In this repository you'll find a set of very simple shell scripts interacting with https://bigml.io/.
These shell scripts use bash, curl and python to interact with https://bigml.io and create datasources, datasets, models and, eventually, predictions. You'll be also able to retrieve them in JSON format.
The scripts are written in bash, and use curl to send HTTPS requests to our servers. Responses, in JSON format, are pretty-printed in the console via python's json.tool module.
To access bigml.io you need a username and an API key, which are assigned to you one you register in our site.
The scripts look for them in the environment variables BIGML_USERNAME
and BIGML_API_KEY, which are combined in the authentication token
BIGML_AUTH. The env/api_key.sh file provides a
template you can use in your .bashrc or .bash_profile to set those
variables automatically when you log in:
BIGML_USERNAME=<username>
BIGML_API_KEY=<apikey>
export BIGML_URL=https://bigml.io/andromeda/
export BIGML_AUTH="username=$BIGML_USERNAME;api_key=$BIGML_API_KEY;"All scripts in the repo live in the bin directory.
To create a datasource from a local data file, use create_source.sh.
This simple script takes a single parameter, namely, the path to the
data file, and uses curl to upload the file to BigML's servers,
register an associated datasource and prints its JSON descriptor.
Here's a sample invocation:
~/bigml/io/bash $ ./create_source.sh ../csv/iris.csv
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 5461 100 653 100 4808 114 843 0:00:05 0:00:05 --:--:-- 8094
{
"code": 201,
"content_type": "application/octet-stream",
"created": "2012-03-23T01:44:25.687600",
"credits": 0.0,
"file_name": "iris.csv",
"md5": "d1175c032e1042bec7f974c91e4a65ae",
"name": "iris.csv",
"number_of_datasets": 0,
"number_of_models": 0,
"number_of_predictions": 0,
"private": true,
"resource": "source/4f6bd5791552687fb5000003",
"size": 4608,
"source_parser": {
"header": true,
"locale": "en-US",
"missing_tokens": [
"N/A",
"n/a",
"NA",
"na",
"-",
"?"
],
"quote": "\"",
"separator": ",",
"trim": true
},
"status": {
"code": 2,
"elapsed": 0,
"message": "The source creation has been started"
},
"type": 0,
"updated": "2012-03-23T01:44:25.687628"
}Resource creation is asynchronous: the created resource status code
will be, as you see in the sample above, 2 (i.e., in-progress). You
can retrieve the descriptor (with its updated status) at any time
using the get.sh script with the resource identifier as its only
argument.
~/bigml/io/bash $ ./get.sh source/4f6bd5791552687fb5000003
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 1050 100 1050 0 0 179 0 0:00:05 0:00:05 --:--:-- 2908
{
"code": 200,
"content_type": "application/octet-stream",
"created": "2012-03-23T01:44:25.687000",
"credits": 0.0087890625,
"fields": {
"000000": {
"column_number": 0,
"name": "sepal length",
"optype": "numeric"
},
"000001": {
"column_number": 1,
"name": "sepal width",
"optype": "numeric"
},
"000002": {
"column_number": 2,
"name": "petal length",
[...]
"status": {
"code": 5,
"elapsed": 2969,
"message": "The source has been created"
},
"type": 0,
"updated": "2012-03-23T01:44:28.755000"
}Once you have created a datasource, you can create a dataset, which is a processed version of the data accompanied by metadata describing its contents (data types, histograms, etc.).
Creating a dataset containing all columns in the source and using the
default parsing options is accomplished via create_dataset.sh, which
takes the datasource resource identifier as its only argument.
~/bigml/io/bash $ ./create_dataset.sh source/4f6bd5791552687fb5000003
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 919 100 874 100 45 156 8 0:00:05 0:00:05 --:--:-- 1758
{
"code": 201,
"columns": 5,
"created": "2012-03-23T02:07:49.831948",
"credits": 0.0087890625,
"fields": {
"000000": {
"column_number": 0,
"name": "sepal length",
"optype": "numeric"
},
"000001": {
"column_number": 1,
"name": "sepal width",
"optype": "numeric"
},
"000002": {
"column_number": 2,
"name": "petal length",
"optype": "numeric"
},
"000003": {
"column_number": 3,
"name": "petal width",
"optype": "numeric"
},
"000004": {
"column_number": 4,
"name": "species",
"optype": "categorical"
}
},
"locale": "en-US",
"name": "iris' dataset",
"number_of_models": 0,
"number_of_predictions": 0,
"private": true,
"resource": "dataset/4f6bdaf51552687fb3000006",
"rows": 0,
"size": 4608,
"source": "source/4f6bd5791552687fb5000003",
"source_status": true,
"status": {
"code": 1,
"message": "The dataset is being processed and will be created soon"
},
"updated": "2012-03-23T02:07:49.831969"
}Again, the process is asynchronous: your task has been scheduled
(status 1, i.e., queued), and the new resource has been assigned an
identifier (dataset/4f6bdaf51552687fb3000006) which you can use in
conjunction with the get.sh script to recover the dataset's JSON
metadata at any other time:
./get.sh dataset/4f6bdaf51552687fb3000006With a dataset resource in your hand, you can proceed to the creation
of a predictive model, using create_model.sh.
~/bigml/io/bash (master) $ ./create_model.sh dataset/4f6bdaf51552687fb3000006
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 691 100 644 100 47 112 8 0:00:05 0:00:05 --:--:-- 1018
{
"code": 201,
"columns": 5,
"created": "2012-03-23T02:28:57.741150",
"credits": 0.03515625,
"dataset": "dataset/4f6bdaf51552687fb3000006",
"dataset_status": true,
"holdout": 0.0,
"input_fields": [],
"locale": "en-US",
"max_columns": 5,
"max_rows": 150,
"name": "iris' dataset model",
"number_of_predictions": 0,
"objective_fields": [],
"private": true,
"range": [
1,
150
],
"resource": "model/4f6bdfe9035d075177000005",
"rows": 150,
"size": 4608,
"source": "source/4f6bd5791552687fb5000003",
"source_status": true,
"status": {
"code": 1,
"message": "The model is being processed and will be created soon"
},
"updated": "2012-03-23T02:28:57.741177"
}Again, the model is scheduled for creation, and you can retrieve its
status at any time by means of get.sh and its resource identifier.
You can now use the model resource identifier together with some
input parameters to ask for predictions, using create_prediction.sh:
~/bigml/io/bash $ ./create_prediction.sh model/4f6bdfe9035d075177000005 '{"000002":1.2}'
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 1040 0 967 100 73 170 12 0:00:06 0:00:05 0:00:01 1729
{
"code": 201,
"created": "2012-03-23T02:52:23.651644",
"credits": 0.01,
"dataset": "dataset/4f6bdaf51552687fb3000006",
"dataset_status": true,
"fields": {
"000002": {
"column_number": 2,
"datatype": "double",
"name": "petal length",
"optype": "numeric"
},
"000004": {
"column_number": 4,
"datatype": "string",
"name": "species",
"optype": "categorical"
}
},
"input_data": {
"000002": 1.2
},
"locale": "en-US",
"model": "model/4f6bdfe9035d075177000005",
"model_status": true,
"name": "Prediction for species",
"objective_fields": [
"000004"
],
"prediction": {
"000004": "Iris-setosa"
},
"prediction_path": {
"bad_fields": [],
"next_predicates": [],
"path": [
{
"field": "000002",
"operator": "<=",
"value": 2.45
}
],
"unknown_fields": []
},
"private": true,
"resource": "prediction/4f6be5671552687fb5000005",
"source": "source/4f6bd5791552687fb5000003",
"source_status": true,
"status": {
"code": 5,
"message": "The prediction has been created"
},
"updated": "2012-03-23T02:52:23.651667"
}Given an identifier, the corresponding resource can be deleted with
delete.sh:
~/bigml/io/bash $ ./delete.sh prediction/4f6be5671552687fb5000005
~/bigml/io/bash $ ./get.sh prediction/4f6be5671552687fb5000005
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 65 100 65 0 0 12 0 0:00:05 0:00:05 --:--:-- 211
{"code": 404, "status": {"code": -1104, "message": "Not found"}}
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