Project

class Project(object)

This namespace represents a project in DS2.ai's desktop application, specifically CLICK AI, which is for AI modeling.

init()

__init__(info, user)

Parmeters

  • info (str) : Information about the current Click AI project corresponding to the object
  • user (str) : User for creating, modifying, deleting the project

Fields

  • id : Project ID.
  • url : DS2 API url information.
  • user : User information.
  • user_token : User token information.
  • status
  • dataconnectorList
  • models
  • jupyter_servers

repr()

Returns the Click AI's project ID as a printable format.
__repr__()

Return type

  • str

refresh()

Reads and refresh Project id, and check whether the project is ready to use.
modify()

Return type

  • class CLICKAI project

delete()

Deletes the Project object that has corresponding project id and token.
delete()

Return type

  • None

Reference

  • You can find the corresponding project id deleted.

train()

Starts AI training according to the corresponding training method, value for prediction, and training option.
startTraining(training_method=None, value_for_predict=None, option="accuracy")

Parmeters

  • training_method (str) : One of the suitable training methods with your data from 'cycle_gan', 'image', 'normal_classification', 'normal_regression', 'object_detection', 'time_series', 'time_series_regression', 'time_series_classification', 'recommender' or 'image_classification'.
  • value_for_predict (str): When the format of data_file is .csv, result column name. (if has_label_data is "True", input the column name the data file has, if "False", enter an name you want as the result column name) When the format of data_file is not .csv, leave as default value.
  • option (str): One of the training options, 'accuracy' for higher accuracy, or 'speed' for faster training speed.

Return type

  • Project object

Reference

The project has been started.

stop()

Stops the ongoing AI training.
stop()

Return type

  • Project object

get_magic_code()

Makes a new ipynb file to the designated file path and writes the needed code for AI modeling.
get_magic_code(training_method, value_for_predict, file_path="output.ipynb")

Parameters

  • training_method (str): Training method as a string.
  • value_for_predict (str): User for making the data connector corresponding to the object.
  • file_path (str): Designated file path for saving ipynb file.

Return type

  • None
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class Project(object)
init()
refresh()
delete()
train()
stop()
get_magic_code()