Metadata-Version: 2.1
Name: learning-orchestra-client
Version: 0.1.0
Summary: Learning Orchestra client for Python
Home-page: https://github.com//riibeirogabriel/learningOrchestra
Author: Gabriel Ribeiro
Author-email: gabbriel.rribeiro@gmail.com
License: UNKNOWN
Description: # Learning Orchestra Client
        This python package is created to usage with Learning Orchestra microservices
        
        ## Installation
        pip install learning_orchestra_cliet
        
        ## Documentation
        
        After downloading the package, import all classes:
        
        `
        from learning_orchestra_client import *
        `
        
        create a Context object passing a ip from your cluster in constructor parameter:
        
        `
        cluster_ip = "34.95.222.197"
        Context(cluster_ip)
        `
        
        After create a Context object, you will able to usage learningOrchestra microservices.
        
        ## DatabaseApi
        
        ### `read_resume_files(pretty_response=True)`
        
        Read all metadata files in learningOrchestra
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ### `read_file(self, filename_key, skip=0, limit=10, query={}, pretty_response=True)`
        * filename_ley : filename of file
        * skip: number of rows amount to skip in pagination (default 0)
        * limit: number of rows to return in pagination (default 10)(max setted in 20 rows per request)
        * query: query to make in mongo (default empty query)
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ### `create_file(self, filename, url, pretty_response=True)`
        * filename: filename of file to be created
        * url: url to csv file
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ### `delete_file(self, filename, pretty_response=True)`
        * filename: file filename to be deleted
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ## Projection
        
        ### `create_projection(self, filename, projection_filename, fields, pretty_response=True)`
        
        * filename: filename of file to make projection
        * projection_filename: filename used to create projection
        * field: list with fields to make projection 
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ## DataTypeHandler
        
        ### `change_file_type(self, filename, fields_dict, pretty_response=True)`
        * filenbame: filename of file
        * fields_dict: dictionary with "field": "number" or field: "string" keys  
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ## ModelBuilder
        
        ### `build_model(self, training_filename, test_filename, label='label', pretty_response=True)`
        
        * training_filename: filename to be used in training
        * test_filename: filename to be used in test
        * label: case of traning filename have a label with other name
        * pretty_response: return indented string to visualization (default True, if False, return dict)
        
        ## Example
        
        In below there is script using the package:
        
        
            from learning_orchestra_client import *
        
            cluster_ip = "34.95.222.197"
        
            Context(cluster_ip)
        
            database_api = DatabaseApi()
        
            print(database_api.read_file("training", skip=20, limit=10))
        
            projection = Projection()
        
            print(projection.create_projection(
                    "training2", "titanic_training_projection",
                    ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
                    'Parch', 'Embarked']))
            print(projection.create_projection(
                    "titanic_testing_10", "titanic_testing_projection",
                    ['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',
                    'Ticket', 'Fare', 'Embarked']))
        
        
            print(database_api.read_resume_files())
            print(database_api.delete_file("titanic_testing_10"))
            print(database_api.delete_file("titanic_training_10"))
            print(database_api.read_resume_files())
            print(database_api.create_file(
                "titanic_training_10",
                "https://filebin.net/rpfdy8clm5984a4c/titanic_training.csv?t=bg4b9hfg"))
            print(database_api.create_file(
                "titanic_testing_10",
                "https://filebin.net/mguee52ke97k0x9h/titanic_testing.csv?t=7iojj2d2"))
            print(database_api.read_file("titanic_training_10"))
            print(database_api.read_resume_files())
        
            projection = Projection()
        
            data_type_handler = DataTypeHandler()
        
            print(data_type_handler.change_file_type(
                "titanic_training_10", {"Survived": "number"}))
        
            model_builder = ModelBuilder()
        
        
            print(model_builder.build_model(
                "titanic_training_10", "titanic_testing_10", "Survived"))
        
            print(database_api.delete_file("titanic_testing_10"))
        
            print(database_api.read_resume_files())
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
