h2o-py/h2o/h2o.py
def get_model(model_id):
    """
    Load a model from the server.

    :param model_id: The model identification in H2O

    :returns: Model object, a subclass of H2OEstimator
    """
    assert_is_type(model_id, str)
    model_json = api("GET /3/Models/%s" % model_id)["models"][0]
    algo = model_json["algo"]
    if algo == "svd":            m = H2OSVD()
    elif algo == "pca":          m = H2OPrincipalComponentAnalysisEstimator()
    elif algo == "drf":          m = H2ORandomForestEstimator()
    elif algo == "naivebayes":   m = H2ONaiveBayesEstimator()
    elif algo == "kmeans":       m = H2OKMeansEstimator()
    elif algo == "glrm":         m = H2OGeneralizedLowRankEstimator()
    elif algo == "glm":          m = H2OGeneralizedLinearEstimator()
    elif algo == "gbm":          m = H2OGradientBoostingEstimator()
    elif algo == "deepwater":    m = H2ODeepWaterEstimator()
    elif algo == "xgboost":      m = H2OXGBoostEstimator()
    elif algo == "word2vec":     m = H2OWord2vecEstimator()
    elif algo == "generic": m = H2OGenericEstimator()
    elif algo == "deeplearning":
        if model_json["output"]["model_category"] == "AutoEncoder":
            m = H2OAutoEncoderEstimator()
        else:
            m = H2ODeepLearningEstimator()
    elif algo == "stackedensemble": m = H2OStackedEnsembleEstimator()
    elif algo == "isolationforest": m = H2OIsolationForestEstimator()
    else:
        raise ValueError("Unknown algo type: " + algo)
    m._resolve_model(model_id, model_json)
    return m
def get_model(model_id):
    """
    Load a model from the server.

    :param model_id: The model identification in H2O

    :returns: Model object, a subclass of H2OEstimator
    """
    assert_is_type(model_id, str)
    model_json = api("GET /3/Models/%s" % model_id)["models"][0]
    algo = model_json["algo"]
    if algo == "svd":            m = H2OSVD()
    elif algo == "pca":          m = H2OPrincipalComponentAnalysisEstimator()
    elif algo == "drf":          m = H2ORandomForestEstimator()
    elif algo == "naivebayes":   m = H2ONaiveBayesEstimator()
    elif algo == "kmeans":       m = H2OKMeansEstimator()
    elif algo == "glrm":         m = H2OGeneralizedLowRankEstimator()
    elif algo == "glm":          m = H2OGeneralizedLinearEstimator()
    elif algo == "gbm":          m = H2OGradientBoostingEstimator()
    elif algo == "deepwater":    m = H2ODeepWaterEstimator()
    elif algo == "xgboost":      m = H2OXGBoostEstimator()
    elif algo == "word2vec":     m = H2OWord2vecEstimator()
    elif algo == "generic": m = H2OGenericEstimator()
    elif algo == "deeplearning":
        if model_json["output"]["model_category"] == "AutoEncoder":
            m = H2OAutoEncoderEstimator()
        else:
            m = H2ODeepLearningEstimator()
    elif algo == "stackedensemble": m = H2OStackedEnsembleEstimator()
    elif algo == "isolationforest": m = H2OIsolationForestEstimator()
    else:
        raise ValueError("Unknown algo type: " + algo)
    m._resolve_model(model_id, model_json)
    return m
def get_model ( model_id ) : assert_is_type ( model_id , str ) model_json = api ( "GET /3/Models/%s" % model_id ) [ "models" ] [ 0 ] algo = model_json [ "algo" ] if algo == "svd" : m = H2OSVD ( ) elif algo == "pca" : m = H2OPrincipalComponentAnalysisEstimator ( ) elif algo == "drf" : m = H2ORandomForestEstimator ( ) elif algo == "naivebayes" : m = H2ONaiveBayesEstimator ( ) elif algo == "kmeans" : m = H2OKMeansEstimator ( ) elif algo == "glrm" : m = H2OGeneralizedLowRankEstimator ( ) elif algo == "glm" : m = H2OGeneralizedLinearEstimator ( ) elif algo == "gbm" : m = H2OGradientBoostingEstimator ( ) elif algo == "deepwater" : m = H2ODeepWaterEstimator ( ) elif algo == "xgboost" : m = H2OXGBoostEstimator ( ) elif algo == "word2vec" : m = H2OWord2vecEstimator ( ) elif algo == "generic" : m = H2OGenericEstimator ( ) elif algo == "deeplearning" : if model_json [ "output" ] [ "model_category" ] == "AutoEncoder" : m = H2OAutoEncoderEstimator ( ) else : m = H2ODeepLearningEstimator ( ) elif algo == "stackedensemble" : m = H2OStackedEnsembleEstimator ( ) elif algo == "isolationforest" : m = H2OIsolationForestEstimator ( ) else : raise ValueError ( "Unknown algo type: " + algo ) m . _resolve_model ( model_id , model_json ) return m
Load a model from the server.

    :param model_id: The model identification in H2O

    :returns: Model object, a subclass of H2OEstimator
Load a model from the server .