastVec().toEnum();
        if (resp != null)
            gtrash(resp);
        float[] priorDist = setPriorDist(train);
        train = setStrat(train, test, resp);
        model = initModel(train, test, priorDist);
        model.start_training(null);
        model.write_lock(self());
        drfParams = DRFParams.create(train.find(resp), model.N, model.max_depth, (int) train.numRows(), model.nbins, model.statType, use_seed, model.weights, mtries, model.sampling_strategy, (float) sample_rate, model.strata_samples, model.verbose ? 100 : 1, _exclusiveSplitLimit, true, regression);
        DRFTask tsk = new DRFTask(self(), train, drfParams, model._key, model.src_key);
        tsk.validateInputData(train);
        tsk.invokeOnAllNodes();
        Log.info("Tree building complete. Scoring...");
        model = UKV.get(dest());
        model.scoreAllTrees(test == null ? train : test, resp);
        // Launch a Variable Importance Task
        if (importance && !regression) {
            Log.info("Scoring complete. Performing Variable Importance Calculations.");
            model.current_status = "Performing Variable Importance Calculation.";
            Timer VITimer = new Timer();
            model.variableImportanceCalc(train, resp);
            Log.info("Variable Importance on " + (train.numCols() - 1) + " variables and " + ntrees + " trees done in " + VITimer);
        }
        Log.info("Generating Tree Stats");
        JsonObject trees = new JsonObject();
        trees.addProperty(Constants.TREE_COUNT, model.size());
        if (model.size() > 0) {
            trees.add(Constants.TREE_DEPTH, model.depth().toJson());
            trees.add(Constants.TREE_LEAVES, model.leaves().toJson());
        }
        model.generateHTMLTreeStats(new StringBuilder(), trees);
        model.current_status = "Model Complete";
    } finally {
        if (model != null) {
            model.unlock(self());
            model.stop_training();
        }
    }
}

Example 27 with JsonObject

use of dontweave.gson.JsonObject in project h2o-2 by h2oai.

the class SpeeDRFModel method generateHTML.

public void generateHTML(String title, StringBuilder sb) {
    String style = "<style>\n" + "td, th { min-width:60px;}\n" + "</style>\n";
    sb.append(style);
    DocGen.HTML.title(sb, title);
    sb.append("<div class=\"alert\">").append("Actions: ");
    sb.append(Inspect2.link("Inspect training data (" + _dataKey.toString() + ")", _dataKey)).append(", ");
    if (validation)
        sb.append(Inspect2.link("Inspect testing data (" + testKey.toString() + ")", testKey)).append(", ");
    sb.append(Predict.link(_key, "Score on dataset"));
    if (this.size() > 0 && this.size() < N && !Job.findJob(jobKey).isCancelledOrCrashed()) {
        sb.append(", ");
        sb.append("<i class=\"icon-stop\"></i>&nbsp;").append(Cancel.link(jobKey, "Cancel training"));
    }
    sb.append("</div>");
    DocGen.HTML.paragraph(sb, "Model Key: " + _key);
    DocGen.HTML.paragraph(sb, "Max max_depth: " + max_depth + ", Nbins: " + nbins + ", Trees: " + this.size());
    DocGen.HTML.paragraph(sb, "Sample Rate: " + sample + ", User Seed: " + get_params().seed + ", Internal Seed: " + zeed + ", mtry: " + mtry);
    sb.append("</pre>");
    if (this.size() > 0 && this.size() < N)
        sb.append("Current Status: ").append("Building Random Forest");
    else {
        if (this.size() == N && !this.current_status.equals("Performing Variable Importance Calculation.")) {
            sb.append("Current Status: ").append("Complete.");
        } else {
            if (Job.findJob(jobKey).isCancelledOrCrashed()) {
                sb.append("Current Status: ").append("Cancelled.");
            } else {
                sb.append("Current Status: ").append(this.current_status);
            }
        }
    }
    if (_have_cv_results) {
        sb.append("<div class=\"alert\">Scoring results reported for ").append(this.parameters.n_folds).append("-fold cross-validated training data ").append(Inspect2.link(_dataKey.toString(), _dataKey)).append("</div>");
    } else {
        if (testKey != null)
            sb.append("<div class=\"alert\">Reported on ").append(Inspect2.link(testKey.toString(), testKey)).append("</div>");
        else
            sb.append("<div class=\"alert\">Reported on ").append(oobee ? "OOB" : "training").append(" data</div>");
    }
    //build cm
    if (!regression) {
        //      } else {
        if (this.cms[this.cms.length - 1] != null && (this.N * .25 > 0 && classes() >= 2)) {
            this.cms[this.cms.length - 1].toHTML(sb, this.cmDomain);
        //        }
        }
    }
    sb.append("<br />");
    if (errsNotNull() && this.size() > 0) {
        DocGen.HTML.section(sb, "Mean Squared Error by Tree");
        DocGen.HTML.arrayHead(sb);
        sb.append("<tr style='min-width:60px'><th>Trees</th>");
        // + 1;
        int last = this.size();
        for (int i = last; i >= 0; i--) sb.append("<td style='min-width:60px'>").append(i).append("</td>");
        sb.append("</tr>");
        sb.append("<tr style='min-width: 60px;'><th style='min-width: 60px;' class='warning'>MSE</th>");
        for (int i = last; i >= 0; i--) sb.append((!(Double.isNaN(errs[i]) || errs[i] <= 0.0)) ? String.format("<td style='min-width:60px'>%5.5f</td>", errs[i]) : "<td style='min-width:60px'>---</td>");
        sb.append("</tr>");
        DocGen.HTML.arrayTail(sb);
    }
    sb.append("<br/>");
    JsonObject trees = new JsonObject();
    trees.addProperty(Constants.TREE_COUNT, this.size());
    if (this.size() > 0) {
        trees.add(Constants.TREE_DEPTH, this.depth().toJson());
        trees.add(Constants.TREE_LEAVES, this.leaves().toJson());
    }
    if (validAUC != null) {
        generateHTMLAUC(sb);
    }
    generateHTMLTreeStats(sb, trees);
    if (varimp != null) {
        generateHTMLVarImp(sb);
    }
    printCrossValidationModelsHTML(sb);
}

Example 28 with JsonObject

use of dontweave.gson.JsonObject in project h2o-2 by h2oai.

the class Models method summarizeDeepLearningModel.

/**
   * Summarize fields which are specific to hex.deeplearning.DeepLearningModel.
   */
private static void summarizeDeepLearningModel(ModelSummary summary, hex.deeplearning.DeepLearningModel model) {
    // add generic fields such as column names
    summarizeModelCommonFields(summary, model);
    summary.model_algorithm = "DeepLearning";
    JsonObject all_params = (model.get_params()).toJSON();
    summary.critical_parameters = whitelistJsonObject(all_params, DL_critical_params);
    summary.secondary_parameters = whitelistJsonObject(all_params, DL_secondary_params);
    summary.expert_parameters = whitelistJsonObject(all_params, DL_expert_params);
}

Example 29 with JsonObject

use of dontweave.gson.JsonObject in project h2o-2 by h2oai.

the class Models method serveOneOrAll.

/**
   * Fetch all the Models from the KV store, sumamrize and enhance them, and return a map of them.
   */
private Response serveOneOrAll(Map<String, Model> modelsMap) {
    // returns empty sets if !this.find_compatible_frames
    Pair<Map<String, Frame>, Map<String, Set<String>>> frames_info = fetchFrames();
    Map<String, Frame> all_frames = frames_info.getFirst();
    Map<String, Set<String>> all_frames_cols = frames_info.getSecond();
    Map<String, ModelSummary> modelSummaries = Models.generateModelSummaries(null, modelsMap, find_compatible_frames, all_frames, all_frames_cols);
    Map resultsMap = new LinkedHashMap();
    resultsMap.put("models", modelSummaries);
    // If find_compatible_frames then include a map of the Frame summaries.  Should we put this on a separate switch?
    if (this.find_compatible_frames) {
        Set<String> all_referenced_frames = new TreeSet<String>();
        for (Map.Entry<String, ModelSummary> entry : modelSummaries.entrySet()) {
            ModelSummary summary = entry.getValue();
            all_referenced_frames.addAll(summary.compatible_frames);
        }
        Map<String, FrameSummary> frameSummaries = Frames.generateFrameSummaries(all_referenced_frames, all_frames, false, null, null);
        resultsMap.put("frames", frameSummaries);
    }
    // TODO: temporary hack to get things going
    String json = gson.toJson(resultsMap);
    JsonObject result = gson.fromJson(json, JsonElement.class).getAsJsonObject();
    return Response.done(result);
}

Example 30 with JsonObject

use of dontweave.gson.JsonObject in project h2o-2 by h2oai.

the class Models method summarizeNBModel.

/**
   * Summarize fields which are specific to hex.nb.NBModel.
   */
private static void summarizeNBModel(ModelSummary summary, hex.nb.NBModel model) {
    // add generic fields such as column names
    summarizeModelCommonFields(summary, model);
    summary.model_algorithm = "Naive Bayes";
    JsonObject all_params = (model.get_params()).toJSON();
    summary.critical_parameters = whitelistJsonObject(all_params, NB_critical_params);
    summary.secondary_parameters = whitelistJsonObject(all_params, NB_secondary_params);
    summary.expert_parameters = whitelistJsonObject(all_params, NB_expert_params);
}