| frame/add_columns |
Add columns to current frame. |
| frame/aggregate_with_udf |
Combine by key current frame. |
| frame/assign_sample |
Randomly group rows into user-defined classes. |
| frame/bin_column |
Classify data into user-defined groups. |
| frame/bin_column_equal_depth |
Classify column into groups with the same frequency. |
| frame/bin_column_equal_width |
Classify column into same-width groups. |
| frame/box_cox |
Calculate the box-cox transformation for each row in current frame. |
| frame/categorical_summary |
Build summary of the data. |
| frame/classification_metrics |
Model statistics of accuracy, precision, and others. |
| frame/column_median |
Calculate the (weighted) median of a column. |
| frame/column_mode |
Evaluate the weights assigned to rows. |
| frame/column_summary_statistics |
Calculate multiple statistics for a column. |
| frame/copy |
New frame with copied columns. |
| frame/correlation |
Calculate correlation for two columns of current frame. |
| frame/correlation_matrix |
Calculate correlation matrix for two or more columns. |
| frame/count_where |
Counts qualified rows. |
| frame/covariance |
Calculate covariance for exactly two columns. |
| frame/covariance_matrix |
Calculate covariance matrix for two or more columns. |
| frame/cumulative_percent |
Add column to frame with cumulative percent sum. |
| frame/cumulative_sum |
Add column to frame with cumulative percent sum. |
| frame/daal_covariance_matrix |
[BETA] Calculate covariance matrix for two or more columns. |
| frame/dot_product |
Calculate dot product for each row in current frame. |
| frame/drop_columns |
Remove columns from the frame. |
| frame/drop_duplicates |
Modify the current frame, removing duplicate rows. |
| frame/ecdf |
Builds new frame with columns for data and distribution. |
| frame/entropy |
Calculate the Shannon entropy of a column. |
| frame/export_to_csv |
Write current frame to HDFS in csv format. |
| frame/export_to_hbase |
Write current frame to HBase table. |
| frame/export_to_hive |
Write current frame to Hive table. |
| frame/export_to_jdbc |
Write current frame to JDBC table. |
| frame/export_to_json |
Write current frame to HDFS in JSON format. |
| frame/flatten_columns |
Spread data to multiple rows based on cell data. |
| frame/group_by |
Summarized Frame with Aggregations. |
| frame/histogram |
Compute the histogram for a column in a frame. |
| frame/quantiles |
New frame with Quantiles and their values. |
| frame/rename |
Change the name of the current frame. |
| frame/reverse_box_cox |
Calculate the reverse box-cox transformation for each row in current frame. |
| frame/sort |
Sort by one or more columns. |
| frame/sorted_k |
Get a sorted subset of the data. |
| frame/tally |
Count number of times a value is seen. |
| frame/tally_percent |
Compute a cumulative percent count. |
| frame/timeseries_augmented_dickey_fuller_test |
Augmented Dickey-Fuller statistics test |
| frame/timeseries_breusch_godfrey_test |
Breusch-Godfrey statistics test |
| frame/timeseries_breusch_pagan_test |
Breusch-Pagan statistics test |
| frame/timeseries_durbin_watson_test |
Durbin-Watson statistics test |
| frame/timeseries_from_observations |
Returns a frame that has the observations formatted as a time series. |
| frame/timeseries_slice |
Returns a frame that is a sub-slice of the given series. |
| frame/top_k |
Most or least frequent column values. |
| frame/unflatten_columns |
Compacts data from multiple rows based on cell data. |
| frame:/filter |
Select all rows which satisfy a predicate. |
| frame:/join |
Join two data frames (similar to SQL JOIN). |
| frame:/load |
Append data from a CSV/XML into an existing (possibly empty) frame |
| frame:/rename_columns |
Rename columns |
| frame:edge/add_edges |
Add edges to a graph. |
| frame:edge/rename_columns |
Rename columns for edge frame. |
| frame:vertex/add_vertices |
Add vertices to a graph. |
| frame:vertex/drop_duplicates |
Remove duplicate vertex rows. |
| frame:vertex/filter |
|
| frame:vertex/rename_columns |
Rename columns for vertex frame. |
| graph/annotate_degrees |
Make new graph with degrees. |
| graph/annotate_weighted_degrees |
Calculates the weighted degree of each vertex with respect to an (optional) set of labels. |
| graph/clustering_coefficient |
Coefficient of graph with respect to labels. |
| graph/copy |
Make a copy of the current graph. |
| graph/graphx_connected_components |
Implements the connected components computation on a graph by invoking graphx api. |
| graph/graphx_label_propagation |
[ALPHA] Implements the label propagation computation on a graph by invoking graphx api. |
| graph/graphx_pagerank |
Determine which vertices are the most important. |
| graph/graphx_triangle_count |
Number of triangles among vertices of current graph. |
| graph/rename |
Rename a graph in the database. |
| graph:/define_edge_type |
Define an edge type. |
| graph:/define_vertex_type |
Define a vertex type by label. |
| graph:/edge_count |
Get the total number of edges in the graph. |
| graph:/export_to_orientdb |
Exports graph to OrientDB |
| graph:/kclique_percolation |
[BETA] Find groups of vertices with similar attributes. |
| graph:/label_propagation |
Classification on sparse data using Belief Propagation. |
| graph:/loopy_belief_propagation |
Classification on sparse data using Belief Propagation. |
| graph:/vertex_count |
Get the total number of vertices in the graph. |
| model/rename |
Rename a model. |
| model:arima/new |
Create a ‘new’ instance of an Autoregressive Integrated Moving Average (ARIMA) model. |
| model:arima/predict |
[ALPHA] Forecasts future periods using ARIMA. |
| model:arima/publish |
[ALPHA] Creates a tar file that will be used as input to the scoring engine |
| model:arima/train |
[ALPHA] Creates Autoregressive Integrated Moving Average (ARIMA) Model from the specified time series values. |
| model:arimax/new |
Create a ‘new’ instance of an Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX) model. |
| model:arimax/predict |
[ALPHA] New frame with column of predicted y values |
| model:arimax/publish |
[ALPHA] Creates a tar file that will be used as input to the scoring engine |
| model:arimax/train |
[ALPHA] <Missing Doc> |
| model:arx/new |
[ALPHA] Create a ‘new’ instance of a AutoRegressive Exogenous model. |
| model:arx/predict |
[ALPHA] New frame with column of predicted y values |
| model:arx/publish |
[ALPHA] Creates a tar file that will be used as input to the scoring engine |
| model:arx/train |
[ALPHA] Creates AutoregressionX (ARX) Model from train frame. |
| model:collaborative_filtering/new |
Create a new Collaborative Filtering (ALS) model. |
| model:collaborative_filtering/predict |
Collaborative Filtering Predict (ALS). |
| model:collaborative_filtering/recommend |
Collaborative Filtering Predict (ALS). |
| model:collaborative_filtering/score |
Collaborative Filtering Predict (ALS). |
| model:collaborative_filtering/train |
Collaborative filtering (ALS) model |
| model:cox_ph/new |
Create a ‘new’ instance of a Multivariate Cox Proportional Hazards model |
| model:cox_ph/predict |
[ALPHA] <Missing Doc> |
| model:cox_ph/train |
[ALPHA] Build Cox proportional hazard model. |
| model:daal_k_means/new |
[BETA] Create a ‘new’ instance of a DAAL k-means model. |
| model:daal_k_means/predict |
[BETA] Predict the cluster assignments for the data points. |
| model:daal_k_means/publish |
[BETA] Creates a tar file that will be used as input to the scoring engine |
| model:daal_k_means/train |
[ALPHA] Creates DAAL KMeans Model from train frame. |
| model:daal_linear_regression/new |
[BETA] Create a ‘new’ instance of a DAAL Linear Regression model. |
| model:daal_linear_regression/predict |
[BETA] Predict labels for a test frame using trained Intel DAAL linear regression model. |
| model:daal_linear_regression/publish |
[BETA] Creates a tar file that will be used as input to the scoring engine |
| model:daal_linear_regression/test |
[BETA] Compute test metrics for trained Intel DAAL linear regression model. |
| model:daal_linear_regression/train |
[BETA] Build Intel DAAL linear regression model. |
| model:daal_naive_bayes/new |
[BETA] Create a ‘new’ instance of a multinomial Naive Bayes model |
| model:daal_naive_bayes/predict |
[BETA] Predict labels for data points using trained multinomial Naive Bayes model. |
| model:daal_naive_bayes/publish |
[BETA] Creates a scoring engine tar file. |
| model:daal_naive_bayes/test |
[BETA] Predict test frame labels and return metrics. |
| model:daal_naive_bayes/train |
[BETA] Build a multinomial naive bayes model. |
| model:daal_principal_components/new |
[BETA] Create a ‘new’ instance of an Intel DAAL Principal Components model. |
| model:daal_principal_components/predict |
[BETA] Predict using principal components model. |
| model:daal_principal_components/publish |
[BETA] Creates a tar file that will be used as input to the scoring engine |
| model:daal_principal_components/train |
[BETA] Build Intel DAAL principal components model. |
| model:gmm/new |
Create a ‘new’ instance of a gmm model. |
| model:gmm/predict |
Predict the cluster assignments for the data points. |
| model:gmm/train |
Creates a GMM Model from the train frame. |
| model:h2o_random_forest_regressor_private/new |
[BETA] Create a ‘new’ instance of a H2O Random Forest Regressor model. |
| model:h2o_random_forest_regressor_private/predict |
[BETA] Predict the values for the data points. |
| model:h2o_random_forest_regressor_private/publish |
[BETA] Creates a tar file that will be used as input to the scoring engine |
| model:h2o_random_forest_regressor_private/test |
[BETA] Predict test frame values and return metrics. |
| model:k_means/new |
Create a ‘new’ instance of a k-means model. |
| model:k_means/predict |
Predict the cluster assignments for the data points. |
| model:k_means/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:k_means/train |
Creates KMeans Model from train frame. |
| model:lasso/new |
Create a ‘new’ instance of a Lasso Model. |
| model:lasso/predict |
Predict the labels for the data points |
| model:lasso/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:lasso/test |
Predict test frame labels and return metrics. |
| model:lasso/train |
Train Lasso Model |
| model:lda/new |
Creates Latent Dirichlet Allocation model |
| model:lda/predict |
Predict conditional probabilities of topics given document. |
| model:lda/publish |
Creates a tar file that will used as input to the scoring engine |
| model:lda/train |
Creates Latent Dirichlet Allocation model |
| model:libsvm/new |
Create a ‘new’ instance of a Support Vector Machine model. |
| model:libsvm/predict |
New frame with new predicted label column. |
| model:libsvm/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:libsvm/score |
Calculate the prediction label for a single observation. |
| model:libsvm/test |
Predict test frame labels and return metrics. |
| model:libsvm/train |
Train a Lib Svm model |
| model:linear_regression/new |
Create a ‘new’ instance of a Linear Regression model. |
| model:linear_regression/predict |
<Missing Doc> |
| model:linear_regression/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:linear_regression/test |
<Missing Doc> |
| model:linear_regression/train |
Build linear regression model. |
| model:logistic_regression/new |
Create a ‘new’ instance of logistic regression model. |
| model:logistic_regression/predict |
Predict labels for data points using trained logistic regression model. |
| model:logistic_regression/test |
Predict test frame labels and return metrics. |
| model:logistic_regression/train |
Build logistic regression model. |
| model:max/new |
Create a ‘new’ instance of Moving Average with Explanatory Variables (MAX) model. |
| model:max/predict |
[ALPHA] New frame with column of predicted y values |
| model:max/publish |
[ALPHA] Creates a tar file that will be used as input to the scoring engine |
| model:max/train |
[ALPHA] <Missing Doc> |
| model:naive_bayes/new |
Create a ‘new’ instance of a Naive Bayes model |
| model:naive_bayes/predict |
Predict labels for data points using trained Naive Bayes model. |
| model:naive_bayes/publish |
Creates a scoring engine tar file. |
| model:naive_bayes/test |
Predict test frame labels and return metrics. |
| model:naive_bayes/train |
Build a naive bayes model. |
| model:power_iteration_clustering/new |
Create a ‘new’ instance of a PowerIterationClustering model. |
| model:power_iteration_clustering/predict |
Predict the clusters to which the nodes belong to |
| model:principal_components/new |
Create a ‘new’ instance of a Principal Components model. |
| model:principal_components/predict |
Predict using principal components model. |
| model:principal_components/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:principal_components/train |
Build principal components model. |
| model:random_forest_classifier/new |
Create a ‘new’ instance of a Random Forest Classifier model. |
| model:random_forest_classifier/predict |
Predict the labels for the data points. |
| model:random_forest_classifier/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:random_forest_classifier/test |
Predict test frame labels and return metrics. |
| model:random_forest_classifier/train |
Build Random Forests Classifier model. |
| model:random_forest_regressor/new |
Create a ‘new’ instance of a Random Forest Regressor model. |
| model:random_forest_regressor/predict |
Predict the values for the data points. |
| model:random_forest_regressor/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:random_forest_regressor/train |
Build Random Forests Regressor model. |
| model:svm/new |
Create a ‘new’ instance of a Support Vector Machine model. |
| model:svm/predict |
Predict the labels for the data points |
| model:svm/publish |
Creates a tar file that will be used as input to the scoring engine |
| model:svm/test |
Predict test frame labels and return metrics. |
| model:svm/train |
Build SVM with SGD model |