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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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