mlrequest-python/mlrequest/mlrequest.py at master · mlrequest/mlrequest-python · GitHub

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from mlrequest import regions

from requests_futures.sessions import FuturesSession

import json

import requests

import sklearn_json as skljson

import sys

import logging

class Classifier:

session = FuturesSession()

base_url = 'https://api.mlrequest.com'

model_path = '/v1/classifier/'

batch_model_path = '/v1/classifier/batch/'

predict_path = 'predict'

learn_path = 'learn'

batch_size = 100

def __init__(self, api_key=None):

if not api_key:

raise MissingAPIKey('An API Key is required. Visit https://mlrequest.com for a free or paid API Key.')

self.session.headers['MLREQ-API-KEY'] = api_key

def predict(self, features, model_name, class_count):

self._validate_predict_fields(features, model_name, class_count)

if isinstance(features, list):

return self.batch_predict(features, model_name, class_count)

else:

payload = {

'features': features,

'model_name': model_name,

'class_count': class_count

}

future = self.session.post(f'{self.base_url}{self.model_path}{self.predict_path}', json=payload)

response = json.loads(future.result().content)

if ('predict_result' in response) and ('class_scores' in response):

return Response({'predict_result': response['predict_result'], 'class_scores': response['class_scores'], 'content': response})

else:

return Response({'predict_result': None, 'content': response})

def batch_predict(self, features, model_name, class_count):

class_scores = []

predict_results = []

errors = []

for i in range(0, len(features), self.batch_size):

futures = []

feature_batch = features[i:i+self.batch_size]

payload = {

'features': feature_batch,

'model_name': model_name,

'class_count': class_count

}

futures.append(self.session.post(f'{self.base_url}{self.batch_model_path}{self.predict_path}', json=payload))

for future in futures:

response = json.loads(future.result().content)

if ('class_scores' in response) and ('predict_result' in response):

class_scores += response['class_scores']

predict_results += response['predict_result']

else:

class_scores += [None]

predict_results += [None]

errors.append(response)

return Response({'predict_result': predict_results, 'class_scores': class_scores, 'errors': errors, 'content': response})

def learn(self, training_data, model_name, class_count):

self._validate_learn_fields(training_data, model_name, class_count)

if isinstance(training_data, list):

return self.batch_learn(training_data, model_name, class_count)

else:

payload = {

'features': training_data['features'],

'model_name': model_name,

'class_count': class_count,

'label': training_data['label']

}

future = self.session.post(f'{self.base_url}{self.model_path}{self.learn_path}', json=payload)

return Response({'content': json.loads(future.result().content)})

def batch_learn(self, training_data, model_name, class_count):

responses = []

for i in range(0, len(training_data), self.batch_size):

futures = []

training_batch = training_data[i:i+self.batch_size]

payload = {

'training_data': training_batch,

'model_name': model_name,

'class_count': class_count,

}

futures.append(self.session.post(f'{self.base_url}{self.batch_model_path}{self.learn_path}', json=payload))

for future in futures:

responses.append(json.loads(future.result().content))

return Response({'content': responses})

def _validate_predict_fields(self, features, model_name, class_count):

if not (isinstance(features, dict) or isinstance(features, list)):

raise ValueError(f'features must be a dict or a list of dicts')

if len(features) == 0:

raise ValueError(f'features must not be empty')

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

if not isinstance(class_count, int):

raise ValueError(f'class_count must be an integer')

def _validate_learn_fields(self, training_data, model_name, class_count):

if not (isinstance(training_data, dict) or isinstance(training_data, list)):

raise ValueError(f'training_data must be a dict or a list of dicts containing features and an associated label.')

if len(training_data) == 0:

raise ValueError(f'training data must not be empty')

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

if not isinstance(class_count, int):

raise ValueError(f'class_count must be an integer')

class Regression:

session = FuturesSession()

base_url = 'https://api.mlrequest.com'

model_path = '/v1/regression/'

batch_model_path = '/v1/regression/batch/'

predict_path = 'predict'

learn_path = 'learn'

batch_size = 100

def __init__(self, api_key=None):

if not api_key:

raise MissingAPIKey('An API Key is required. Visit https://mlrequest.com for a free or paid API Key.')

self.session.headers['MLREQ-API-KEY'] = api_key

def predict(self, features, model_name):

self._validate_predict_fields(features, model_name)

if isinstance(features, list):

return self.batch_predict(features, model_name)

else:

payload = {

'features': features,

'model_name': model_name

}

future = self.session.post(f'{self.base_url}{self.model_path}{self.predict_path}', json=payload)

response = json.loads(future.result().content)

if 'predict_result' in response:

return Response({'predict_result': response['predict_result'], 'content': response})

else:

return Response({'predict_result': None, 'content': response})

def batch_predict(self, features, model_name):

predict_results = []

errors = []

for i in range(0, len(features), self.batch_size):

futures = []

feature_batch = features[i:i+self.batch_size]

payload = {

'features': feature_batch,

'model_name': model_name

}

futures.append(self.session.post(f'{self.base_url}{self.batch_model_path}{self.predict_path}', json=payload))

for future in futures:

response = json.loads(future.result().content)

if 'predict_result' in response:

predict_results += response['predict_result']

else:

predict_results += [None]

errors.append(response)

return Response({'predict_result': predict_results, 'errors': errors, 'content': response})

def learn(self, training_data, model_name):

self._validate_learn_fields(training_data, model_name)

if isinstance(training_data, list):

return self.batch_learn(training_data, model_name)

else:

payload = {

'features': training_data['features'],

'model_name': model_name,

'label': training_data['label']

}

future = self.session.post(f'{self.base_url}{self.model_path}{self.learn_path}', json=payload)

return Response({'content': json.loads(future.result().content)})

def batch_learn(self, training_data, model_name):

responses = []

for i in range(0, len(training_data), self.batch_size):

futures = []

training_batch = training_data[i:i+self.batch_size]

payload = {

'training_data': training_batch,

'model_name': model_name

}

futures.append(self.session.post(f'{self.base_url}{self.batch_model_path}{self.learn_path}', json=payload))

for future in futures:

responses.append(json.loads(future.result().content))

return Response({'content': responses})

def _validate_predict_fields(self, features, model_name):

if not (isinstance(features, dict) or isinstance(features, list)):

raise ValueError(f'features must be a dict or a list of dicts')

if len(features) == 0:

raise ValueError(f'features must not be empty')

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

def _validate_learn_fields(self, training_data, model_name):

if not (isinstance(training_data, dict) or isinstance(training_data, list)):

raise ValueError(f'training_data must be a dict or a list of dicts containing features and an associated label.')

if len(training_data) == 0:

raise ValueError(f'training data must not be empty')

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

class RL:

session = FuturesSession()

base_url = 'https://api.mlrequest.com'

model_path = '/v1/rl/'

predict_path = 'predict'

learn_path = 'reward'

def __init__(self, api_key=None):

if not api_key:

raise MissingAPIKey('An API Key is required. Visit https://mlrequest.com for a free or paid API Key.')

self.session.headers['MLREQ-API-KEY'] = api_key

def predict(self, features, model_name, session_id, negative_reward,

action_count, epsilon=0.2, action_list=None):

self._validate_predict_fields(features, model_name, session_id, negative_reward,

action_count, epsilon, action_list)

payload = {

'features': features,

'model_name': model_name,

'session_id': session_id,

'negative_reward': negative_reward,

'action_count': action_count,

'epsilon': epsilon,

'action_list': action_list

}

future = self.session.post(f'{self.base_url}{self.model_path}{self.predict_path}', json=payload)

response = json.loads(future.result().content)

if 'predict_result' in response:

return Response({'predict_result': response['predict_result'], 'content': response})

else:

return Response({'predict_result': None, 'content': response})

def reward(self, reward, model_name, session_id):

self._validate_reward_fields(model_name, session_id, reward)

payload = {

'model_name': model_name,

'session_id': session_id,

'reward': reward

}

future = self.session.post(f'{self.base_url}{self.model_path}{self.learn_path}', json=payload)

return Response({'content': json.loads(future.result().content)})

def _validate_predict_fields(self, features, model_name, session_id, negative_reward,

action_count, epsilon, action_list):

if not isinstance(features, dict):

raise ValueError(f'features must be a dict')

if len(features) == 0:

raise ValueError(f'features must not be empty')

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

if not isinstance(session_id, str):

raise ValueError(f'session_id must be a string')

if not isinstance(negative_reward, (int, float)):

raise ValueError(f'negative_reward must be a number')

if (not isinstance(action_count, int)) or action_count < 0:

raise ValueError(f'negative_reward must be an int greater than 0')

if not isinstance(epsilon, float) or epsilon < 0 or epsilon > 1:

raise ValueError(f'negative_reward must be an float between 0 and 1')

if (action_list is not None) and (not isinstance(action_list, list)):

raise ValueError(f'action_list must be None or a list')

def _validate_reward_fields(self, model_name, session_id, reward):

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

if not isinstance(session_id, str):

raise ValueError(f'session_id must be a string')

if not isinstance(reward, (int, float)):

raise ValueError(f'model_name must be a number')

class Account:

session = FuturesSession()

base_url = 'https://api.mlrequest.com'

def __init__(self, api_key=None):

self.log = logging.getLogger(__name__ + '.Account')

if not api_key:

raise MissingAPIKey('An API Key is required. Visit https://mlrequest.com for a free or paid API Key.')

self.session.headers['MLREQ-API-KEY'] = api_key

def delete_model(self, model_name):

payload = {

'model_name': model_name

}

future = self.session.post(f'{self.base_url}/v1/model/delete', json=payload)

response = json.loads(future.result().content)

return Response({'content': response})

def get_details(self):

future = self.session.get(f'{self.base_url}/v1/account')

response = json.loads(future.result().content)

return Response({'content': response})

class SKLearn:

def __init__(self, api_key=None):

self.log = logging.getLogger(__name__ + '.SKLearn')

if not api_key:

raise MissingAPIKey('An API Key is required. Visit https://mlrequest.com for a free or paid API Key.')

self.headers = {'MLREQ-API-KEY': api_key}

def deploy(self, sklearn_model, model_name, region_url=regions.ALL):

payload = {

'model_name': model_name

}

try:

r = requests.post(f'{region_url}/v1/sklearn/deploy/url', json=payload, headers=self.headers)

response = r.json()

except Exception as e:

return Response({'content': f'There was an error getting the deploy URL {r.text}'})

if 'url' in response and 'tier' in response:

url = response['url']

tier = response['tier']

model_dict = skljson.to_dict(sklearn_model)

model_size_bytes = sys.getsizeof(json.dumps(model_dict))

model_size_mb = model_size_bytes/1000**2

if (model_size_mb > 1) and (tier == 0):

raise ModelSizeExceeded(f'Model size is {model_size_mb} MB. Upgrade to a paid plan for model sizes greater than 1 MB.')

if model_size_mb > 100:

raise ModelSizeExceeded(f'Model size is {model_size_mb} MB and cannot exceed 100 MB.')

requests.put(url, json=model_dict, headers={'content-type': 'application/json'})

return Response({'content': 'Model deployed.'})

elif 'message' in response:

return Response({'content': response['message']})

else:

return Response({'content': response})

def predict(self, features, model_name, region_url=regions.ALL):

self._validate_predict_fields(features, model_name)

payload = {

'features': features,

'model_name': model_name

}

try:

r = requests.post(f'{region_url}/v1/sklearn/predict', json=payload, headers=self.headers)

response = r.json()

except Exception as e:

return f'Error getting scikit-learn prediction: {e}, {r.text}'

if 'predict_result' in response:

return Response({'predict_result': response['predict_result'], 'content': response})

else:

return Response({'predict_result': None, 'content': response})

def _validate_predict_fields(self, features, model_name):

if not (isinstance(features, list) and isinstance(features[0], list)):

raise ValueError(f'Features must be a list of lists. '

f'Example of one prediction: [[1, 2, 3]]. '

f'Example of multiple predictions: [[1, 2, 3], [5, 6, 7], ...]')

if len(features) == 0:

raise ValueError(f'features must not be empty')

if not isinstance(model_name, str):

raise ValueError(f'model_name must be a string')

class Response(dict):

def __init__(self, *args, **kwargs):

super(Response, self).__init__(*args, **kwargs)

self.__dict__ = self

class MissingAPIKey(Exception):

pass

class ModelSizeExceeded(Exception):

pass

mlrequest-python/mlrequest/mlrequest.py at master · mlrequest/mlrequest-python · GitHub

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