Metadata-Version: 2.1
Name: mmanager
Version: 2.0.1
Summary: Modelmanager API With Azure ML Integration
Home-page: UNKNOWN
Author: falcon
License: MIT
Description: ﻿# Welcome to Modelmanager-api!
        
        ## This is a api model for interacting with modelmanager.
        
        > **Note:**
        >
        > - Example files are are in example_script directory.
        > - Example assets are in assets directory.
        > - It contains scripts for different actions(Add, Update, Delete).
        
        ---
        
        ## **Example Codes**
        
        ## Add Project / Usecase
        
        ```python
        from mmanager.mmanager import Usecase
        secret_key = 'Secret-Key'
        url = 'URL'
        data = {
        		"name": "UsecaseName",
        		"author": "AuthorName",
        		"description": "UsecaseDescription",
        		"source": "UsecasSource",
        		"contributor": "UsecaseContributor",
        		"image": 'image.jpg' , #path to image file
        		"banner": 'banner.jpg' , #path to banner file
        	}
        Usecase(secret_key, url).post_usecase(data)
        ```
        
        ---
        
        ## Update Project
        
        ```python
        from mmanager.mmanager import Usecase
        secret_key = 'Secret-Key'
        url = 'URL'
        project_id = Project_id #use model_id number to update
        data = {
        		"author": "AuthorName",
        		"description": "UsecaseDescription",
        		"source": "UsecasSource",
        		"contributor": "UsecaseContributor",
        		"image": 'image.jpg' , #path to image file
        		"banner": 'banner.jpg' , #path to banner file
        	}
        Usecase(secret_key, url).patch_usecase(data, project_id)
        ```
        
        ---
        
        ## Delete Project
        
        ```python
        from mmanager.mmanager import Usecase
        secret_key = 'Secret-Key'
        url = 'URL'
        project_id = Project_id #use project_id number to delete
        Usecase(secret_key,url).delete_usecase(project_id)
        ```
        
        ---
        
        
        ## Create Config File For Azure ML Credentials
        - Get Credentials from your existing Azure ML account.
        - Create a config file in following format 
        - Give credential file path in credPath field to enable using AML integration service.
        
        ```json
        {
            "subscription_id": "<subscription-id>",
            "resource_group": "<resource_group>",
            "workspace_name": "<workspace_name>",
            "tenant-id": "<tenant-id>",
            "datastore_name": "<datastore_name>"
        }
        ```
        
        ---
        
        ## Add Model No ML Integration
        
        ```python
        from mmanager.mmanager import Model
        secret_key = 'Secret-Key'
        url = 'URL'
        path = 'assets' #path to csv file
        
        model_data = {
            "project": "<project-id>", #Project ID or Usecase ID
            "transformerType": "model-type", #Options: Classification, Regression, Forcasting
            "training_dataset": "%s/model_assets/train.csv" % path, #path to csv file
            "test_dataset": "%s/model_assets/test.csv" % path, #path to csv file
            "pred_dataset": "%s/model_assets/pred.csv" % path, #path to csv file
            "actual_dataset": "%s/model_assets/truth.csv" % path, #path to csv file
            "model_file_path": "%s/model_assets/model.h5" % path, #path to model file
            "target_column": "target-column-name", #Target Column
            "note": "" #Short description of Model
        }
        Model(secret_key, url).post_model(model_data)
        ```
        ## Additional model fields
        ```json
        {
            "model_area": "",
            "model_dependencies": "",
            "model_usage": "",
            "model_audjustment": "",
            "model_developer": "",
            "model_approver": "",
            "model_maintenance": "",
        }
        ```
        ## Add Model, Fetch Datasets And Model From Azure ML
        
        ```python
        from mmanager.mmanager import Model
        secret_key = 'Secret-Key'
        url = 'URL'
        model_data = {
            "project": "<project-id>", #Project ID or Usecase ID
            "transformerType": "model-type", #Options: Classification, Regression, Forcasting
            "training_dataset": "",
            "test_dataset": "",
            "pred_dataset": "",
            "actual_dataset": "", 
            "model_file_path": "", 
            "target_column": "target-column-name", #Target Column
            "note": "" #Short description of Model
            }
        
        ml_options = {
            "credPath": "config.json", #Path to Azure ML credential files.
            "datasetinsertionType": "AzureML", #Option: AzureML, Manual
            "fetchOption": ["Model"], #To fetch model, add ["Model", "Dataset"] to fetch both model and datasets.
            "modelName": "model-name", #Fetch model file registered with model name.
            "dataPath": "dataset-name", #Get datasets registered with dataset name.
            }
        Model(secret_key, url).post_model(model_data, ml_options)
        ```
        ## Add Model, Upload Datasets And Model Manually
        
        ```python
        from mmanager.mmanager import Model
        secret_key = 'Secret-Key'
        url = 'URL'
        path = 'assets' #path to csv file
        model_data = {
            "project": "<project-id>", #Project ID or Usecase ID
            "transformerType": "model-type", #Options: Classification, Regression, Forcasting
            "training_dataset": "%s/model_assets/train.csv" % path, #path to csv file
            "test_dataset": "%s/model_assets/test.csv" % path, #path to csv file
            "pred_dataset": "%s/model_assets/pred.csv" % path, #path to csv file
            "actual_dataset": "%s/model_assets/truth.csv" % path, #path to csv file
            "model_file_path": "%s/model_assets/model.h5" % path, #path to model file
            "target_column": "target-column-name", #Target Column
            "note": "" #Short description of Model
            }
        
        ml_options = {
            "credPath": "config.json", #Path to Azure ML credential files.
            "datasetinsertionType": "Manual", #Option: AzureML, Manual
            "registryOption": ["Model"], #To register model, add ["Model", "Dataset"] to register both model and datasets.
            "datasetUploadPath": "dataset-name", #To registere dataset on path.
            }
        Model(secret_key, url).post_model(model_data, ml_options)
        ```
        ---
        
        ## Update Model
        
        ```python
        from mmanager.mmanager import Model
        secret_key = 'Secret-Key'
        url = 'URL'
        model_id = Model_id #use model_id number to update
        data = {
        		"transformerType": "logistic",
        		"target_column": "id",
        		"note": "api script Model",
        		"model_area": "api script Model",
        		"model_dependencies": "api script Model",
        		"model_usage": "api script Model",
        		"model_audjustment": "api script Model",
        		"model_developer": "api script Model",
        		"model_approver": "api script Model",
        		"model_maintenance": "api script Model",
        		"documentation_code": "api script Model",
        		"implementation_plateform": "api script Model",
        		"training_dataset": "train.csv", #path to csv file
        		"pred_dataset": "submissionsample.csv", #path to csv file
        		"actual_dataset": "truth.csv", #path to csv file
        		"test_dataset": "test.csv", #path to csv file
        		"model_file_path":"",
        	    "scoring_file_path":"",
        		"model_image_path":"" ,
            	"model_summary_path":"",
        	}
        Model(secret_key, url).patch_model(data, model_id)
        ```
        
        ---
        
        # Delete Model
        
        ---
        
        ```python
        from mmanager.mmanager import Model
        secret_key = 'Secret-Key'
        url = 'URL'
        model_id = Model_id #use model_id number to delete
        Model(secret_key,url).delete_model(model_id)
        ```
        
        ---
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
