Data Input (DP_1)
Specify the data sources, this functionality can take database connection, CSV, JSON or ZIP files
NOTE: If you're using a database connection, create a separate user with appropriate permissions, before uploading data from a database.
Sample Request
{
"project_id": 1,
"parent_id": 0,
"block_id": 1,
"function_code": "DP_1",
"args": {
"dburl": "https://raw.githubusercontent.com/autogonai/autogon-public-datasets/main/mobile_price_prediction.csv",
"dbservertype": "",
"file_type": "csv",
"database_name": "",
"dbuser": "",
"dbpassword": "",
"query": "",
}
}Data input
POST https://api.autogon.ai/api/v1/engine/start
Loads data into a project.
Request Body
dburl*
String
database host or Data Source URL
file_type*
String
File type for data input (db for database, csv for CSV, json for JSON for source files.
dbservertype
String
database server type (required with file_type: db)
database_name
String
Name of the database (required with file_type: db)
dbuser
String
Username for connecting with database (required with file_type: db)
dbpassword
String
Password for connecting with the database (required with file_type: db)
query
String
query to fetch data from the database (required with file_type: db)
project_id*
int
current project ID
block_id*
int
current block ID
function_code*
String
block's function code
args*
object
block arguments
parent_id
int
previous block ID
method*
String
Method of importation.
Options:
'img_class_folder': import as an image classification problem with folder names as image class labels
'img_csv': import as an image regression or classification problem
with image paths listed in a colmn of a csv and target variables on other column(s)
(required with file_type : zip)
images_path
String
Path to images from zip file's root
(required with file_type: zip)
class_mode
String
Method of importing target labels or variables Options:
'binary': The inputter will return binary labels indicating the class membership of each sample
'categorical': The inputter will return one-hot encoded labels, where each class is represented by a binary vector with a single 1 and 0s elsewhere
'sparse': Instead of one-hot encoded labels for a multi-classifcation problem, the inputter will return integer labels for each class
'input': The inputter will return the input images as both the input and output.
'raw': The inputter will return the raw data gotten for the y_column
target_size*
array
Image size in pixels (Length, Width)
(required with file_type: zip)
csv_path*
String
Path to CSV from zip file's root
(required with file_type: zip and method: img_csv)
image_files_column*
String
Column name of column containing the list of image file names (images within folders aren't allowed)
(required with file_type: zip and method: img_csv)
y_columns
array
Column names of columns containing target variable(s). Leave empty to include all other columns
(required with file_type: zip and method: img_csv)
{
"status": "true",
"message": {
"id": 1,
"project": 2,
"block_id": 1,
"parent_id": 0,
"dataset_url": "",
"x_value_url": "",
"y_value_url": ""
}
}// Some codeawait client.data_input(1, 0, 1, {
dburl: "https://raw.githubusercontent.com/autogonai/autogon-public-datasets/main/mobile_price_prediction.csv" ,
file_type: "csv"
})Last updated
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