deepint is a python package to work with Deep Intelligence in a more easy and intuitive way.
Project description
DeepIntelligence SDK for Python
DeepIntelligence SDK
deepint is a python package to work with DeepIntelligence in a more easy and intuitive way, allowing the programmer to develop in a fast way the data, analisys and visualization flows. The package consists in a wrapper arround DeepIntelligence API, with some extra facilities.
Deep Intelligence
Deep Intelligence has been designed to help you select optimal AI & Machine Learning algorithms for the analysis of your business’ datasets. This platform can be customized to read any type of data from webs, files, databases, sensors…, it can also stream data in real time if needed, it’s all very simple!
A highly attractive, user-friendly and intuitive visualization environment will guide you in the creation and configuration of algorithms that will analyze your data optimally. The platform makes it possible to create dashboards for better visualization experience, moreover, they can be easily integrated into any other online application. Improve your business decision-making without any expert IT knowledge!
Deep Intelligence is a platform for Fintech, IoT, Smart Cities, Smart Grids, Biomedical analysis, Logistics, Industry 4.0, etc. Some of our customers have already increased their business’ profits by 50%!
Our extensive team of data analysis experts will be at your complete disposal for any information, guidance and support you may need.
Visit the DeepIntelligence on it's website.
Installation
- install:
python3 -m pip install deepint
- run tests: install test dependencies with
python3 -m pip install deepint-sdk[tests]
, then define the enviroment variablesDEEPINT_TOKEN
,DEEPINT_ORGANIZATION
andTEST_CSV
(the last one is a CSV file to test the features of the SKD). Finally, go to the tests foleder and runpytest -vv test.py
- generate doc: install documentation dependencies with
python3 -m pip install -e deepint-sdk[docs]
, then go to the docs foleder and runmake html
Documentation
Visit the documentation page at Pypi or readthedocs
Setup credentials
Credentials can be set up with one of the following methods (the token is loaded in the priority defined in the order of the following items):
- instance credentials object with the token and organization optional parameters
c = Credentials(token='a token')
- create a environment variable called
DEEPINT_TOKEN
with the token value. - create a .ini file in your home directory called
.deepint
coninting in theDEFAULT
section the keytoken
like in following example[DEFAULT] token=a token
To learn more about credentials setup, please visit the official documentation.
Usage of main components
Load organization and access information and components
from deepint import Organization
org = Organization.build(organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b")
print(org.account)
print(org.workspaces.fetch_all())
ws = org.workspaces.fetch_all()[0]
print(ws.alerts.fetch_all())
print(ws.tasks.fetch_all())
print(ws.models.fetch_all())
print(ws.sources.fetch_all())
print(ws.dashboards.fetch_all())
print(ws.visualizations.fetch_all())
print(ws.info)
print(ws.alerts.fetch_all()[0].info)
print(ws.tasks.fetch_all()[0].info)
print(ws.models.fetch_all()[0].info)
print(ws.sources.fetch_all()[0].output_features)
print(ws.sources.fetch_all()[0].input_features.fetch_all())
print(ws.sources.fetch_all()[0].info)
print(ws.sources.fetch_all()[0].features.fetch_all())
print(ws.dashboards.fetch_all()[0].info)
print(ws.visualizations.fetch_all()[0].info)
# also all elements have to_dict method
print(ws.info.to_dict())
Create workspace, source, alert and model
from deepint import Organization, AlertType, ModelType, ModelMethod
org = Organization.build(organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b")
workspace = org.workspaces.create(name='example', description='example')
source = workspace.sources.create(name='example', description='example', features=[])
target_feature = source.features.fetch_all()[0]
model = workspace.models.create(name='example', description='example', model_type=ModelType.regressor, method=ModelMethod.tree, source=source, target_feature_name=target_feature.name)
alert = workspace.alerts.create(name='example', description='example', subscriptions=['example@example.ex'], color='#FF00FF', alert_type=AlertType.update, source_id=source.info.source_id)
task = workspace.tasks.fetch_all(force_reload=True)[0]
Load elements with builder
from deepint import Organization, Workspace, Model, Alert, Task, Alert, Source
t_id = 'f88cd9ac-8bc7-49db-ab49-b53512b6adc9'
a_id = 'ce92588d-700a-42d6-92f9-76863b648359'
m_id = 'a1dec81d-b46d-44a0-8c7d-3d9db6b45449'
ws_id = '03f695f2-8b6a-4b7d-9f66-e2479f8025a4'
src_id = 'e7da542c-f38c-42bf-bc1d-e89eac179047'
org_id = 'organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b'
ws = Workspace.build(credentials=org.credentials, workspace_id=ws_id, organization_id=org_id)
task = Task.build(task_id=a_id, workspace_id=ws_id, organization_id=org_id, credentials=org.credentials)
model = Model.build(model_id=a_id, workspace_id=ws_id, organization_id=org_id, credentials=org.credentials)
alert = Alert.build(alert_id=a_id, workspace_id=ws_id, organization_id=org_id, credentials=org.credentials)
src = Source.build(source_id=src_id, workspace_id=ws_id, organization_id=org_id, credentials=org.credentials)
Load elements with URL
from deepint import Organization, Workspace, Model, Alert, Task, Alert, Source
t_id = 'f88cd9ac-8bc7-49db-ab49-b53512b6adc9'
a_id = 'ce92588d-700a-42d6-92f9-76863b648359'
m_id = 'a1dec81d-b46d-44a0-8c7d-3d9db6b45449'
ws_id = '03f695f2-8b6a-4b7d-9f66-e2479f8025a4'
src_id = 'e7da542c-f38c-42bf-bc1d-e89eac179047'
org_id = 'organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b'
ws = Workspace.from_url(url=f'https://app.deepint.net/o/{org_id}/workspace?ws={ws_id}', credentials=org.credentials)
ws = Workspace.from_url(url=f'https://app.deepint.net/api/v1/workspace/{ws_id}', credentials=org.credentials, organization_id=org_id)
t = Task.from_url(url=f'https://app.deepint.net/api/v1/workspace/{ws_id}/task/{t_id}', credentials=org.credentials, organization_id=org_id)
t = Task.from_url(url=f'https://app.deepint.net/o/{org_id}/workspace?ws={ws_id}&s=task&i={t_id}', credentials=org.credentials)
m = Model.from_url(url=f'https://app.deepint.net/o/{org_id}/workspace?ws={ws_id}&s=model&i={m_id}', credentials=org.credentials)
m = Model.from_url(url=f'https://app.deepint.net/api/v1/workspace/{ws_id}/models/{m_id}', credentials=org.credentials, organization_id=org_id)
a = Alert.from_url(url=f'https://app.deepint.net/o/{org_id}/workspace?ws={ws_id}&s=alert&i={a_id}', credentials=org.credentials)
a = Alert.from_url(url=f'https://app.deepint.net/api/v1/workspace/{ws_id}/alerts/{a_id}', credentials=org.credentials, organization_id=org_id)
src = Source.from_url(url=f'https://app.deepint.net/o/{org_id}/workspace?ws={ws_id}&s=source&i={src_id}', credentials=org.credentials)
src = Source.from_url(url=f'https://app.deepint.net/api/v1/workspace/{ws_id}/source/{src_id}', credentials=org.credentials, organization_id=org_id)
Create source from pandas.DataFrame
import pandas as pd
from deepint import Organization, Source
org = Organization.build(organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b")
ws = org.workspaces.fetch(name='example')
# create empty source
empty_source = ws.sources.create(name='example', description='example', features=[])
# create source from dataframe (creates columns with the index, name nad type)
data = pd.read_csv('example.csv')
source = ws.sources.create_and_initialize(name='exampe', description='exampe', data=data)
Use sources
import pandas as pd
from deepint import Organization, Source, Task
ws_id = '03f695f2-8b6a-4b7d-9f66-e2479f8025a4'
org = Organization.build(organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b")
ws = org.workspaces.fetch(workspace_id='example')
# create source from dataframe (creates columns with the index, name nad type)
data = pd.read_csv('example.csv')
source = ws.sources.create_and_initialize(name='exampe', description='exampe', data=data)
# update instances
data2 = pd.read_csv('example.csv')
task = source.instances.update(data=data2)
# wait for task to finish
task.resolve()
# retrieve all instances
retrieved_data = source.instances.fetch()
# query for instances
query = {...} # query of deepint.net
retrieved_data = source.instances.fetch(where=query)
# delete instances
task = source.instances.delete(where=query)
task.resolve()
# udpate source name
source.update(name='example2', description='example2')
# update source features
feature = source.features.fetch_all()[0]
feature.feature_type = FeatureType.unknown
source.features.update()
# create source if not exists, else only retrieve
source = ws.sources.create_if_not_exists('test')
source1 = ws.sources.create_if_not_exists('test')
if source == source1:
print('source is equal to source1 because the method works!')
source.delete()
# create (with initialization) source if not exists, else only retrieve
source = ws.sources.create_else_update(('test', data)
source1 = ws.sources.create_else_update('test', data)
if source == source1:
print('source is equal to source1 because the method works!')
source.delete()
# delete source
source.delete()
Use models
import pandas as pd
from deepint import Organization, Model, Task
org = Organization.build(organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b")
ws = org.workspaces.fetch(name='example')
data = pd.read_csv('example.csv')
source = ws.sources.create_and_initialize(name='example', description='example', data=data)
# create model
model = ws.models.create(name='example', description='example', model_type=ModelType.classifier, method=ModelMethod.gradient, source=source, target_feature_name='country')
# update model
model.update(name=f'other name', description=f'other description')
# get model evaluation
evaluation = model.predictions.evaluation()
# predict one instance
data_one_instance = data.head(n=1)
del data_one_instance['country']
prediction_result = model.predictions.predict(data_one_instance)
# predict batch
data_some_instances = data.head(n=25)
del data_some_instances['name']
prediction_result = model.predictions.predict_batch(data_some_instances)
# predict with variaions
variations = [i/100 for i in range(100)]
prediction_result = model.predictions.predict_unidimensional(data_one_instance, variations, 'water_percentage')
# delete model
model.delete()
Use tasks
import pandas as pd
from deepint import Organization, Model, Task, TaskStatus
from deepint DeepintTaskError
org = Organization.build(organization_id="3a874c05-26d1-4b8c-894d-caf90e40078b")
ws = org.workspaces.fetch(name='example')
# retrieve tasks by status
pending_tasks = ws.tasks.fetch_by_status(status=TaskStatus.pending)
# cancel task
t = pending_tasks[0]
t.delete()
# wait for task to finish
t = pending_tasks[1]
try:
t.resolve()
result = t.fetch_result()
except DeepintTaskError as e:
print(f'the task was errored with error {e}')
# update and check if errored
t = pending_tasks[2]
t.load()
if t.is_errored():
print('an errror occurred')
Use visualizations
import pandas as pd
from deepint import Organization, Visualization, Source
# load organization and create workspace
org = Organization.build(organization_id='a1faa528-1d42-4cf0-ae04-e122d0ddf9aa')
ws = org.workspaces.create(name='example', description='example')
# create a source for the visualization
data = pd.read_csv('example.csv')
src = ws.sources.create_and_initialize(name='exampe', description='exampe', data=data)
# create visualization
vis = ws.visualizations.create(name='example', description='example', privacy='public', source='source_id', configuration={})
# update visualization
vis.update(name='example2', description='example2', source='source_id')
# delete visualization
vis.delete()
Use dashboards
from deepint import Credentials, Organization, Dashboard
# load organization and create workspace
org = Organization.build(organization_id='e612d27d-9c81-479f-a35f-85cac80c0718')
ws = org.workspaces.create(name='example', description='example')
# create dashboard
dash = ws.dashboards.create(name='example', description='example', privacy='public', shareOpt="",
gaId="", restricted=True, configuration={})
# update dashboard
dash.update(name='example2', description='example2')
# delete dashboard
dash.delete()
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