learning-orchestra-client 0.1.0

Learning Orchestra client for Python

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())