BigMLer - A command-line tool for BigML’s API

BigMLer makes BigML even easier.

BigMLer wraps BigML’s API Python bindings to offer a high-level command-line script to easily create and publish datasets and models, create ensembles, make local predictions from multiple models, clusters and simplify many other machine learning tasks.

BigMLer is open sourced under the Apache License, Version 2.0.

BigMLer subcommands

In addition to the BigMLer simple command, that covers the main functionality, there are some additional subcommands:

Usual workflows’ subcommands

bigmler cluster:

Used to generate clusters and centroids’ predictions See Cluster subcommand.

bigmler anomaly:

Used to generate anomaly detectors and anomaly scores. See Anomaly subcommand.

bigmler sample:

Used to generate samples of data from your existing datasets. See Sample subcommand.

bigmler association:

Used to generate association rules from your datasets. See Association subcommand.

bigmler logistic-regression:

Used to generate logistic regression models and predictions. See Logistic-regression subcommand.

bigmler topic-model:

Used to generate topic models and topic distributions. See Topic Model subcommand.

bigmler time-series:

Used to generate time series and forecasts. See Time Series subcommand.

bigmler deepnets:

Used to generate deepnets and their predictions. See Deepnet subcommand.

bigmler project:

Used to generate and manage projects for organization purposes. See Project subcommand.

Management subcommands

bigmler delete:

Used to delete the remotely created resources. See Delete subcommand.

bigmler.export:

Used to generate the code you need to predict locally with no connection to BigML. See Export subcommand.

Reporting subcommands

bigmler report:

Used to generate reports for the analyze subcommand showing the ROC curve and evaluation metrics of cross-validations. See Report subcommand.

Model tuning subcommands

bigmler analyze:

Used for feature analysis, node threshold analysis and k-fold cross-validation. See Analyze subcommand.

Scripting subcommands

bigmler reify:

Used to generate scripts to reproduce the existing resources in BigML. See Reify subcommand.

bigmler execute:

Used to create WhizzML libraries or scripts and execute them. See Execute subcommand.

bigmler whizzml:

Used to create WhizzML packages of libraries or scripts based on the information of the metadata.json file in the package directory. See Whizzml subcommand

bigmler retrain:

Used to retrain models by adding new data to the existing datasets and building a new model from it. See Retrain subcommand

Quick Start

Let’s see some basic usage examples. Check the installation and authentication sections below if you are not familiar with BigML.

Basics

You can create a new model just with

bigmler --train data/iris.csv

If you check your dashboard at BigML, you will see a new source, dataset, and model. Isn’t it magic?

You can generate predictions for a test set using

bigmler --train data/iris.csv --test data/test_iris.csv

You can also specify a file name to save the newly created predictions

bigmler --train data/iris.csv --test data/test_iris.csv --output predictions

If you do not specify the path to an output file, BigMLer will auto-generate one for you under a new directory named after the current date and time (e.g., MonNov1212_174715/predictions.csv). With --prediction-info flag set to brief only the prediction result will be stored (default is normal and includes confidence information). You can also set it to full if you prefer the result to be presented as a row with your test input data followed by the corresponding prediction. To include a headers row in the prediction file you can set --prediction-header. For both the --prediction-info full and --prediction-info brief options, if you want to include a subset of the fields in your test file you can select them by setting --prediction-fields to a comma-separated list of them. Then

bigmler --train data/iris.csv --test data/test_iris.csv \
        --prediction-info full --prediction-header \
        --prediction-fields 'petal length','petal width'

will include in the generated predictions file a headers row

petal length,petal width,species,confidence

and only the values of petal length and petal width will be shown before the objective field prediction species.

A different objective field (the field that you want to predict) can be selected using

bigmler --train data/iris.csv --test data/test_iris.csv \
        --objective 'sepal length'

If you do not explicitly specify an objective field, BigML will default to the last column in your dataset. You can also use as selector the field column number instead of the name (when –no-train-header is used, for instance).

Also, if your test file uses a particular field separator for its data, you can tell BigMLer using --test-separator. For example, if your test file uses the tab character as field separator the call should be like

bigmler --train data/iris.csv --test data/test_iris.tsv \
        --test-separator '\t'

The model’s predictions in BigMLer are based on the mean of the distribution of training values in the predicted node. In case you would like to use the median instead, you could just add the --median flag to your command

bigmler --train data/grades.csv --test data/test_grades.csv \
        --median

Note that this flag can only be applied to regression models.

If you don’t provide a file name for your training source, BigMLer will try to read it from the standard input

cat data/iris.csv | bigmler --train

or you can also read the test info from there

cat data/test_iris.csv | bigmler --train data/iris.csv --test

BigMLer will try to use the locale of the model both to create a new source (if the --train flag is used) and to interpret test data. In case it fails, it will try en_US.UTF-8 or English_United States.1252 and a warning message will be printed. If you want to change this behaviour you can specify your preferred locale

bigmler --train data/iris.csv --test data/test_iris.csv \
        --locale "English_United States.1252"

If you check your working directory you will see that BigMLer creates a file with the model ids that have been generated (e.g., FriNov0912_223645/models). This file is handy if then you want to use those model ids to generate local predictions. BigMLer also creates a file with the dataset id that has been generated (e.g., TueNov1312_003451/dataset) and another one summarizing the steps taken in the session progress: bigmler_sessions. You can also store a copy of every created or retrieved resource in your output directory (e.g., TueNov1312_003451/model_50c23e5e035d07305a00004f) by setting the flag --store.

Remote Predictions

All the predictions we saw in the previous section are computed locally in your computer. BigMLer allows you to ask for a remote computation by adding the --remote flag. Remote computations are treated as batch computations. This means that your test data will be loaded in BigML as a regular source and the corresponding dataset will be created and fed as input data to your model to generate a remote batch prediction object. BigMLer will download the predictions file created as a result of this batch prediction and save it to local storage just as it did for local predictions

bigmler --train data/iris.csv --test data/test_iris.csv \
        --remote --output my_dir/remote_predictions.csv

This command will create a source, dataset and model for your training data, a source and dataset for your test data and a batch prediction using the model and the test dataset. The results will be stored in the my_dir/remote_predictions.csv file. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output.

In case you prefer BigMLer to issue one-by-one remote prediction calls, you can use the --no-batch flag

bigmler --train data/iris.csv --test data/test_iris.csv \
        --remote --no-batch

Remote Sources

You can create models using remote sources as well. You just need a valid URL that points to your data. BigML recognizes a growing list of schemas (http, https, s3, azure, odata, etc). For example

bigmler --train https://test:test@static.bigml.com/csv/iris.csv

bigmler --train "s3://bigml-public/csv/iris.csv?access-key=[your-access-key]&secret-key=[your-secret-key]"

bigmler --train azure://csv/diabetes.csv?AccountName=bigmlpublic

bigmler --train odata://api.datamarket.azure.com/www.bcn.cat/BCNOFFERING0005/v1/CARRegistration?$top=100

Can you imagine how powerful this feature is? You can create predictive models for huge amounts of data without using you local CPU, memory, disk or bandwidth. Welcome to the cloud!!!

Ensembles

You can also easily create ensembles. For example, using bagging is as easy as

bigmler --train data/iris.csv --test data/test_iris.csv \
        --number-of-models 10 --sample-rate 0.75 --replacement \
        --tag my_ensemble

To create a random decision forest just use the –randomize option

bigmler --train data/iris.csv --test data/test_iris.csv \
        --number-of-models 10 --sample-rate 0.75 --replacement \
        --tag my_random_forest --randomize

The fields to choose from will be randomized at each split creating a random decision forest that when used together will increase the prediction performance of the individual models.

To create a boosted trees’ ensemble use the –boosting option

bigmler --train data/iris.csv --test data/test_iris.csv \
        --boosting --tag my_boosted_trees

or add the ``–boosting-iterations` limit

bigmler --train data/iris.csv --test data/test_iris.csv \
        --booting-iterations 10 --sample-rate 0.75 --replacement \
        --tag my_boosted_trees

Once you have an existing ensemble, you can use it to predict. You can do so with the command

bigmler --ensemble ensemble/51901f4337203f3a9a000215 \
        --test data/test_iris.csv

Or if you want to evaluate it

bigmler --ensemble ensemble/51901f4337203f3a9a000215 \
        --test data/iris.csv --evaluate

There are some more advanced options that can help you build local predictions with your ensembles. When the number of local models becomes quite large holding all the models in memory may exhaust your resources. To avoid this problem you can use the --max_batch_models flag which controls how many local models are held in memory at the same time

bigmler --train data/iris.csv --test data/test_iris.csv \
        --number-of-models 10 --sample-rate 0.75 --max-batch-models 5

The predictions generated when using this option will be stored in a file per model and named after the models’ id (e.g. model_50c23e5e035d07305a00004f__predictions.csv”). Each line contains the prediction, its confidence, the node’s distribution and the node’s total number of instances. The default value for ``max-batch-models` is 10.

When using ensembles, model’s predictions are combined to issue a final prediction. There are several different methods to build the combination. You can choose plurality, confidence weighted, probability weighted or threshold using the --method flag

bigmler --train data/iris.csv --test data/test_iris.csv \
        --number-of-models 10 --sample-rate 0.75 \
        --method "confidence weighted"

For classification ensembles, the combination is made by majority vote: plurality weights each model’s prediction as one vote, confidence weighted uses confidences as weight for the prediction, probability weighted uses the probability of the class in the distribution of classes in the node as weight, and threshold uses an integer number as threshold and a class name to issue the prediction: if the votes for the chosen class reach the threshold value, then the class is predicted and plurality for the rest of predictions is used otherwise

bigmler --train data/iris.csv --test data/test_iris.csv \
        --number-of-models 10 --sample-rate 0.75 \
        --method threshold --threshold 4 --class 'Iris-setosa'

For regression ensembles, the predicted values are averaged: plurality again weights each predicted value as one, confidence weighted weights each prediction according to the associated error and probability weighted gives the same results as plurality.

As in the model’s case, you can base your prediction on the median of the predicted node’s distribution by adding --median to your BigMLer command.

It is also possible to enlarge the number of models that build your prediction gradually. You can build more than one ensemble for the same test data and combine the votes of all of them by using the flag combine_votes followed by the comma separated list of directories where predictions are stored. For instance

bigmler --train data/iris.csv --test data/test_iris.csv \
        --number-of-models 20 --sample-rate 0.75 \
        --output ./dir1/predictions.csv
bigmler --dataset dataset/50c23e5e035d07305a000056 \
        --test data/test_iris.csv  --number-of-models 20 \
        --sample-rate 0.75 --output ./dir2/predictions.csv
bigmler --combine-votes ./dir1,./dir2

would generate a set of 20 prediction files, one for each model, in ./dir1, a similar set in ./dir2 and combine all of them to generate the final prediction.

Making your Dataset and Model public or share it privately

Creating a model and making it public in BigML’s gallery is as easy as

bigmler --train data/iris.csv --white-box

If you just want to share it as a black-box model just use

bigmler --train data/iris.csv --black-box

If you also want to make public your dataset

bigmler --train data/iris.csv --public-dataset

You can also share your datasets, models and evaluations privately with whomever you choose by generating a private link. The --shared flag will create such a link

bigmler --dataset dataset/534487ef37203f0d6b000894 --shared --no-model

and the link will be listed in the output of the command

bigmler --dataset dataset/534487ef37203f0d6b000894 --shared --no-model
[2014-04-18 09:29:27] Retrieving dataset. https://bigml.com/dashboard/dataset/534487ef37203f0d6b000894
[2014-04-18 09:29:30] Updating dataset. https://bigml.com/dashboard/dataset/534487ef37203f0d6b000894
[2014-04-18 09:29:30] Shared dataset link. https://bigml.com/shared/dataset/8VPwG7Ny39g1mXBRD1sKQLuHrqE

or can also be found in the information pannel for the resource through the web interface.

Content

Before making your model public, probably you want to add a name, a category, a description, and tags to your resources. This is easy too. For example

bigmler --train data/iris.csv --name "My model" --category 6 \
        --description data/description.txt --tag iris --tag my_tag

Please note:

  • You can get a full list of BigML category codes here.
  • Descriptions are provided in a text file that can also include markdown.
  • Many tags can be added to the same resource.
  • Use --no_tag if you do not want default BigMLer tags to be added.
  • BigMLer will add the name, category, description, and tags to all the newly created resources in each request.

Projects

Each resource created in BigML can be associated to a project. Projects are intended for organizational purposes, and BigMLer can create projects each time a source is created using a --project option. For instance

bigmler --train data/iris.csv --project "my new project"

will first check for the existence of a project by that name. If it exists, will associate the source, dataset and model resources to this project. If it doesn’t, a new project is created and then associated.

You can also associate resources to any project in your account by specifying the option --project-id followed by its id

bigmler --train data/iris.csv --project-id project/524487ef37203f0d6b000894

Note: Once a source has been associated to a project, all the resources derived from this source will be automatically associated to the same project.

You can also create projects or update their properties by using the bigmler project subcommand. In particular, when projects need to be created in an organization, the --organization option has to be added to inform about the ID of the organization where the project should be created:

bigmler project --organization organization/524487ef37203f0d6b000594 \
                --name "my new project"

Only allowed users can create projects in organizations. If you are not the owner or an administrator, please check your permissions with them first. You can learn more about organizations at the API documentation.

You can also create resources in an organization’s project if your user has the right privileges. In order to do that, you should add the --org-project option followed by the organization’s project ID.

bigmler --train data/iris.csv \
        --org-project project/524487ef37203f0d6b000894

Using the existing resources in BigML

You don’t need to create a model from scratch every time that you use BigMLer. You can generate predictions for a test set using a previously generated model, cluster, etc. The example shows how you would do that for a tree model:

bigmler --model model/50a1f43deabcb404d3000079 --test data/test_iris.csv

You can also use a number of models providing a file with a model/id per line

bigmler --models TueDec0412_174148/models --test data/test_iris.csv

Or all the models that were tagged with a specific tag

bigmler --model-tag my_tag --test data/test_iris.csv

The same can be extended to any other subcomamnd, like bigmler cluster using the correct option (--cluster cluster/50a1f43deabcb404d3000da2, --clusters TueDec0412_174148/clusters and cluster-tag my_tag). Please, check each subcommand available options for details.

You can also use a previously generated dataset to create a new model

bigmler --dataset dataset/50a1f441035d0706d9000371

You can also input the dataset from a file

bigmler --datasets iris_dataset

A previously generated source can also be used to generate a new dataset and model

bigmler --source source/50a1e520eabcb404cd0000d1

And test sources and datasets can also be referenced by id in new BigMLer requests for remote predictions

bigmler --model model/52af53a437203f1cfe0001f0 --remote \
        --test-source source/52b0cbe637203f1d3e0015db

bigmler --model model/52af53a437203f1cfe0001f0 --remote \
        --test-dataset dataset/52b0fb5637203f5c4f000018

Evaluations

BigMLer can also help you to measure the performance of your supervised models (decision trees, ensembles and logistic regressions). The simplest way to build a model and evaluate it all at once is

bigmler --train data/iris.csv --evaluate

which will build the source, dataset and model objects for you using 80% of the data in your training file chosen at random. After that, the remaining 20% of the data will be run through the model to obtain the corresponding evaluation.

The same procedure is available for ensembles:

bigmler --train data/iris.csv --number-of-models 10 --evaluate

and for logistic regressions:

bigmler logistic-regression --train data/iris.csv --evaluate

You can use the same procedure with a previously existing source or dataset

bigmler --source source/50a1e520eabcb404cd0000d1 --evaluate
bigmler --dataset dataset/50a1f441035d0706d9000371 --evaluate

The results of an evaluation are stored both in txt and json files. Its contents will follow the description given in the Developers guide, evaluation section and vary depending on the model being a classification or regression one.

Finally, you can also evaluate a preexisting model using a separate set of data stored in a file or a previous dataset

bigmler --model model/50a1f43deabcb404d3000079 --test data/iris.csv \
        --evaluate
bigmler --model model/50a1f43deabcb404d3000079 \
        --test-dataset dataset/50a1f441035d0706d9000371 --evaluate

As for predictions, you can specify a particular file name to store the evaluation in

bigmler --train data/iris.csv --evaluate --output my_dir/evaluation

Cross-validation

If you need cross-validation techniques to ponder which parameters (like the ones related to different kinds of pruning) can improve the quality of your models, you can use the --cross-validation-rate flag to settle the part of your training data that will be separated for cross validation. BigMLer will use a Monte-Carlo cross-validation variant, building 2*n different models, each of which is constructed by a subset of the training data, holding out randomly n% of the instances. The held-out data will then be used to evaluate the corresponding model. For instance, both

bigmler --train data/iris.csv --cross-validation-rate 0.02
bigmler --dataset dataset/519029ae37203f3a9a0002bf \
        --cross-validation-rate 0.02

will hold out 2% of the training data to evaluate a model built upon the remaining 98%. The evaluations will be averaged and the result saved in json and human-readable formats in cross-validation.json and cross-validation.txt respectively. Of course, in this kind of cross-validation you can choose the number of evaluations yourself by setting the --number-of-evaluations flag. You should just keep in mind that it must be high enough to ensure low variance, for instance

bigmler --train data/iris.csv --cross-validation-rate 0.1 \
        --number-of-evaluations 20

The --max-parallel-evaluations flag will help you limit the number of parallel evaluation creation calls.

bigmler --train data/iris.csv --cross-validation-rate 0.1 \
        --number-of-evaluations 20 --max-parallel-evaluations 2

Configuring Datasets and Models

What if your raw data isn’t necessarily in the format that BigML expects? So we have good news: you can use a number of options to configure your sources, datasets, and models.

Most resources in BigML contain information about the fields used in the resource construction. Sources contain information about the name, label, description and type of the fields detected in the data you upload. In addition to that, datasets contain the information of the values that each field contains, whether they have missing values or errors and even if they are preferred fields or non-preferred (fields that are not expected to convey real information to the model, like user IDs or constant fields). This information is available in the “fields” attribute of each resource, but BigMLer can extract it and build a CSV file with a summary of it.

bigmler --source source/50a1f43deabcb404d3010079 \
        --export-fields fields_summary.csv \
        --output-dir summary

By using this command, BigMLer will create a fields_summary.csv file in a summary output directory. The file will contain a headers row and the fields information available in the source, namely the field column, field ID, field name, field label and field description of each field. If you execute the same command on a dataset

bigmler --dataset dataset/50a1f43deabcb404d3010079 \
        --export-fields fields_summary.csv \
        --output-dir summary

you will also see the number of missing values and errors found in each field and an excerpt of the values and errors.

But then, imagine that you want to alter BigML’s default field names or the ones provided by the training set header and capitalize them, even to add a label or a description to each field. You can use several methods. Write a text file with a change per line as follows

bigmler --train data/iris.csv --field-attributes fields.csv

where fields.csv would be

0,'SEPAL LENGTH','label for SEPAL LENGTH','description for SEPAL LENGTH'
1,'SEPAL WIDTH','label for SEPAL WIDTH','description for SEPAL WIDTH'
2,'PETAL LENGTH','label for PETAL LENGTH','description for PETAL LENGTH'
3,'PETAL WIDTH','label for PETAL WIDTH','description for PETAL WIDTH'
4,'SPECIES','label for SPECIES','description for SPECIES'

The number on the left in each line is the column number of the field in your source and is followed by the new field’s name, label and description.

Similarly you can also alter the auto-detect type behavior from BigML assigning specific types to specific fields

bigmler --train data/iris.csv --types types.txt

where types.txt would be

0, 'numeric'
1, 'numeric'
2, 'numeric'
3, 'numeric'
4, 'categorical'

Finally, the same summary file that could be built with the --export-fields option can be used to modify the updatable information in sources and datasets. Just edit the CSV file with your favourite editor setting the new values for the fields and use:

bigmler --source source/50a1f43deabcb404d3010079 \
        --import-fields summary/fields_summary.csv

to update the names, labels, descriptions or types of the fields with the ones in the summary/fields_summary.csv file.

You could also use this option to change the preferred attributes for each of the fields. This transformation is made at the dataset level, so in the prior code it will be applied once a dataset is created from the referred source. You might as well act on an existing dataset:

bigmler --dataset dataset/50a1f43deabcb404d3010079 \
        --import-fields summary/fields_summary.csv

In order to update more detailed source options, you can use the --source-attributes option pointing to a file path that contains the configuration settings to be modified in JSON format

bigmler --source source/52b8a12037203f48bc00000a \
        --source-attributes my_dir/attributes.json --no-dataset

Let’s say this source has a text field with id 000001. The attributes.json to change its text parsing mode to full field contents would read

{"fields": {"000001": {"term_analysis": {"token_mode": "full_terms_only"}}}}

you can also reference the fields by its column number in this JSON structures. If the field to be modified is in the second column (column index starts at 0) then the contents of the attributes.json file could be as well

{"fields": {"1": {"term_analysis": {"token_mode": "full_terms_only"}}}}

The source-attributes JSON can contain any of the updatable attributes described in the developers section You can specify the fields that you want to include in the dataset by naming them explicitly

bigmler --train data/iris.csv \
        --dataset-fields 'sepal length','sepal width','species'

or the fields that you want to include as predictors in the model

bigmler --train data/iris.csv --model-fields 'sepal length','sepal width'

You can also specify the chosen fields by adding or removing the ones you choose to the list of preferred fields of the previous resource. Just prefix their names with + or - respectively. For example, you could create a model from an existing dataset using all their fields but the sepal length by saying

bigmler --dataset dataset/50a1f441035d0706d9000371 \
        --model-fields -'sepal length'

When evaluating, you can map the fields of the evaluated model to those of the test dataset by writing in a file the field column of the model and the field column of the dataset separated by a comma and using –fields-map flag to specify the name of the file

bigmler --dataset dataset/50a1f441035d0706d9000371 \
        --model model/50a1f43deabcb404d3000079 --evaluate \
        --fields-map fields_map.txt

where fields_map.txt would contain

0, 1
1, 0
2, 2
3, 3
4, 4

if the first two fields had been reversed.

Finally, you can also tell BigML whether your training and test set come with a header row or not. For example, if both come without header

bigmler --train data/iris_nh.csv --test data/test_iris_nh.csv \
        --no-train-header --no-test-header

Splitting Datasets

When following the usual proceedings to evaluate your models you’ll need to separate the available data in two sets: the training set and the test set. With BigMLer you won’t need to create two separate physical files. Instead, you can set a --test-split flag that will set the percentage of data used to build the test set and leave the rest for training. For instance

bigmler --train data/iris.csv --test-split 0.2 --name iris --evaluate

will build a source with your entire file contents, create the corresponding dataset and split it in two: a test dataset with 20% of instances and a training dataset with the remaining 80%. Then, a model will be created based on the training set data and evaluated using the test set. By default, split is deterministic, so that every time you issue the same command will get the same split datasets. If you want to generate different splits from a unique dataset you can set the --seed option to a different string in every call

bigmler --train data/iris.csv --test-split 0.2 --name iris \
        --seed my_random_string_382734627364 --evaluate

Advanced Dataset management

As you can find in the BigML’s API documentation on datasets besides the basic name, label and description that we discussed in previous sections, there are many more configurable options in a dataset resource. As an example, to publish a dataset in the gallery and set its price you could use

{"private": false, "price": 120.4}

Similarly, you might want to add fields to your existing dataset by combining some of its fields or simply tagging their rows. Using BigMLer, you can set the --new-fields option to a file path that contains a JSON structure that describes the fields you want to select or exclude from the original dataset, or the ones you want to combine and the Flatline expression to combine them. This structure must follow the rules of a specific languange described in the Transformations item of the developers section

bigmler --dataset dataset/52b8a12037203f48bc00000a \
        --new-fields my_dir/generators.json

To see a simple example, should you want to include all the fields but the one with id 000001 and add a new one with a label depending on whether the value of the field sepal length is smaller than 1, you would write in generators.json

{"all_but": ["000001"], "new_fields": [{"name": "new_field", "field": "(if (< (f \"sepal length\") 1) \"small\" \"big\")"}]}

Or, as another example, to tag the outliers of the same field one coud use

{"new_fields": [{"name": "outlier?", "field": "(if (within-percentiles? \"sepal length\" 0.5 0.95) \"normal\" \"outlier\")"}]}

You can also export the contents of a generated dataset by using the --to-csv option. Thus,

bigmler --dataset dataset/52b8a12037203f48bc00000a \
        --to-csv my_dataset.csv --no-model

will create a CSV file named my_dataset.csv in the default directory created by BigMLer to place the command output files. If no file name is given, the file will be named after the dataset id.

A dataset can also be generated as the union of several datasets using the flag --multi-dataset. The datasets will be read from a file specified in the --datasets option and the file must contain one dataset id per line.

bigmler --datasets my_datasets --multi-dataset --no-model

This syntax is used when all the datasets in the my_datasets file share a common field structre, so the correspondence of the fields of all the datasets is straight forward. In the general case, the multi-dataset will inherit the field structure of the first component dataset. If you want to build a multi-dataset with datasets whose fields share not the same column disposition, you can specify which fields are correlated to the ones of the first dataset by mapping the fields of the rest of datasets to them. The option --multi-dataset-attributes can point to a JSON file that contains such a map. The command line syntax would then be

bigmler --datasets my_datasets --multi-dataset \
        --multi-dataset-attributes my_fields_map.json \
        --no-model

and for a simple case where the second dataset had flipped the first and second fields with respect to the first one, the file would read

where dataset/53330bce37203f222e00004b would be the id of the second dataset in the multi-dataset.

Model Weights

To deal with imbalanced datasets, BigMLer offers three options: --balance, --weight-field and --objective-weights.

For classification models, the --balance flag will cause all the classes in the dataset to contribute evenly. A weight will be assigned automatically to each instance. This weight is inversely proportional to the number of instances in the class it belongs to, in order to ensure even distribution for the classes.

You can also use a field in the dataset that contains the weight you would like to use for each instance. Using the --weight-field option followed by the field name or column number will cause BigMLer to use its data as instance weight. This is valid for both regression and classification models.

The --objective-weights option is used in classification models to transmit to BigMLer what weight is assigned to each class. The option accepts a path to a CSV file that should contain the class,``weight`` values one per row

bigmler --dataset dataset/52b8a12037203f48bc00000a \
        --objective-weights my_weights.csv

where the my_weights.csv file could read

Iris-setosa,5
Iris-versicolor,3

so that BigMLer would associate a weight of 5 to the Iris-setosa class and 3 to the Iris-versicolor class. For additional classes in the model, like Iris-virginica in the previous example, weight 1 is used as default. All specified weights must be non-negative numbers (with either integer or real values) and at least one of them must be non-zero.

Predictions’ missing strategy

Sometimes the available data lacks some of the features our models use to predict. In these occasions, BigML offers two different ways of handling input data with missing values, that is to say, the missing strategy. When the path to the prediction reaches a split point that checks the value of a field which is missing in your input data, using the last prediction strategy the final prediction will be the prediction for the last node in the path before that point, and using the proportional strategy it will be a weighted average of all the predictions for the final nodes reached considering that both branches of the split are possible.

BigMLer adds the --missing-strategy option, that can be set either to last or proportional to choose the behavior in such cases. Last prediction is the one used when this option is not used.

bigmler --model model/52b8a12037203f48bc00001a \
        --missing-strategy proportional --test my_test.csv

Models with missing splits

Another configuration argument that can change models when the training data has instances with missing values in some of its features is --missing-splits. By setting this flag, the model building algorithm will be able to include the instances that have missing values for the field used to split the data in each node in one of the stemming branches. This will, obviously, affect also the predictions given by the model for input data with missing values. Here’s an example to build a model using missing-splits and predict with it.

bigmler --dataset dataset/52b8a12037203f48bc00023b \
        --missing-splits --test my_test.csv

Fitering Sources

Imagine that you have create a new source and that you want to create a specific dataset filtering the rows of the source that only meet certain criteria. You can do that using a JSON expresion as follows

bigmler --source source/50a2bb64035d0706db0006cc --json-filter filter.json

where filter.json is a file containg a expression like this

["<", 7.00, ["field", "000000"]]

or a LISP expression as follows

bigmler --source source/50a2bb64035d0706db0006cc --lisp-filter filter.lisp

where filter.lisp is a file containing a expression like this

(< 7.00 (field "sepal length"))

For more details, see the BigML’s API documentation on filtering rows.

Multi-labeled categories in training data

Sometimes the information you want to predict is not a single category but a set of complementary categories. In this case, training data is usually presented as a row of features and an objective field that contains the associated set of categories joined by some kind of delimiter. BigMLer can also handle this scenario.

Let’s say you have a simple file

color,year,sex,class
red,2000,male,"Student,Teenager"
green,1990,female,"Student,Adult"
red,1995,female,"Teenager,Adult"

with information about a group of people and we want to predict the class another person will fall into. As you can see, each record has more than one class per person (for example, the first person is labeled as being both a Student and a Teenager) and they are all stored in the class field by concatenating all the applicable labels using , as separator. Each of these labels is, ‘per se’, an objective to be predicted, and that’s what we can rely on BigMLer to do.

The simplest multi-label command in BigMLer is

bigmler --multi-label --train data/tiny_multilabel.csv

First, it will analyze the training file to extract all the labels stored in the objective field. Then, a new extended file will be generated from it by adding a new field per label. Each generated field will contain a boolean set to True if the associated label is in the objective field and False otherwise

color,year,sex,class - Adult,class - Student,class - Teenager
red,2000,male,False,True,True
green,1990,female,True,True,False
red,1995,female,True,False,True

This new file will be fed to BigML to build a source, a dataset and a set of models using four input fields: the first three fields as input features and one of the label fields as objective. Thus, each of the classes that label the training set can be predicted independently using one of the models.

But, naturally, when predicting a multi-labeled field you expect to obtain all the labels that qualify the input features at once, as you provide them in the training data records. That’s also what BigMLer does. The syntax to predict using multi-labeled training data sets is similar to the single labeled case

bigmler --multi-label --train data/tiny_multilabel.csv \
        --test data/tiny_test_multilabel.csv

the main difference being that the ouput file predictions.csv will have the following structure

"Adult,Student","0.34237,0.20654"
"Adult,Teenager","0.34237,0.34237"

where the first column contains the class prediction and the second one the confidences for each label prediction. If the models predict True for more than one label, the prediction is presented as a sequence of labels (and their corresponding confidences) delimited by ,.

As you may have noted, BigMLer uses , both as default training data fields separator and as label separator. You can change this behaviour by using the --training-separator, --label-separator and --test-separator flags to use different one-character separators

bigmler --multi-label --train data/multilabel.tsv \
        --test data/test_multilabel.tsv --training-separator '\t' \
        --test-separator '\t' --label-separator ':'

This command would use the tab character as train and test data field delimiter and : as label delimiter (the examples in the tests set use , as field delimiter and ‘:’ as label separator).

You can also choose to restrict the prediction to a subset of labels using the --labels flag. The flag should be set to a comma-separated list of labels. Setting this flag can also reduce the processing time for the training file, because BigMLer will rely on them to produce the extended version of the training file. Be careful, though, to avoid typos in the labels in this case, or no objective fields will be created. Following the previous example

bigmler --multi-label --train data/multilabel.csv \
        --test data/test_multilabel.csv --label-separator ':' \
        --labels Adult,Student

will limit the predictions to the Adult and Student classes, leaving out the Teenager classification.

Multi-labeled predictions can also be computed using ensembles, one for each label. To create an ensemble prediction, use the --number-of-models option that will set the number of models in each ensemble

bigmler --multi-label --train data/multilabel.csv \
        --number-of-models 20 --label-separator ':' \
        --test data/test_multilabel.csv

The ids of the ensembles will be stored in an ensembles file in the output directory, and can be used in other predictions by setting the --ensembles option

bigmler --multi-label --ensembles multilabel/ensembles \
        --test data/test_multilabel.csv

or you can retrieve all previously tagged ensembles with --ensemble-tag

bigmler --multi-label --ensemble-tag multilabel \
        --test data/test_multilabel.csv

Multi-labeled resources

The resources generated from a multi-labeled training data file can also be recovered and used to generate more multi-labeled predictions. As in the single-labeled case

bigmler --multi-label --source source/522521bf37203f412f000100 \
        --test data/test_multilabel.csv

would generate a dataset and the corresponding set of models needed to create a predictions.csv file that contains the multi-labeled predictions.

Similarly, starting from a previously created multi-labeled dataset

bigmler --multi-label --dataset source/522521bf37203f412fac0135 \
        --test data/test_multilabel.csv --output multilabel/predictions.csv

creates a bunch of models, one per label, and predicts storing the results of each operation in the multilabel directory, and finally

bigmler --multi-label --models multilabel/models \
        --test data/test_multilabel.csv

will retrieve the set of models created in the last example and use them in new predictions. In addition, for these three cases you can restrict the labels to predict to a subset of the complete list available in the original objective field. The --labels option can be set to a comma-separated list of the selected labels in order to do so.

The --model-tag can be used as well to retrieve multi-labeled models and predict with them

bigmler --multi-label --model-tag my_multilabel \
        --test data/test_multilabel.csv

Finally, BigMLer is also able to handle training files with more than one multi-labeled field. Using the --multi-label-fields option you can settle the fields that will be expanded as containing multiple labels in the generated source and dataset.

bigmler --multi-label --multi-label-fields class,type \
        --train data/multilabel_multi.csv --objective class

This command creates a source (and its corresponding dataset) where both the class and type fields have been analysed to create a new field per label. Then the --objective option sets class to be the objective field and only the models needed to predict this field are created. You could also create a new multi-label prediction for another multi-label field, type in this case, by issuing a new BigMLer command that uses the previously generated dataset as starting point

bigmler --multi-label --dataset dataset/52cafddb035d07269000075b \
        --objective type

This would generate the models needed to predict type. It’s important to remark that the models used to predict class in the first example will use the rest of fields (including type as well as the ones generated by expanding it) to build the prediction tree. If you don’t want this fields to be used in the model construction, you can set the --model-fields option to exclude them. For instance, if type has two labels, label1 and label2, then excluding them from the models that predict class could be achieved using

bigmler --multi-label --dataset dataset/52cafddb035d07269000075b \
        --objective class
        --model-fields=' -type,-type - label1,-type - label2'

You can also generate new fields applying aggregation functions such as count, first or last on the labels of the multi label fields. The option --label-aggregates can be set to a comma-separated list of these functions and a new column per multi label field and aggregation function will be added to your source

bigmler --multi-label --train data/multilabel.csv \
        --label-separator ':' --label-aggregates count,last \
        --objective class

will generate class - count and class - last in addition to the set of per label fields.

Multi-label evaluations

Multi-label predictions are computed using a set of binary models (or ensembles), one for each label to predict. Each model can be evaluated to check its performance. In order to do so, you can mimic the commands explained in the evaluations section for the single-label models and ensembles. Starting from a local CSV file

bigmler --multi-label --train data/multilabel.csv \
        --label-separator ":" --evaluate

will build the source, dataset and model objects for you using a random 80% portion of data in your training file. After that, the remaining 20% of the data will be run through each of the models to obtain an evaluation of the corresponding model. BigMLer retrieves all evaluations and saves them locally in json and txt format. They are named using the objective field name and the value of the label that they refer to. Finally, it averages the results obtained in all the evaluations to generate a mean evaluation stored in the evaluation.txt and evaluation.json files. As an example, if your objective field name is class and the labels it contains are Adult,Student, the generated files will be


Generated files:

MonNov0413_201326
  • evaluations
  • extended_multilabel.csv
  • source
  • evaluation_class_student.txt
  • models
  • evaluation_class_adult.json
  • dataset
  • evaluation.json
  • evaluation.txt
  • evaluation_class_student.json
  • bigmler_sessions
  • evaluation_class_adult.txt

You can use the same procedure with a previously existing multi-label source or dataset

bigmler --multi-label --source source/50a1e520eabcb404cd0000d1 \
        --evaluate
bigmler --multi-label --dataset dataset/50a1f441035d0706d9000371 \
        --evaluate

Finally, you can also evaluate a preexisting set of models or ensembles using a separate set of data stored in a file or a previous dataset

bigmler --multi-label --models MonNov0413_201326/models \
        --test data/test_multilabel.csv --evaluate
bigmler --multi-label --ensembles MonNov0413_201328/ensembles \
        --dataset dataset/50a1f441035d0706d9000371 --evaluate

High number of Categories

In BigML there’s a limit in the number of categories of a categorical objective field. This limit is set to ensure the quality of the resulting models. This may become a restriction when dealing with categorical objective fields with a high number of categories. To cope with these cases, BigMLer offers the –max-categories option. Setting to a number lower than the mentioned limit, the existing categories will be organized in subsets of that size. Then the original dataset will be copied many times, one per subset, and its objective field will only keep the categories belonging to each subset plus a generic ***** other ***** category that will summarize the rest of categories. Then a model will be created from each dataset and the test data will be run through them to generate partial predictions. The final prediction will be extracted by choosing the class with highest confidence from the distributions obtained for each model’s prediction ignoring the ***** other ****** generic category. For instance, to use the same iris.csv example, you could do

bigmler --train data/iris.csv --max-categories 1 \
        --test data/test_iris.csv --objective species

This command would generate a source and dataset object, as usual, but then, as the total number of categories is three and –max-categories is set to 1, three more datasets will be created, one per each category. After generating the corresponding models, the test data will be run through them and their predictions combined to obtain the final predictions file. The same procedure would be applied if starting from a preexisting source or dataset using the --source or --dataset options. Please note that the --objective flag is mandatory in this case to ensure that the right categorical field is selected as objective field.

--method option accepts a new combine value to use such kind of combination. You can use it if you need to create a new group of predictions based on the same models produced in the first example. Filling the path to the model ids file

bigmler --models my_dir/models --method combine \
        --test data/new_test.csv

the new predictions will be created. Also, you could use the set of datasets created in the first case as starting point. Their ids are stored in a dataset_parts file that can be found in the output location

bigmler --dataset my_dir/dataset_parts --method combine \
        --test data/test.csv

This command would cause a new set of models, one per dataset, to be generated and their predictions would be combined in a final predictions file.

Advanced subcommands in BigMLer

Analyze subcommand

In addition to the main BigMLer capabilities explained so far, there’s a subcommand bigmler analyze with more options to evaluate the performance of your models. For instance

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --cross-validation --k-folds 5

will create a k-fold cross-validation by dividing the data in your dataset in the number of parts given in --k-folds. Then evaluations are created by selecting one of the parts to be the test set and using the rest of data to build the model for testing. The generated evaluations are placed in your output directory and its average is stored in evaluation.txt and evaluation.json.

Similarly, you’ll be able to create an evaluation for ensembles. Using the same command above and adding the options to define the ensembles’ properties, such as --number-of-models, --sample-rate, --randomize or --replacement

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --cross-validation --k-folds 5 --number-of-models 20
                --sample-rate 0.8 --replacement

More insights can be drawn from the bigmler analyze --features command. In this case, the aim of the command is to analyze the complete set of features in your dataset to single out the ones that produce models with better evaluation scores. In this case, we focus on accuracy for categorical objective fields and r-squared for regressions.

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --features

This command uses an algorithm for smart feature selection as described in this blog post that evaluates models built by using subsets of features. It starts by building one model per feature, chooses the subset of features used in the model that scores best and, from there on, repeats the procedure by adding another of the available features in the dataset to the chosen subset. The iteration stops when no improvement in score is found for a number of repetitions that can be controlled using the --staleness option (default is 5). There’s also a --penalty option (default is 0.1%) that sets the amount that is substracted from the score per feature added to the subset. This penalty is intended to mitigate overfitting, but it also favors models which are quicker to build and evaluate. The evaluations for the scores are k-fold cross-validations. The --k-folds value is set to 5 by default, but you can change it to whatever suits your needs using the --k-folds option.

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --features --k-folds 10 --staleness 3 --penalty 0.002

Would select the best subset of features using 10-fold cross-validation and a 0.2% penalty per feature, stopping after 3 non-improving iterations.

Depending on the machine learning problem you intend to tackle, you might want to optimize other evaluation metric, such as precision or recall. The --optimize option will allow you to set the evaluation metric you’d like to optimize.

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --features --optimize recall

For categorical models, the evaluation values are obtained by counting the positive and negative matches for all the instances in the test set, but sometimes it can be more useful to optimize the performance of the model for a single category. This can be specially important in highly non-balanced datasets or when the cost function is mainly associated to one of the existing classes in the objective field. Using ``–optimize-category” you can set the category whose evaluation metrics you’d like to optimize

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --features --optimize recall \
                --optimize-category Iris-setosa

You should be aware that the smart feature selection command still generates a high number of BigML resources. Using k as the k-folds number and n as the number of explored feature sets, it will be generating k datasets (1/k``th of the instances each), and ``k * n models and evaluations. Setting the --max-parallel-models and --max-parallel-evaluations to higher values (up to k) can help you speed up partially the creation process because resources will be created in parallel. You must keep in mind, though, that this parallelization is limited by the task limit associated to your subscription or account type.

As another optimization method, the bigmler analyze --nodes subcommand will find for you the best performing model by changing the number of nodes in its tree. You provide the --min-nodes and --max-nodes that define the range and --nodes-step controls the increment in each step. The command runs a k-fold evaluation (see --k-folds option) on a model built with each node threshold in you range and tries to optimize the evaluation metric you chose (again, default is accuracy). If improvement stops (see the –staleness option) or the node threshold reaches the --max-nodes limit, the process ends and shows the node threshold that lead to the best score.

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --nodes --min-nodes 10 \
                --max-nodes 200 --nodes-step 50

When working with random forest, you can also change the number of random_candidates or number of fields chosen at random when the models in the forest are built. Using bigmler analyze --random-fields the number of random_candidates will range from 1 to the number of fields in the origin dataset, and BigMLer will cross-validate the random forests to determine which random_candidates number gives the best performance.

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --random-fields

Please note that, in general, the exact choice of fields selected as random candidates might be more important than their actual number. However, in some marginal cases (e.g. datasets with a high number noise features) the number of random candidates can impact tree performance significantly.

For any of these options (--features, --nodes and --random-fields) you can add the --predictions-csv flag to the bigmler analyze command. The results will then include a CSV file that stores the predictions obtained in the evaluations that gave the best score. The file content includes the data in your original dataset tagged by k-fold and the prediction and confidence obtained. This file will be placed in an internal folder of your chosen output directory.

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --features --output-dir my_features --predictions-csv

The output directory for this command is my_features and it will contain all the information about the resources generated when testing the different feature combinations organized in subfolders. The k-fold datasets’ IDs will be stored in an inner test directory. The IDs of the resources created when testing each combination of features will be stored in kfold1, kfold2, etc. folders inside the test directory. If the best-scoring prediction models are the ones in the kfold4 folder, then the predictions CSV file will be stored in a new folder named kfold4_pred.

Report subcommand

The results of a bigmler analyze --features or bigmler analyze --nodes command are a series of k-fold cross-validations made on the training data that leads to the configuration value that will create the best performant model. However, the algorithm maximizes only one evaluation metric. To see the global picture for the rest of metrics at each validation configuration you can build a graphical report of the results using the report subcommand. Let’s say you previously ran

bigmler analyze --dataset dataset/5357eb2637203f1668000004 \
                --nodes --output-dir best_recall

and you want to have a look at the results for each node_threshold configuration. Just say:

bigmler report --from-dir best_recall --port 8080

and the command will traverse the directories in best_recall and summarize the results found there in a metrics comparison graphic and an ROC curve if your model is categorical. Then a simple HTTP server will be started locally and bound to a port of your choice, 8080 in the example (8085 will be the default value), and a new web browser window will be started to show the results. You can see an example built on the well known diabetes dataset.

The HTTP server will create an auxiliary bigmler/reports directory in the user’s home directory, where symbolic links to the reports in each output directory will be stored and served from.

Cluster subcommand

Just as the simple bigmler command can generate all the resources leading to finding models and predictions for a supervised learning problem, the bigmler cluster subcommand will follow the steps to generate clusters and predict the centroids associated to your test data. To mimic what we saw in the bigmler command section, the simplest call is

bigmler cluster --train data/diabetes.csv

This command will upload the data in the data/diabetes.csv file and generate the corresponding source, dataset and cluster objects in BigML. You can use any of the generated objects to produce new clusters. For instance, you could set a subgroup of the fields of the generated dataset to produce a different cluster by using

bigmler cluster --dataset dataset/53b1f71437203f5ac30004ed \
                --cluster-fields="-blood pressure"

that would exclude the field blood pressure from the cluster creation input fields.

Similarly to the models and datasets, the generated clusters can be shared using the --shared option, e.g.

bigmler cluster --source source/53b1f71437203f5ac30004e0 \
                --shared

will generate a secret link for both the created dataset and cluster that can be used to share the resource selectively.

As models were used to generate predictions (class names in classification problems and an estimated number for regressions), clusters can be used to predict the subgroup of data that our input data is more similar to. Each subgroup is represented by its centroid, and the centroid is labelled by a centroid name. Thus, a cluster would classify our test data by assigning to each input an associated centroid name. The command

bigmler cluster --cluster cluster/53b1f71437203f5ac30004f0 \
                --test data/my_test.csv

would produce a file centroids.csv with the centroid name associated to each input. When the command is executed, the cluster information is downloaded to your local computer and the centroid predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the centroid predictions remotely, you can do so too

bigmler cluster --cluster cluster/53b1f71437203f5ac30004f0 \
                --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch centroid also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output.

The k-means algorithm used in clustering can only use training data that has no missing values in their numeric fields. Any data that does not comply with that is discarded in cluster construction, so you should ensure that enough number of rows in your training data file has non-missing values in their numeric fields for the cluster to be built and relevant. Similarly, the cluster cannot issue a centroid prediction for input data that has missing values in its numeric fields, so centroid predictions will give a “-” string as output in this case.

You can change the number of centroids used to group the data in the clustering procedure

bigmler cluster --dataset dataset/53b1f71437203f5ac30004ed \
                --k 3

And also generate the datasets associated to each centroid of a cluster. Using the --cluster-datasets option

bigmler cluster –cluster cluster/53b1f71437203f5ac30004f0
–cluster-datasets “Cluster 1,Cluster 2”

you can generate the datasets associated to a comma-separated list of centroid names. If no centroid name is provided, all datasets are generated.

Similarly, you can generate the models to predict if one instance is associated to each centroid of a cluster. Using the --cluster-models option

bigmler cluster –cluster cluster/53b1f71437203f5ac30004f0
–cluster-models “Cluster 1,Cluster 2”

you can generate the models associated to a comma-separated list of centroid names. If no centroid name is provided, all models are generated. Models can be useful to see which features are important to determine whether a certain instance belongs to a concrete cluster.

Anomaly subcommand

The bigmler anomaly subcommand generates all the resources needed to buid an anomaly detection model and/or predict the anomaly scores associated to your test data. As usual, the simplest call

bigmler anomaly --train data/tiny_kdd.csv

uploads the data in the data/tiny_kdd.csv file and generates the corresponding source, dataset and anomaly objects in BigML. You can use any of the generated objects to produce new anomaly detectors. For instance, you could set a subgroup of the fields of the generated dataset to produce a different anomaly detector by using

bigmler anomaly --dataset dataset/53b1f71437203f5ac30004ed \
                --anomaly-fields="-urgent"

that would exclude the field urgent from the anomaly detector creation input fields. You can also change the number of top anomalies enclosed in the anomaly detector list and the number of trees that the anomaly detector iforest uses. The default values are 10 top anomalies and 128 trees per iforest:

bigmler anomaly --dataset dataset/53b1f71437203f5ac30004ed \
                --top-n 15 --forest-size 50

with this code, the anomaly detector is built using an iforest of 50 trees and will produce a list of the 15 top anomalies.

Similarly to the models and datasets, the generated anomaly detectors can be shared using the --shared option, e.g.

bigmler anomaly --source source/53b1f71437203f5ac30004e0 \
                --shared

will generate a secret link for both the created dataset and anomaly detector that can be used to share the resource selectively.

The anomaly detector can be used to assign an anomaly score to each new input data set. The anomaly score is a number between 0 (not anomalous) and 1 (highest anomaly). The command

bigmler anomaly --anomaly anomaly/53b1f71437203f5ac30005c0 \
                --test data/test_kdd.csv

would produce a file anomaly_scores.csv with the anomaly score associated to each input. When the command is executed, the anomaly detector information is downloaded to your local computer and the anomaly score predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the anomaly score predictions remotely, you can do so too

bigmler anomaly --anomaly anomaly/53b1f71437203f5ac30005c0 \
                --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch anomaly score also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output.

Similarly, you can split your data in train/test datasets to build the anomaly detector and create batch anomaly scores with the test portion of data

bigmler anomaly --train data/tiny_kdd.csv --test-split 0.2 --remote

or if you want to apply the anomaly detector on the same training data set to create a batch anomaly score, use:

bigmler anomaly --train data/tiny_kdd.csv --score --remote

To extract the top anomalies as a new dataset, or to exclude from the training dataset the top anomalies in the anomaly detector, set the

--anomalies-dataset to ìn or out respectively:
bigmler anomaly --dataset dataset/53b1f71437203f5ac30004ed \
                --anomalies-dataset out

will create a new dataset excluding the top anomalous instances according to the anomaly detector.

Sample subcommand

You can extract samples from your datasets in BigML using the bigmler sample subcommand. When a new sample is requested, a copy of the dataset is stored in a special format in an in-memory cache. This sample can then be used, before its expiration time, to extract data from the related dataset by setting some options like the number of rows or the fields to be retrieved. You can either begin from scratch uploading your data to BigML, creating the corresponding source and dataset and extracting your sample from it

bigmler sample --train data/iris.csv --rows 10 --row-offset 20

This command will create a source, a dataset, a sample object, whose id will be stored in the samples file in the output directory, and extract 10 rows of data starting from the 21st that will be stored in the sample.csv file.

You can reuse an existing sample by using its id in the command.

bigmler sample --sample sample/53b1f71437203f5ac303d5c0 \
               --sample-header --row-order-by="-petal length" \
               --row-fields "petal length,petal width" --mode linear

will create a new sample.csv file with a headers row where only the petal length and petal width are retrieved. The --mode linear option will cause the first available rows to be returned and the --row-order-by="-petal length" option returns these rows sorted in descending order according to the contents of petal length.

You can also add to the sample rows some statistical information by using the --stat-field or --stat-fields options. Adding them to the command will generate a stat-info.json file where the Pearson’s and Spearman’s correlations, and linear regression terms will be stored in a JSON format.

You can also apply a filter to select the sample rows by the values in their fields using the --fields-filter option. This must be set to a string containing the conditions that must be met using field ids and values.

bigmler sample --sample sample/53b1f71437203f5ac303d5c0 \
               --fields-filter "000001=&!000004=Iris-setosa"

With this command, only rows where field id 000001 is missing and field id 000004 is not Iris-setosa will be retrieved. You can check the available operators and syntax in the samples’ developers doc . More available options can be found in the Samples subcommand Options section.

Reify subcommand

This subcommand extracts the information in the existing resources to determine the arguments that were used when they were created, and generates scripts that could be used to reproduce them. Currently, the language used in the scripts will be Python. The usual starting point for BigML resources is a source created from inline, local or remote data. Thus, the script keeps analyzing the chain of calls that led to a certain resource until the root source is found.

The simplest example would be:

bigmler reify --id source/55d77ba60d052e23430027bb

that will output:

"""Python code to reify source/55d77ba60d052e23430027bb

"""

from bigml.api import BigML
api = BigML()

source1 = api.create_source("iris.csv", {"name": "my source"})
api.ok(source1)

According to this output, the source was created from a file named iris.csv and was assigned a name. This script will be stored in the command output directory and named reify.py` (you can specify a different name and location using the --output option).

When creating sources from data, field types are inferred from the contents of the first lines in the uploaded file. Sometimes, these field types must be adapted and the source fields attributes are updated. You can also change other fields attributes, like their name, label or description. In order to make sure that the right fields information is reproduced, add the --add-fields flag:

bigmler reify --id source/55d77ba60d052e23430027bb --add-fields \
              --output my_dir/reify_source.py
"""Python code to reify source/55d77ba60d052e23430027bb

"""

from bigml.api import BigML
api = BigML()

source1 = api.create_source("iris.csv")
api.ok(source1)

source1 = api.update_source(source1, \
    {'fields': {u'000004': {'optype': u'categorical', 'name': u'species'},
                u'000002': {'optype': u'numeric', 'name': u'petal length'},
                u'000003': {'optype': u'numeric', 'name': u'petal width'},
                u'000000': {'optype': u'numeric', 'name': u'sepal length'},
                u'000001': {'optype': u'numeric', 'name': u'sepal width'}}
    }
)
api.ok(source1)

Other resources will have more complex workflows and more user-given attributes. Let’s see for instance the script to generate an evaluation from a train/test split of a source that was created using the bigmler --train data/iris.csv --evaluate command:

bigmler reify --id evaluation/55d919850d052e234b000833
"""Python code to reify evaluation/55d919850d052e234b000833

"""

from bigml.api import BigML
api = BigML()

source1 = api.create_source("iris.csv", {'category': 12,
    'description': u'Created using BigMLer',
    'name': u'BigMLer_SunAug2315_025314',
    'tags': [u'BigMLer', u'BigMLer_SunAug2315_025314']})
api.ok(source1)

dataset1 = api.create_dataset(source1,
    {'name': u'BigMLer_SunAug2315_025314',
     'tags': [u'BigMLer', u'BigMLer_SunAug2315_025314']})
api.ok(dataset1)

model1 = api.create_model(dataset1,
    {'seed': u'BigML, Machine Learning made easy',
     'sample_rate': 0.8, 'name': u'BigMLer_SunAug2315_025314'})
api.ok(model1)

evaluation1 = api.create_evaluation(model1, dataset1,
    {'seed': u'BigML, Machine Learning made easy', 'sample_rate': 0.8,
     'out_of_bag': True, 'name': u'BigMLer_SunAug2315_025314'})
api.ok(evaluation1)

As you can see, BigMLer has added a default category, name, description, tags, has built the model on 80% of the data and used the out_of_bag attribute for the evaluation to use the remaining part of the dataset test data.

Execute subcommand

This subcommand creates and executes scripts in WhizzML (BigML’s automation language). With WhizzML you can program any specific workflow that involves Machine Learning resources like datasets, models, etc. You just write a script using the directives in the reference manual and upload it to BigML, where it will be available as one more resource in your dashboard. Scripts can also be shared and published in the gallery, so you can reuse other users’ scripts and execute them. These operations can also be done using the bigmler execute subcommand.

The simplest example is executing some basic code, like adding two numbers:

bigmler execute --code "(+ 1 2)" --output-dir simple_exe

With this command, bigmler will generate a script in BigML whose source code is the one given as a string in the --code option. The script ID will be stored in a file called scripts in the simple_text directory. After that, the script will be executed, so a new resource called execution will be created in BigML, and the corresponding ID will be stored in the execution file of the output directory. Similarly, the result of the execution will be stored in whizzml_results.txt and whizzml_results.json (in human-readable format and JSON respectively) in the directory set in the --output-dir option. You can also use the code stored in a file with the --code-file option.

Adding the --no-execute flag to the command will cause the process to stop right after the script creation. You can also compile your code as a library to be used in many scripts by setting the --to-library flag.

bigmler execute --code-file my_library.whizzml --to-library

Existing scripts can be referenced for execution with the --script option

bigmler execute --script script/50a2bb64035d0706db000643

or the script ID can be read from a file:

bigmler execute --scripts simple_exe/scripts

The script we used as an example is very simple and needs no additional parameter. But, in general, scripts will have input parameters and output variables. The inputs define the script signature and must be declared in order to create the script. The outputs are optional and any variable in the script can be declared to be an output. Both inputs and outputs can be declared using the --declare-inputs and --declare-outputs options. These options must contain the path to the JSON file where the information about the inputs and outputs (respectively) is stored.

bigmler execute --code '(define addition (+ a b))' \
                --declare-inputs my_inputs_dec.json \
                --declare-outputs my_outputs_dec.json \
                --no-execute

in this example, the my_inputs_dec.json file could contain

[{"name": "a",
  "default": 0,
  "type": "number"},
 {"name": "b",
  "default": 0,
  "type": "number",
  "description": "second number to add"}]

and my_outputs_dec.json

[{"name": "addition",
  "type": "number"}]

so that the value of the addition variable would be returned as output in the execution results.

Additionally, a script can import libraries. The list of libraries to be used as imports can be added to the command with the option --imports followed by a comma-separated list of library IDs.

Once the script has been created and its inputs and outputs declared, to execute it you’ll need to provide a value for each input. This can be done using --inputs, that will also point to a JSON file where each input should have its corresponding value.

bigmler execute --script script/50a2bb64035d0706db000643 \
                --inputs my_inputs.json

where the my_inputs.json file would contain:

[["a", 1],
 ["b", 2]]

For more details about the syntax to declare inputs and outputs, please refer to the Developers documentation.

You can also provide default configuration attributes for the resources generated in an execution. Add the --creation-defaults option followed by the path to a JSON file that contains a dictionary whose keys are the resource types to which the configuration defaults apply and whose values are the configuration attributes set by default.

bigmler execute --code-file my_script.whizzml \
                --creation-defaults defaults.json

For instance, if my_script.whizzml creates an ensemble from a remote file:

(define file "s3://bigml-public/csv/iris.csv")
(define source (create-and-wait-source {"remote" file}))
(define dataset (create-and wait-dataset {"source" source}))
(define ensemble (create-and-wait-ensemble {"dataset" dataset}))

and my_create_defaults.json contains

{
    "source": {
    "project": "project/54d9553bf0a5ea5fc0000016"
    },
    "ensemble": {
    "number_of_models": 100, "sample_rate": 0.9
    }
}

the source created by the script will be associated to the given project and the ensemble will have 100 models and a 0.9 sample rate unless the source code in your script explicitly specifies a different value, in which case it takes precedence over these defaults.

Whizzml subcommand

This subcommand creates packages of scripts and libraries in WhizzML (BigML’s automation language) based on the information provided by a metadata.json file. These operations can also be performed individually using the bigmler execute subcommand, but bigmler whizzml reads the components of the package, and for each component analyzes the corresponding metadata.json file to identify the kind of code (script or library) that it contains and creates the corresponding resource in BigML. The metadata.json is expected to contain the name, kind, description, inputs and outputs needed to create the script. As an example,

{
  "name": "Example of whizzml script",
  "description": "Test example of a whizzml script that adds two numbers",
  "kind": "script",
  "source_code": "code.whizzml",
  "inputs": [
      {
          "name": "a",
          "type": "number",
          "description": "First number"
      },
      {
          "name": "b",
          "type": "number",
          "description": "Second number"
      }
  ],
  "outputs": [
      {
          "name": "addition",
          "type": "number",
          "description": "Sum of the numbers"
      }
  ]
}

describes a script whose code is to be found in the code.whizzml file. The script will have two inputs a and b and one output: addition.

In order to create this script, you can type the following command:

bigmler whizzml --package-dir my_package --output-dir creation_log

and bigmler will:

  • look for the metadata.json file located in the my_package directory.
  • parse the JSON, identify that it defines a script and look for its code in the code.whizzml file
  • create the corresponding BigML script resource, adding as arguments the ones provided in inputs, outputs, name and description.

Packages can contain more than one script. In this case, a nested directory structure is expected. The metadata.json file for a package with many components should include the name of the directories where these components can be found:

{
  "name": "Best k",
  "description": "Library and scripts implementing Pham-Dimov-Nguyen k selection algorithm",
  "kind": "package",
  "components":[
    "best-k-means",
    "cluster",
    "evaluation",
    "batchcentroid"
  ]
}

In this example, each string in the components attributes list corresponds to one directory where a new script or library (with its corresponding metadata.json descriptor) is stored. Then, using bigmler whizzml for this composite package will create each of the component scripts or libraries. It will also handle dependencies, using the IDs of the created libraries as imports for the scripts when needed.

Retrain subcommand

This subcommand can be used to retrain an existing modeling resource (model, ensemble, deepnet, etc.) by adding new data to it. In BigML, resources are immutable to ensure traceability, but at the same time they are reproducible. Therefore, any model can be rebuilt using the data stored in a new consolidated dataset or even from a list of existing datasets. That’s retraining the model and the bigmler retrain subcommand provides a simple way to do it.

In the basic use case, different parameters and model types are tried and evaluated till the best performing model is found. Then you can call:

bigmler retrain --id model/5a3ae0f14006833a070003a4 --add data/iris.csv \
                --output-dir retrain_directory

so that the data in your local data/iris.csv file is uploaded to the platform and all the steps that led to your existing model are reproduced to create a new merged dataset that will be used to retrain your model. The command output will contain the URL that you need to call to ensure you always use the latest version of your model. The URL will look like:


https://bigml.io/andromeda/model?username=my_user;api_key=my_api_key;limit=1;full=yes;tags=retrain:model/5a3ae0f14006833a070003a4

Instead of using the original model ID, you can choose to add a unique tag to your modeling resource and use that as reference:

bigmler retrain --ensemble-tag my_ensemble --add data/iris.csv \
                --output-dir retrain_directory

in this case, the resource to retrain is an ensemble that has been previously tagged as my_ensemble. The bigmler retrain command will look for the newest ensemble that contains that tag and after uploading and consolidating your data with the one previously used in the ensemble, it will rebuild it. The reference used in the URL that will contain the latest version of the ensemble will use this tag also as reference:


https://bigml.io/andromeda/ensemble?username=my_user;api_key=my_api_key;limit=1;full=yes;tags=my_ensemble

In a different scenario, you might want to retrain your model from a list of datasets, for instance training an anomaly detector using the data of the last 6 months. This means that you don’t want your data to be merged. Rather you would like to use a window over the list of available datasets.

bigmler retrain --ensemble-tag my_ensemble --add data/iris.csv \
                --window-size 6 --output-dir retrain_directory

In this case, adding the --window-size option to your command will cause the dataset created by uploading your new data to be added to the list of datasets as a separate resource. Then model will be rebuilt using the number of datasets set as --window-size.

The operations run by bigmler retrain are mainly run in BigML’s servers using WhizzML scripts. This scripts are previously created in the user’s account the first time you run the command, but they can also be recreated by using the --upgrade flag in any bigmler retrain command call.

Delete subcommand

You have seen that BigMLer is an agile tool that empowers you to create a great number of resources easily. This is a tremedous help, but it also can lead to a garbage-prone environment. To keep a control of each new created remote resource use the flag –resources-log followed by the name of the log file you choose.

bigmler --train data/iris.csv --resources-log my_log.log

Each new resource created by that command will cause its id to be appended as a new line of the log file.

BigMLer can help you as well in deleting these resources. Using the delete subcommand there are many options available. For instance, deleting a comma-separated list of ids

bigmler delete \
        --ids source/50a2bb64035d0706db0006cc,dataset/50a1f441035d0706d9000371

deleting resources listed in a file

bigmler delete --from-file to_delete.log

where to_delete.log contains a resource id per line.

As we’ve previously seen, each BigMLer command execution generates a bunch of remote resources whose ids are stored in files located in a directory that can be set using the --output-dir option. The bigmler delete subcommand can retrieve the ids stored in such files by using the --from-dir option.

bigmler --train data/iris.csv --output my_BigMLer_output_dir
bigmler delete --from-dir my_BigMLer_output_dir

The last command will delete all the remote resources previously generated by the fist command by retrieving their ids from the files in my_BigMLer_output_dir directory.

You can also delete resources based on the tags they are associated to

bigmler delete --all-tag my_tag

or restricting the operation to a specific type

bigmler delete --source-tag my_tag
bigmler delete --dataset-tag my_tag
bigmler delete --model-tag my_tag
bigmler delete --prediction-tag my_tag
bigmler delete --evaluation-tag my_tag
bigmler delete --ensemble-tag my_tag
bigmler delete --batch-prediction-tag my_tag
bigmler delete --cluster-tag my_tag
bigmler delete --centroid-tag my_tag
bigmler delete --batch-centroid-tag my_tag
bigmler delete --anomaly-tag my_tag
bigmler delete --anomaly-score-tag my_tag
bigmler delete --batch-anomaly-score-tag my_tag
bigmler delete --project-tag my_tag
bigmler delete --logistic-regression-tag my_tag
bigmler delete --time-series-tag my_tag
bigmler delete --deepnet-tag my_tag
bigmler delete --topic-model-tag my_tag
bigmler delete --topic-distribution-tag my_tag
bigmler delete --association-tag my_tag

You can also delete resources by date. The options --newer-than and --older-than let you specify a reference date. Resources created after and before that date respectively, will be deleted. Both options can be combined to set a range of dates. The allowed values are:

  • dates in a YYYY-MM-DD format
  • integers, that will be interpreted as number of days before now
  • resource id, the creation datetime of the resource will be used

Thus,

bigmler delete --newer-than 2

will delete all resources created less than two days ago (now being 2014-03-23 14:00:00.00000, its creation time will be greater than 2014-03-21 14:00:00.00000).

bigmler delete --older-than 2014-03-20 --newer-than 2014-03-19

will delete all resources created during 2014, March the 19th (creation time between 2014-03-19 00:00:00 and 2014-03-20 00:00:00) and

bigmler delete --newer-than source/532db2b637203f3f1a000104

will delete all resources created after the source/532db2b637203f3f1a000104 was created.

You can also combine both types of options, to delete sources tagged as my_tag starting from a certain date on

bigmler delete --newer-than 2 --source-tag my_tag

And finally, you can filter the type of resource to be deleted using the --resource-types option to specify a comma-separated list of resource types to be deleted

bigmler delete --older-than 2 --resource-types source,model

will delete the sources and models created more than two days ago.

You can simulate the a delete subcommand using the --dry-run flag

bigmler delete --newer-than source/532db2b637203f3f1a000104 \
               --source-tag my_source --dry-run

The output for the command will be a list of resources that would be deleted if the --dry-run flag was removed. In this case, they will be sources that contain the tag my_source and were created after the one given as --newer-than value. The first 15 resources will be logged to console, and the complete list can be found in the bigmler_sessions file.

By default, only finished resources are selected to be deleted. If you want to delete other resources, you can select them by choosing their status:

bigmler delete --older-than 2 --status failed

would remove all failed resources created more than two days ago.

Export subcommand

The bigmler export subcommand is intended to help generating the code needed for the models in BigML to be integrated in other applications. To produce a prediction using a BigML model you just need a function that receives as argument the new test case data and returns this prediction (and a confidence). The bigmler export subcommand will retrieve the JSON information of your existing decision tree model in BigML and will generate from it this function code and store it in a file that can be imported or copied directly in your application.

Obviously, the function syntax will depend on the model and the language used in your application, so these will be the options we need to provide:

bigmler export --model model/532db2b637203f3f1a001304 \
               --language javascript --output-dir my_exports

This command will create a javascript version of the function that produces the predictions and store it in a file named model_532db2b637203f3f1a001304.js (after the model ID) in the my_exports directory.

Models can currently exported in Python, Javascript and R. For models whose fields are numeric or categorical, the command also supports creating MySQL functions and Tableau separate expressions for both the prediction and the confidence.

You can also generate the code for all the models in an ensemble in a single bigmler export command using the –ensemble option followed by the corresponding ensemble ID. The code for each model will be stored in a separate file, named after the model ID and transforming the slash into an underscore.

bigmler export --ensemble ensemble/532db2b637203f3f1a001307 \
               --language javascript --output-dir my_ensemble

Project subcommand

Projects are organizational resources and they are usually created at source-creation time in order to keep together in a separate repo all the resources derived from a source. However, you can also create a project or update its properties independently using the bigmler project subcommand.

bigmler project --name my_project

will create a new project and name it. You can also add other attributes such as --tag, --description or --category in the project creation call. You can also add or update any other attribute to the project using a JSON file with the --project-attributes option.

bigmler project --project-id project/532db2b637203f3f1a000153 \
                --project-attributes my_attributes.json

Association subcommand

Association Discovery is a popular method to find out relations among values in high-dimensional datasets.

A common case where association discovery is often used is market basket analysis. This analysis seeks for customer shopping patterns across large transactional datasets. For instance, do customers who buy hamburgers and ketchup also consume bread?

Businesses use those insights to make decisions on promotions and product placements. Association Discovery can also be used for other purposes such as early incident detection, web usage analysis, or software intrusion detection.

In BigML, the Association resource object can be built from any dataset, and its results are a list of association rules between the items in the dataset. In the example case, the corresponding association rule would have hamburguers and ketchup as the items at the left hand side of the association rule and bread would be the item at the right hand side. Both sides in this association rule are related, in the sense that observing the items in the left hand side implies observing the items in the right hand side. There are some metrics to ponder the quality of these association rules:

  • Support: the proportion of instances which contain an itemset.

For an association rule, it means the number of instances in the dataset which contain the rule’s antecedent and rule’s consequent together over the total number of instances (N) in the dataset.

It gives a measure of the importance of the rule. Association rules have to satisfy a minimum support constraint (i.e., min_support).

  • Coverage: the support of the antedecent of an association rule.

It measures how often a rule can be applied.

  • Confidence or (strength): The probability of seeing the rule’s consequent

under the condition that the instances also contain the rule’s antecedent. Confidence is computed using the support of the association rule over the coverage. That is, the percentage of instances which contain the consequent and antecedent together over the number of instances which only contain the antecedent.

Confidence is directed and gives different values for the association rules Antecedent → Consequent and Consequent → Antecedent. Association rules also need to satisfy a minimum confidence constraint (i.e., min_confidence).

  • Leverage: the difference of the support of the association

rule (i.e., the antecedent and consequent appearing together) and what would be expected if antecedent and consequent where statistically independent. This is a value between -1 and 1. A positive value suggests a positive relationship and a negative value suggests a negative relationship. 0 indicates independence.

Lift: how many times more often antecedent and consequent occur together than expected if they where statistically independent. A value of 1 suggests that there is no relationship between the antecedent and the consequent. Higher values suggest stronger positive relationships. Lower values suggest stronger negative relationships (the presence of the antecedent reduces the likelihood of the consequent)

As to the items used in association rules, each type of field is parsed to extract items for the rules as follows:

  • Categorical: each different value (class) will be considered a separate item.
  • Text: each unique term will be considered a separate item.
  • Items: each different item in the items summary will be considered.
  • Numeric: Values will be converted into categorical by making a

segmentation of the values. For example, a numeric field with values ranging from 0 to 600 split into 3 segments: segment 1 → [0, 200), segment 2 → [200, 400), segment 3 → [400, 600]. You can refine the behavior of the transformation using discretization and field_discretizations.

The bigmler association subcommand will discover the association rules present in your datasets. Starting from the raw data in your files:

bigmler association --train my_file.csv

will generate the source, dataset and association objects required to present the association rules hidden in your data. You can also limit the number of rules extracted using the --max-k option

bigmler association --dataset dataset/532db2b637203f3f1a000103 \
                    --max-k 20

With the prior command only 20 association rules will be extracted. Similarly, you can change the search strategy used to find them

bigmler association --dataset dataset/532db2b637203f3f1a000103 \
                    --search-strategy confidence

In this case, the confidence is used (the default value being leverage).

Logistic-regression subcommand

The bigmler logistic-regression subcommand generates all the resources needed to buid a logistic regression model and use it to predict. The logistic regression model is a supervised learning method for solving classification problems. It predicts the objective field class as logistic function whose argument is a linear combination of the rest of features. The simplest call to build a logistic regression is

bigmler logistic-regression --train data/iris.csv

uploads the data in the data/iris.csv file and generates the corresponding source, dataset and logistic regression objects in BigML. You can use any of the generated objects to produce new logistic regressions. For instance, you could set a subgroup of the fields of the generated dataset to produce a different logistic regression model by using

bigmler logistic-regression --dataset dataset/53b1f71437203f5ac30004ed \
                --logistic-fields="-sepal length"

that would exclude the field sepal length from the logistic regression model creation input fields. You can also change some parameters in the logistic regression model, like the bias (scale of the intercept term), c (the strength of the regularization map) or eps (stopping criteria for solver).

bigmler logistic-regression --dataset dataset/53b1f71437203f5ac30004ed \
                            --bias 1 --c 5 --eps 0.5

with this code, the logistic regression is built using an independent term of 1, the step in the regularization is 5 and the difference between the results from the current and last iterations is 0.5.

Similarly to the models and datasets, the generated logistic regressions can be shared using the --shared option, e.g.

bigmler logistic-regression --source source/53b1f71437203f5ac30004e0 \
                            --shared

will generate a secret link for both the created dataset and logistic regressions, that can be used to share the resource selectively.

The logistic regression can be used to assign a prediction to each new input data set. The command

bigmler logistic-regression \
        --logistic-regression logisticregression/53b1f71435203f5ac30005c0 \
        --test data/test_iris.csv

would produce a file predictions.csv with the predictions associated to each input. When the command is executed, the logistic regression information is downloaded to your local computer and the logistic regression predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the predictions remotely, you can do so too

bigmler logistic-regression
        --logistic-regression logisticregression/53b1f71435203f5ac30005c0 \
        --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch prediction also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output.

Topic Model subcommand

Using this subcommand you can generate all the resources leading to finding a topic model and its topic distributions. These are unsupervised learning models which find out the topics in a collection of documents and will then be useful to classify new documents according to the topics. The bigmler topic-model subcommand will follow the steps to generate topic models and predict the topic distribution, or distribution of probabilities for the new document to be associated to a certain topic. As shown in the bigmler command section, the simplest call is

bigmler topic-model --train data/spam.csv

This command will upload the data in the data/spam.csv file and generate the corresponding source, dataset and topic model objects in BigML. You can use any of the intermediate generated objects to produce new topic models. For instance, you could set a subgroup of the fields of the generated dataset to produce a different topic model by using

bigmler topic-model --dataset dataset/53b1f71437203f5ac30004ed \
                    --topic-fields="-Message"

that would exclude the field Message from the topic model creation input fields.

Similarly to the models and datasets, the generated topic models can be shared using the --shared option, e.g.

bigmler topic-model --source source/53b1f71437203f5ac30004e0 \
                    --shared

will generate a secret link for both the created dataset and topic model that can be used to share the resource selectively.

As models were used to generate predictions (class names in classification problems and an estimated number for regressions), topic models can be used to classify a new document in the discovered list of topics. The classification is run by computing the probability for the document to belonging to the topic group. The command

bigmler topic-model --topic-model topicmodel/58437a277e0a8d38ec028a5f \
                    --test data/my_test.csv

would produce a file topic_distributions.csv where each row will contain the probabilities associated to each topic for the corresponding test input. When the command is executed, the topic model information is downloaded to your local computer and the distributions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the topic distributions remotely, you can do so too

bigmler topic-model --topic-model topicmodel/58437a277e0a8d38ec028a5f \
                    --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch topic distribution also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output.

Time Series subcommand

Using this subcommand you can generate all the resources leading to a time series and its forecasts. The time series is a supervised learning model that works on an ordered sequence of data to extract the patterns needed to make forecasts. The bigmler time-series subcommand will follow the steps to generate time series and predict the forecasts for every numeric field in the original dataset that has been set as objective field. As shown in the bigmler command section, the simplest call is

bigmler time-series --train data/grades.csv

This command will upload the data in the data/grades.csv file and generate the corresponding source, dataset and time series objects in BigML. You can use any of the intermediate generated objects to produce new time series. For instance, you could set a subgroup of the numeric fields in the dataset to be used as objective fields using the --objectives option.

bigmler time-series --dataset dataset/53b1f71437203f5ac30004ed \
                    --objectives "Assignment,Final"

its value is expected to be a comma-separated list of fields.

Similarly to the models and datasets, the generated clusters can be shared using the --shared option, e.g.

bigmler time-series --source source/53b1f71437203f5ac30004e0 \
                    --shared

will generate a secret link for both the created dataset and time series that can be used to share the resource selectively.

As models were used to generate predictions (class names in classification problems and an estimated number for regressions), time series can be used to generate forecasts, that is, to predict the value of each objective field up till the user-given horizon. The command

bigmler time-series --time-series timeseries/58437a277e0a8d38ec028a5f \
                    --horizon 10

would produce a file forecast_000001.csv with ten rows, one per point, and as many columns as ETS models the time series contains.

When the command is executed, the time series information is downloaded to your local computer and the forecasts are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the forecasts remotely, you can do so too

bigmler time-series --time-series timeseries/58437a277e0a8d38ec028a5f \
                    --horizon 10 --remote

would create a remote forecast with the specified horizon. You can also specify more complex inputs for the forecast. For instance, you can set a different horizon to each objective field and you can give some criteria to select the models used in the forecast. All of this can be done using the --test option pointing to a JSON file that should contain the input to be used in the forecast as described in the API documentation. As an example, let’s set a horizon of 5 points for the Final field and select the first model in the time series array of ETS models, and also forecast 7 points for the Assignment field using the model with less aic (the one used by default). The command call should then be:

bigmler time-series --time-series timeseries/58437a277e0a8d38ec028a5f \
                    --test test.json

and the test.json file should contain the following JSON:

{"Final": {"horizon": 5, "ets_models": {"indices": [0]}},
 "Assignment": {"horizon": 7}}

Deepnet subcommand

The bigmler deepnet subcommand generates all the resources needed to buid a deepnet model and use it to predict. The deepnet model is a supervised learning method for solving both regression and classification problems. It uses deep neural networks, a composition of layers of different functions that when applied to the input data generate the prediction.

The simplest call to build a deepnet is:

bigmler deepnet --train data/iris.csv

uploads the data in the data/iris.csv file and generates the corresponding source, dataset and deepnet objects in BigML. You can use any of the generated objects to produce new deepnets. For instance, you could set a subgroup of the fields of the generated dataset to produce a different deepnet model by using

bigmler deepnet --dataset dataset/53b1f71437203f5ac30004ed \
                --deepnet-fields="-sepal length"

that would exclude the field sepal length from the deepnet model creation input fields. You can also change some parameters in the deepnet model, like the number_of_hidden_layers, max_iterations or default_numeric_value. Please check the `Deepnets section of the API documentation <https://bigml.com/api/deepnets`_ for a detailed description of the available arguments.

bigmler deepnet --dataset dataset/53b1f71437203f5ac30004ed \
                --number-of-hidden-layers 3
                --max-iterations 10 --default-numeric-value mean

with this code, the deepnet is built using 3 hidden layers, approximations will stop after 10 iterations and the missing numerics will be filled with the mean of the rest of values in the field.

Similarly to the models and datasets, the generated deepnets can be shared using the --shared option, e.g.

bigmler deepnet --source source/53b1f71437203f5ac30004e0 \
                --shared

will generate a secret link for both the created dataset and deepnet, that can be used to share the resource selectively.

The deepnet can be used to assign a prediction to each new input data set. The command

bigmler deepnet \
        --deepnet deepnet/5331f71435203f5ac30005c0 \
        --test data/test_iris.csv

would produce a file predictions.csv with the predictions associated to each input. When the command is executed, the deepnet information is downloaded to your local computer and the deepnet predictions are computed locally, with no more latencies involved. Just in case you prefer to use BigML to compute the predictions remotely, you can do so too

bigmler deepnet
        --deepnet deepnet/53b1f71435203f5ac30005c0 \
        --test data/my_test.csv --remote

would create a remote source and dataset from the test file data, generate a batch prediction also remotely and finally download the result to your computer. If you prefer the result not to be dowloaded but to be stored as a new dataset remotely, add --no-csv and to-dataset to the command line. This can be specially helpful when dealing with a high number of scores or when adding to the final result the original dataset fields with --prediction-info full, that may result in a large CSV to be created as output.

Additional Features

Using local models to predict

Most of the previously described commands need the remote resources to be downloaded to work. For instance, when you want to create a new model from an existing dataset, BigMLer is going to download the dataset JSON structure to extract the fields and objective field information, and only then ask for the model creation. As mentioned, the --store flag forces BigMLer to store the downloaded JSON structures in local files inside your output directory. If you use that flag when building a model with BigMLer, then the model is stored in your computer. This model file contains all the information you need in order to make new predictions, so you can use the --model-file option to set the path to this file and predict the value of your objective field for new input data with no reference at all to your remote resources. You could even delete the original remote model and work exclusively with the locally downloaded file

bigmler --model-file my_dir/model_532db2b637203f3f1a000136 \
        --test data/test_iris.csv

The same is available for clusters

bigmler cluster --cluster-file my_dir/cluster_532db2b637203f3f1a000348 \
                --test data/test_diabetes.csv

anomaly detectors

bigmler anomaly --anomaly-file my_dir/anomaly_532db2b637203f3f1a00053a \
                --test data/test_kdd.csv

logistic regressions

bigmler logistic-regression --logistic-file my_dir/logisticregression_532db2b637203f3f1a00053a \
                --test data/test_diabetes.csv

topic models

bigmler topic-model --topic-model-file my_dir/topicmodel_532db2b637203f3f1a00053a \
                    --test data/test_spam.csv

time series

bigmler time-series --time-series-file my_dir/timeseries_532db2b637203f5f1a00053a \
                    --horizon 20

deepnets

bigmler deepnets --deepnet-file my_dir/deepnet_532db2b637203f5f1a00053a \
                 --test data/test_diabetes.csv

Even for ensembles

bigmler --ensemble-file my_dir/ensemble_532db2b637203f3f1a00053b \
        --test data/test_iris.csv

In this case, the models included in the ensemble are expected to be stored also in the same directory where the local file for the ensemble is. They are downloaded otherwise.

Resuming Previous Commands

Network connections failures or other external causes can break the BigMLer command process. To resume a command ended by an unexpected event you can issue

bigmler --resume

BigMLer keeps track of each command you issue in a .bigmler file and of the output directory in .bigmler_dir_stack of your working directory. Then --resume will recover the last issued command and try to continue work from the point it was stopped. There’s also a --stack-level flag

bigmler --resume --stack-level 1

to allow resuming a previous command in the stack. In the example, the one before the last.

Building reports

The resources generated in the execution of a BigMLer command are listed in the standard output by default, but they can be summarized as well in a Gazibit format. Gazibit is a platform where you can create interactive presentations in a flexible and dynamic way. Using BigMLer’s --reports gazibit option you’ll be able to generate a Gazibit summary report of your newly created resources. In case you use also the --shared flag, a second template will be generated where the links for the shared resources will be used. Both reports will be stored in the reports subdirectory of your output directory, where all of the files generated by the BigMLer command are. Thus,

bigmler --train data/iris.csv --reports gazibit --shared \
        --output-dir my_dir

will generate two files: gazibit.json and gazibit_shared.json in a reports subdirectory of your my_dir directory. In case you provide your Gazibit token in the GAZIBIT_TOKEN environment variable, they will also be uploaded to your account in Gazibit. Upload can be avoided, by using the --no-upload flag.

User Chosen Defaults

BigMLer will look for bigmler.ini file in the working directory where users can personalize the default values they like for the most relevant flags. The options should be written in a config style, e.g.

[BigMLer]
dev = true
resources_log = ./my_log.log

as you can see, under a [BigMLer] section the file should contain one line per option. Dashes in flags are transformed to undescores in options. The example would keep development mode on and would log all created resources to my_log.log for any new bigmler command issued under the same working directory if none of the related flags are set.

Naturally, the default value options given in this file will be overriden by the corresponding flag value in the present command. To follow the previous example, if you use

bigmler --train data/iris.csv --resources-log ./another_log.log

in the same working directory, the value of the flag will be preeminent and resources will be logged in another_log.log. For boolean-valued flags, such as --replacement itself, you’ll need to use the associated negative flags to overide the default behaviour. That is, following the former example if you want to avoid storing the downloaded resource JSON information, you should use the --no-store flag.

bigmler --train data/iris.csv --no-store

The set of negative flags is:

--no-debug as opposed to --debug
--no-train-header as opposed to --train-header
--no-test-header as opposed to --test-header
--local as opposed to --remote
--no-replacement as opposed to --replacement
--no-randomize as opposed to --randomize
--no-no-tag as opposed to --no-tag
--no-public-dataset as opposed to --public-dataset
--no-black-box as opposed to --black-box
--no-white-box as opposed to --white-box
--no-progress-bar as opposed to --progress-bar
--no-no-dataset as opposed to --no-dataset
--no-no-model as opposed to --no-model
--no-clear-logs as opposed to --clear-logs
--no-store as opposed to --store
--no-multi-label as opposed to --multi-label
--no-prediction-header as opposed to --prediction-header
--batch as opposed to --no-batch
--no-balance as opposed to --balance
--no-multi-dataset as opposed to --multi-dataset
--unshared as opposed to --shared
--upload as opposed to --no-upload
--fast as opposed to --no-fast
--no-no-csv as opposed to --no-csv
--no-median as opposed to --median
--no-score as opposed to --score
--server as opposed to --no-server

Support

Please report problems and bugs to our BigML.io issue tracker.

Discussions about the different bindings take place in the general BigML mailing list. Or join us in our Campfire chatroom.

Requirements

Python 2.7 and 3 are currently supported by BigMLer.

BigMLer requires bigml 4.18.1 or higher. Using proportional missing strategy will additionally request the use of the numpy and scipy libraries. They are not automatically installed as a dependency, as they are quite heavy and exclusively required in this case. Therefore, they have been left for the user to install them if required. The same happens with the pystemmer library, used only for topic modeling. Check the bindings documentation for more info.

BigMLer Installation

To install the latest stable release with pip

$ pip install bigmler

You can also install the development version of bigmler directly from the Git repository

$ pip install -e git://github.com/bigmlcom/bigmler.git#egg=bigmler

Pystemmer is needed in order to use the bigmler topic-model subcommand. This library is not installed automatically because it needs compilation and some developer tools to be present in your operative system. If the pip install pystemmer command ends in error, please check the error message for the link to these tools (Windows) or install the Xcode developer tools (OSX).

For a detailed description of install instructions on Windows see the :ref:bigmler-windows section.

BigML Authentication

All the requests to BigML.io must be authenticated using your username and API key and are always transmitted over HTTPS.

BigML module will look for your username and API key in the environment variables BIGML_USERNAME and BIGML_API_KEY respectively. You can add the following lines to your .bashrc or .bash_profile to set those variables automatically when you log in

export BIGML_USERNAME=myusername
export BIGML_API_KEY=ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

Otherwise, you can initialize directly when running the BigMLer script as follows

bigmler --train data/iris.csv --username myusername \
        --api-key ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

For a detailed description of authentication instructions on Windows see the :ref:bigmler-windows section.

BigMLer on Windows

To install BigMLer on Windows environments, you’ll need Python for Windows (v.2.7.x) installed.

In addition to that, you’ll need the pip tool to install BigMLer. To install pip, first you need to open your command line window (write cmd in the input field that appears when you click on Start and hit enter), download this python file and execute it

c:\Python27\python.exe ez_setup.py

After that, you’ll be able to install pip by typing the following command

c:\Python27\Scripts\easy_install.exe pip

And finally, to install BigMLer, just type

c:\Python27\Scripts\pip.exe install bigmler

and BigMLer should be installed in your computer. Then issuing

bigmler --version

should show BigMLer version information.

Finally, to start using BigMLer to handle your BigML resources, you need to set your credentials in BigML for authentication. If you want them to be permanently stored in your system, use

setx BIGML_USERNAME myusername
setx BIGML_API_KEY ae579e7e53fb9abd646a6ff8aa99d4afe83ac291

Remember that setx will not change the environment variables of your actual console, so you will need to open a new one to start using them.

BigML Development Mode

The Sandbox environment that could be reached by using the flag --dev has been deprecated and. Right now, there’s only one mode to work with BigML: the previous ``Production Model `, so the flag is no longer available.

Using BigMLer

To run BigMLer you can use the console script directly. The --help option will describe all the available options

bigmler --help

Alternatively you can just call bigmler as follows

python bigmler.py --help

This will display the full list of optional arguments. You can read a brief explanation for each option below.

Optional Arguments

General configuration

--username BigML’s username. If left unspecified, it will default to the values of the BIGML_USERNAME environment variable
--api-key BigML’s api_key. If left unspecified, it will default to the values of the BIGML_API_KEY environment variable
--debug Activates debug level and shows log info for each https request

Basic Functionality

--train TRAINING_SET Full path to a training set. It can be a remote URL to a (gzipped or compressed) CSV file. The protocol schemes can be http, https, s3, azure, odata
--test TEST_SET Full path to a test set. A file containing the data that you want to input to generate predictions
--objective OBJECTIVE_FIELD The column number of the Objective Field (the field that you want to predict) or its name
--output PREDICTIONS Full path to a file to save predictions. If unspecified, it will default to an auto-generated file created by BigMLer. It overrides --output-dir
--output-dir DIRECTORY Directory where all the session files will be stored. It is overriden by --output
--method METHOD Prediction method used: plurality, "confidence weighted", "probability weighted", threshold or combined
--pruning PRUNING_TYPE The pruning applied in building the model. It’s allowed values are smart, statistical and no-pruning The default value is smart
--missing-strategy STRATEGY The strategy applied predicting when a missing value is found in a model split. It’s allowed values are last or proportional. The default value is last
--missing-splits Turns on the missing_splits flag in model creation. The model splits can include in one of its branches the data with missing values
--evaluate Turns on evaluation mode
--resume Retries command execution
--stack-level LEVEL Level of the retried command in the stack
--cross-validation-rate RATE Fraction of the training data held out for Monte-Carlo cross-validation
--number-of-evaluations NUMBER_OF_EVALUATIONS Number of runs that will be used in cross-validation
--max-parallel-evaluations MAX_PARALLEL_EVALUATIONS Maximum number of evaluations to create in parallel
--project PROJECT_NAME Project name for the project to be associated to newly created sources
--project-id PROJECT_ID Project id for the project to be associated to newly created sources
--org-project PROJECT_ID Project id for the project of an Organization
--no-csv Causes the output of a batch prediction, batch centroid or batch anomaly score not to be downloaded as a CSV file
--to-dataset Causes the output of a batch prediction, batch centroid or batch anomaly score to be stored remotely as a new dataset
--median Predictions for single models are returned based on the median of the distribution in the predicted node

Content

--name NAME Name for the resources in BigML.
--category CATEGORY Category code. See full list.
--description DESCRIPTION Path to a file with a description in plain text or markdown
--tag TAG Tag to later retrieve new resources
--no-tag Puts BigMLer default tag if no other tag is given

Data Configuration

--no-train-header The train set file hasn’t a header
--no-test-header The test set file hasn’t a header
--field-attributes PATH Path to a file describing field attributes One definition per line (e.g., 0,’Last Name’)
--types PATH Path to a file describing field types. One definition per line (e.g., 0, ‘numeric’)
--test-field-attributes PATH Path to a file describing test field attributes. One definition per line (e.g., 0,’Last Name’)
--test-types PATH Path to a file describing test field types. One definition per line (e.g., 0, ‘numeric’)
--dataset-fields DATASET_FIELDS Comma-separated list of field column numbers to include in the dataset
--model-fields MODEL_FIELDS Comma-separated list of input fields (predictors) to create the model
--source-attributes PATH Path to a file containing a JSON expression with attributes to be used as arguments (any of the updatable attributes described in the developers section ) in create source calls
--dataset-attributes PATH Path to a file containing a JSON expression with attributes to be used as arguments (any of the updatable attributes described in the developers section ) in create dataset calls
--model-attributes PATH Path to a file containing a JSON expression with attributes to be used as arguments (any of the updatable attributes described in the developers section ) in create model calls
--ensemble-attributes PATH Path to a file containing a JSON expression with attributes to be used as arguments (any of the updatable attributes described in the developers section ) in create ensemble calls
--evaluation-attributes PATH Path to a file containing a JSON expression with attributes to be used as arguments (any of the updatable attributes described in the developers section ) in create evaluation calls
--batch_prediction-attributes PATH Path to a file containing a JSON expression with attributes to be used as arguments (any of the updatable attributes described in the developers section ) in create batch prediction calls
--json-filter PATH Path to a file containing a JSON expression to filter the source
--lisp-filter PATH Path to a file containing a LISP expression to filter the source
--locale LOCALE Locale code string
--fields-map PATH Path to a file containing the dataset to model fields map for evaluation
--test-separator SEPARATOR Character used as test data field separator
--prediction-header Include a headers row in the prediction file
--prediction-fields TEST_FIELDS Comma-separated list of fields of the test file to be included in the prediction file
--max-categories CATEGORIES_NUMBER Sets the maximum number of categories that will be used in a dataset. When more categories are found, new datasets are generated to analize the remaining categories
--new-fields PATH Path to a file containing a JSON expression used to generate a new dataset with new fields created via Flatline <https://github.com/bigmlcom/flatline> by combining or setting their values
--node-threshold Maximum number or nodes to grow the tree with
--balance Automatically balance data to treat all classes evenly
--weight-field FIELD Field name or column number that contains the weights to be used for each instance
--shared Creates a secret link for every dataset, model or evaluation used in the command
--reports Report formats: “gazibit”
--no-upload Disables reports upload
--dataset-off Sets the evaluation mode that uses the list of test datasets and extracts one each time to test the model built with the rest of them (k-fold cross-validation)
--args-separator Character used as separator in multi-valued arguments (default is comma)
--no-missing-splits Turns off the missing_splits flag in model creation.

Remote Resources

--source SOURCE BigML source Id
--dataset DATASET BigML dataset Id
--datasets PATH Path to a file containing a dataset Id
--model MODEL BigML model Id
--models PATH Path to a file containing model/ids. One model per line (e.g., model/4f824203ce80053)
--ensemble ENSEMBLE BigML ensemble Id
--ensembles PATH Path to a file containing ensembles Ids
--test-source SOURCE BigML test source Id (only for remote predictions)
--test-dataset DATASET BigML test dataset Id (only for remote predictions)
--test-datasets PATH Path to the file that contains datasets ids used in evaluations, one id per line.
--source SOURCE BigML source Id
--dataset DATASET BigML dataset Id
--remote Computes predictions remotely (in batch mode by default)
--no-batch Remote predictions are computed individually
--no-fast Ensemble’s local predictions are computed storing the predictions of each model in a separate local file before combining them (the default is –fast, that keeps in memory each model’s prediction)
--model-tag MODEL_TAG Retrieve models that were tagged with tag
--ensemble-tag ENSEMBLE_TAG Retrieve ensembles that were tagged with tag

Ensembles

--number-of-models NUMBER_OF_MODELS Number of models to create
--sample-rate SAMPLE_RATE Sample rate to use (a float between 0.01 and 1)
--replacement Use replacement when sampling
--max-parallel-models MAX_PARALLEL_MODELS Max number of models to create in parallel
--max-batch-models MAX_BATCH_MODELS Max number of local models to be predicted from in parallel. For ensembles with a number of models over it, predictions are stored in files as they are computed and retrived and combined eventually
--randomize Use a random set of fields to split on
--combine-votes LIST_OF_DIRS Combines the votes of models generated in a list of directories
--ensemble-sample-rate RATE Ensemble sampling rate for bagging
--ensemble-sample-seed SEED Value used as seed in ensembles random selections
--ensemble-sample-no-replacement Don’t use replacement when bagging
--boosting Create a boosted ensemble
--boosting-iterations ITERATIONS Maximum number of iterations used in boosted ensembles.
--early-holdout HOLDOUT The portion of the dataset that will be held out for testing at the end of every iteration in boosted ensembles (between 0 and 1)
--no-early-out-of-bag Causes the out of bag samples not to be tested after every iteration in boosted ensembles.
--learning-rate RATE It controls how aggressively the boosting algorithm will fit the data in boosted ensembles (between 0 and 1)
--no-step-out-of-bag Causes the out of bag samples not to be tested after every iteration to choose the gradient step size in boosted ensembles.

If you are not choosing to create an ensemble, make sure that you tag your models conveniently so that you can then retrieve them later to generate predictions.

Multi-labels

--multi-label Use multiple labels in the objective field
--labels Comma-separated list of labels used
--training-separator SEPARATOR Character used as field separator in train data field
--label-separator SEPARATOR Character used as label separator in the multi-labeled objective field

Public Resources

--public-dataset Makes newly created dataset public
--black-box Makes newly created model a public black-box
--white-box Makes newly created model a public white-box
--model-price Sets the price for a public model
--dataset-price Sets the price for a public dataset
--cpp Sets the credits consumed by prediction

Notice that datasets and models will be made public without assigning any price to them.

Local Resources

--model-file PATH Path to a JSON file containing the model info
--ensemble-file PATH Path to a JSON file containing the ensemble info

Fancy Options

--progress-bar Shows an update on the bytes uploaded when creating a new source. This option might run into issues depending on the locale settings of your OS
--no-dataset Does not create a model. BigMLer will only create a source
--no-model Does not create a model. BigMLer will only create a dataset
--resources-log LOG_FILE Keeps a log of the resources generated in each command
--version Shows the version number
--verbosity LEVEL Turns on (1) or off (0) the verbosity.
--clear-logs Clears the .bigmler, .bigmler_dir_stack, .bigmler_dirs and user log file given in --resources-log (if any)
--store Stores every created or retrieved resource in your output directory

Analyze subcommand Options

--cross-validation Sets the k-fold cross-validation mode
--k-folds Number of folds used in k-fold cross-validation (default is 5)
--features Sets the smart selection features mode
--staleness INTEGER Number of iterations with no improvement that is considered the limit for the analysis to stop (default is 5)
--penalty FLOAT Coefficient used to penalyze models with many features in the smart selection features mode (default is 0.001). Also used in node threshold selection (default is 0)
--optimize METRIC Metric that is being optimized in the smart selection features mode or the node threshold search mode (default is accuracy)
--optimize-category CATEGORY Category whoese metric is being optimized in the smart selection features mode or the node threshold search mode (only for categorical models)
--nodes Sets the node threshold search mode
--min-nodes INTEGER Minimum number of nodes to start the node threshold search mode (default 3)
--max-nodes INTEGER Maximum number of nodes to end the node threshold search mode (default 2000)
--nodes-step INTEGER Step in the node threshold search iteration (default 50)
--exclude-features FEATURES Comma-separated list of features in the dataset to be excluded from the features analysis
--score Causes the training set to be run through the anomaly detector generating a batch anomaly score. Only used with the --remote flag.

Report Specific Subcommand Options

--from-dir Path to a directory where BigMLer has stored its session data and created resources used in the report
--port Port number for the HTTP server used to visualize graphics in bigmler report
--no-server Not starting HTTP local server to show the reports

Cluster Specific Subcommand Options

--cluster CLUSTER BigML cluster Id
--clusters PATH Path to a file containing cluster/ids. One cluster per line (e.g., cluster/4f824203ce80051)
--k NUMBER_OF_CENTROIDS Number of final centroids in the clustering
--no-cluster No cluster will be generated
--cluster-fields Comma-separated list of fields that will be used in the cluster construction
--cluster-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the cluster creation call
--cluster-datasets CENTROID_NAMES Comma-separated list of centroid names to generate the related datasets from a cluster. If no CENTROID_NAMES argument is provided all datasets are generated
--cluster-file PATH Path to a JSON file containing the cluster info
--cluster-seed SEED Seed to generate deterministic clusters
--centroid-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the centroid creation call
--batch-centroid-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the batch centroid creation call
--cluster-models CENTROID_NAMES Comma-separated list of centroid names to generate the related models from a cluster. If no CENTROID_NAMES argument is provided all models are generated
--summary-fields SUMMARY_FIELDS Comma-separated list of fields to be kept for reference but not used in the cluster bulding process

Anomaly Specific Subcommand Options

--anomaly ANOMALY BigML anomaly Id
--anomalies PATH Path to a file containing anomaly/ids. One anomaly per line (e.g., anomaly/4f824203ce80051)
--no-anomaly No anomaly detector will be generated
--anomaly-fields Comma-separated list of fields that will be used in the anomaly detector construction
--top-n Number of listed top anomalies
--forest-size Number of models in the anomaly detector iforest
--anomaly-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the anomaly creation call
--anomaly-file PATH Path to a JSON file containing the anomaly info
--anomaly-seed SEED Seed to generate deterministic anomalies
--anomaly-score-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the anomaly score creation call
--batch-anomaly-score-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the batch anomaly score creation call
--anomalies-datasets [in |out] Separates from the training dataset the top anomalous instances enclosed in the top anomalies list and generates a new dataset including them (in option) or excluding them (out option).

.._sample_options:

Samples Subcommand Options

--sample SAMPLE BigML sample Id
--samples PATH Path to a file containing sample/ids. One sample per line (e.g., sample/4f824203ce80051)
--no-sample No sample will be generated
--sample-fields FIELD_NAMES Comma-separated list of fields that will be used in the sample detector construction
--sample-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the sample creation call
--fields-filter QUERY Query string that will be used as filter before selecting the sample rows. The query string can be built using the field ids, their values and the usual operators. You can see some examples in the developers section
--sample-header Adds a headers row to the sample.csv output
--row-index Prepends acolumn to the sample rows with the absolute row number
--occurrence Prepends a column to the sample rows with the number of occurences of each row. When used with –row-index, the occurrence column will be placed after the index column
--precision Decimal numbers precision
--rows SIZE Number of rows returned
--row-offset OFFSET Skip the given number of rows
--row-order-by FIELD_NAME Field name whose values will be used to sort the returned rows
--row-fields FIELD_NAMES Comma-separated list of fields that will be returned in the sample
--stat-fields FIELD_NAME,FIELD_NAME Two comma-separated numeric field names that will be used to compute their Pearson’s and Spearman’s correlations and linear regression terms
--stat-field FIELD_NAME Numeric field that will be used to compute Pearson’s and Spearman’s correlations and linear regression terms against the rest of numeric fields in the sample
--unique Repeated rows are removed from the sample

Logistic regression Subcommand Options

--logistic-regression LOGISTIC_R BigML logistic regression Id
--logistic-regressions PATH Path to a file containing logisticregression/ids. One logistic regression per line (e.g., logisticregression/4f824203ce80051)
--no-logistic-regression No logistic regression will be generated
--logistic-fields LOGISTIC_FIELDS Comma-separated list of fields that will be used in the logistic regression construction
--normalize Normalize feature vectors in training and prediction inputs
--no-missing-numerics Avoids the default behaviour, which creates a new coefficient for missings in numeric fields. Missing rows are discarded.
--no-bias Avoids default behaviour. The logistic regression will have no intercept term.
--no-balance-fields Avoids default behaviour. No automatic field balance.
--field-codings FIELD_CODINGS Numeric encoding for categorical fields (default one-hot encoding)
--c C Strength of the regularization step
--eps EPS Stopping criteria for solver.
--logistic-regression-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the logistic regression creation call
--logistic-regression-file PATH Path to a JSON file containing the logistic regression info

Topic Model Subcommand Options

--topic-model TOPIC_MODEL BigML topic model Id
--topic-models PATH Path to a file containing topicmodel/ids. One topic model per line (e.g., topicmodel/4f824203ce80051)
--no-topic-model No topic model will be generated
--topic-fields TOPIC_FIELDS Comma-separated list of fields that will be used in the topic model construction
--bigrams Use bigrams in topic search
--case-sensitive Use case sensitive tokenization
--excluded-terms EXCLUDED_TERMS Comma-separated list of terms to be excluded from the analysis
--use-stopwords Use stopwords in the analysis.
--topic-model-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the topic model creation call
--topic-model-file PATH Path to a JSON file containing the topic model info

Time Series Subcommand Options

--time-seriers TIME_SERIES BigML time series Id
--time-series-set PATH Path to a file containing timeseries/ids One time series per line (e.g., timeseries/4f824203ce80051)
--no-time-series No time series will be generated.
--objectives OBJECTIVES Comma-separated list of fields that will be used in the time series as objective fields
--time-series-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the time series creation call
--time-series-file PATH Path to a JSON file containing the time series info
--all-numeric-objectives When used, all the numeric fields in the dataset are considered objective fields
--default-numeric-value DEFAULT The value used by default if a numeric field is missing. Spline interpolation is used by default and other options are “mean”, “median”, “minimum”, “maximum” and “zero”
--error TYPE Type of error considered: 1 - Additive, 2 - Multiplicative
--period PERIOD Expected period
--seasonality SEASONALITY Type of seasonality: 0 - None, 1 - Additive, 2 - Multiplicative
--trend TREND Type of trend: 0 - None, 1 - Additive, 2 - Multiplicative
--range RANGE Comma-separated pair of values that set the range limits
--damped-trend When set damping is used in trend
--forecast When set, the time series default forecast is produced
--horizon HORIZON Set to an integer, is the number of points in the forecast
--time-start START Time starting point coordinate
--time-end END Time ending point coordinate
--time-unit UNIT Unit for the time interval. The options are described in the API documentation
--time-interval INTERVAL Time interval between two rows

Deepnet Subcommand Options

--deepnet DEEPNET BigML deepnet Id
--deepnets PATH Path to a file containing deepnet/ids. One deepnet per line (e.g., deepnet/4f824203ce80051)
--no-deepnet No deepnet will be generated
--deepnet-fields DEEPNET_FIELDS Comma-separated list of fields that will be used in the deepnet construction
--batch-normalization Specifies whether to normalize the outputs of a network before being passed to the activation function or not.
--default-numeric-value DFT It accepts any of the following strings to substitute missing numeric values across all the numeric fields in the dataset (options: mean, median minimum, maximum, zero).
--dropout-rate RATE A number between 0 and 1 specifying the rate at which to drop weights during training to control overfitting.
--hidden-layers LAYERS A JSON file that contains a list of maps describing the number and type of layers in the network (other than the output layer, which is determined by the type of learning problem).
--learn-residuals Specifies whether alternate layers should learn a representation of the residuals for a given layer rather than the layer itself or not.
--learning-rate RATE A number between 0 and 1 specifying the learning rate.
--max-iterations ITERATIONS A number between 100 and 100000 for the maximum number of gradient steps to take during the optimization.
--max-training-time TIME The maximum wall-clock training time, in seconds, for which to train the network.
--number-of-hidden-layers #LAYERS The number of hidden layers to use in the network. If the number is greater than the length of the list of hidden_layers, the list is cycled until the desired number is reached. If the number is smaller than the length of the list of hidden_layers, the list is shortened.
--number-of-model-candidates #CAND An integer specifying the number of models to try during the model search.
--search During the deepnet creation, BigML trains and evaluates over all possible network configurations, returning the best networks found for the problem. The final deepnet returned by the search is a compromise between the top n networks found in the search. Since this option builds several networks, it may be significantly slower than the suggest_structure technique.
--missing-numerics Whether to create an additional binary predictor each numeric field which denotes a missing value. If false, these predictors are not created, and rows containing missing numeric values are dropped.
--tree-embedding Specify whether to learn a tree-based representation of the data as engineered features along with the raw features, essentially by learning trees over slices of the input space and a small amount of the training data. The theory is that these engineered features will linearize obvious non-linear dependencies before training begins, and so make learning proceed more quickly.
--no-missing-numerics Avoids the default behaviour, which creates a new coefficient for missings in numeric fields. Missing rows are discarded.
--deepnet-attributes PATH Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) to be used in the deepnet creation call
--deepnet-file PATH Path to a JSON file containing the logistic regression info

Reify Subcommand Options

--id RESOURCE_ID ID for the resource to be reified
--language SCRIPTING_LANG Language to be used for the script. Currently only Python is available
--output PATH Path to the file where the script will be stored
--add-fields Causes the fields information to be added to the source arguments

Execute Subcommand Options

--code SOURCE_CODE WhizzML source code to be executed
``–code-file `` PATH Path to the file that contains Whizzml source code
--creation-defaults RESOURCE_DEFAULTS Path to the JSON file with the default configurations for created resources. Please, see details in the API Developers documentation
--declare-inputs INPUTS_DECLARATION Path to the JSON file with the description of the input parameters. Please, see details in the API Developers documentation
--declare-outputs OUTPUTS_DECLARATION Path to the JSON file with the description of the script outputs. Please, see details in the API Developers documentation
--embedded-libraries PATH Path to a file that contains the location of the files to be embedded in the script as libraries
--execution EXECUTION_ID BigML execution ID
--execution-file EXECUTION_FILE BigML execution JSON structure file
--execution-tag EXECUTION_TAG Select executions tagged with EXECUTION_TAG
--executions EXECUTIONS Path to a file containing execution/ids. Just one execution per line (e.g., execution/50a20697035d0706da0004a4)
--imports LIBRARIES Comma-separated list of libraries IDs to be included as imports in scripts or other libraries
--input-maps INPUT_MAPS Path to the JSON file with the description of the execution inputs for a list of scripts
--inputs INPUTS Path to the JSON file with the description of the execution inputs. Please, see details in the API Developers documentation
--libraries LIBRARIES Path to a file containing libraries/ids. Just one library per line (e.g., library/50a20697035d0706da0004a4)
--library LIBRARY BigML library Id.
--library-file LIBRARY_FILE BigML library JSON structure file.
--library-tag LIBRARY_TAG Select libraries tagged with tag to be deleted
--outputs OUTPUTS Path to the JSON file with the names of the output parameters. Please, see details in the API Developers documentation
--script SCRIPT BigML script Id.
--script-file SCRIPT_FILE BigML script JSON structure file.
--script-tag SCRIPT_TAG Select script tagged with tag to be deleted
--scripts SCRIPTS Path to a file containing script/ids. Just one script per line (e.g., script/50a20697035d0706da0004a4).
--to-library Boolean that causes the code to be compiled and stored as a library

Whizzml Subcommand Options

--package-dir DIR Directory that stores the package files
``–embed-libs `` I causes the subcommand to embed the libraries code in the package scripts instead of creating libraries and importing them

Retrain Subcommand Options

--id RESOURCE_ID ID for the resource to be reified
--window-size SIZE Maximum number of datasets to be used
--add PATH Path to the file that contains the data to be added
--upgrade Causes the scripts that generate the models rebuild to be recreated
--model-tag TAG Retrieves models that were tagged with tag
--ensemble-tag TAG Retrieves ensembles that were tagged with tag
--cluster-tag TAG Retrieves clusters that were tagged with tag
--anomaly-tag TAG Retrieves anomalies that were tagged with tag
--logistic-regression-tag TAG Retrieves logistic regressions that were tagged with tag
--topic-model-tag TAG Retrieves topic models that were tagged with tag
--time-series-tag TAG Retrieves time series that were tagged with tag
--association-tag TAG Retrieves associations that were tagged with tag
--deepnet-tag TAG Retrieves deepnets that were tagged with tag

Delete Subcommand Options

Project Specific Subcommand Options

--organization ORGANIZATION_ID Organization ID to create projects in an organization
--project-attributes Path to a JSON file containing attributes for the project

Association Specific Subcommand Options

--association-attributes Path to a JSON file containing attributes (any of the updatable attributes described in the developers section ) for the association
--max-k K Maximum number of rules to be found
--search-strategy STRATEGY Strategy used when searching for the associations. The possible values are: confidence, coverage, leverage, lift, support

Prior Versions Compatibility Issues

BigMLer will accept flags written with underscore as word separator like --clear_logs for compatibility with prior versions. Also --field-names is accepted, although the more complete --field-attributes flag is preferred. --stat_pruning and --no_stat_pruning are discontinued and their effects can be achived by setting the actual --pruning flag to statistical or no-pruning values respectively.

Running the Tests

To run the tests you will need to install lettuce

$ pip install lettuce

and set up your authentication via environment variables, as explained above. With that in place, you can run the test suite simply by

$ cd tests
$ lettuce

Building the Documentation

Install the tools required to build the documentation

$ pip install sphinx

To build the HTML version of the documentation

$ cd docs/
$ make html

Then launch docs/_build/html/index.html in your browser.

Additional Information

For additional information, see the full documentation for the Python bindings on Read the Docs. For more information about BigML’s API, see the BigML developer’s documentation.

How to Contribute

Please follow the next steps:

  1. Fork the project on github.
  2. Create a new branch.
  3. Commit changes to the new branch.
  4. Send a pull request.

For details on the underlying API, see the BigML API documentation.