Official Python client for the MonkeyLearn API. Build and consume machine learning models for language processing from your Python apps.

Overview

MonkeyLearn API for Python

Official Python client for the MonkeyLearn API. Build and run machine learning models for language processing from your Python apps.

Installation

You can use pip to install the library:

$ pip install monkeylearn

Alternatively, you can just clone the repository and run the setup.py script:

$ python setup.py install

Usage

Before making requests to the API, you need to create an instance of the MonkeyLearn client. You will have to use your account API Key:

from monkeylearn import MonkeyLearn

# Instantiate the client Using your API key
ml = MonkeyLearn('<YOUR API TOKEN HERE>')

Requests

From the MonkeyLearn client instance, you can call any endpoint (check the available endpoints below). For example, you can classify a list of texts using the public Sentiment analysis classifier:

response = ml.classifiers.classify(
    model_id='cl_Jx8qzYJh',
    data=[
        'Great hotel with excellent location',
        'This is the worst hotel ever.'
    ]
)

Responses

The response object returned by every endpoint call is a MonkeyLearnResponse object. The body attribute has the parsed response from the API:

print(response.body)
# =>  [
# =>      {
# =>          'text': 'Great hotel with excellent location',
# =>          'external_id': null,
# =>          'error': false,
# =>          'classifications': [
# =>              {
# =>                  'tag_name': 'Positive',
# =>                  'tag_id': 1994,
# =>                  'confidence': 0.922,
# =>              }
# =>          ]
# =>      },
# =>      {
# =>          'text': 'This is the worst hotel ever.',
# =>          'external_id': null,
# =>          'error': false,
# =>          'classifications': [
# =>              {
# =>                  'tag_name': 'Negative',
# =>                  'tag_id': 1941,
# =>                  'confidence': 0.911,
# =>              }
# =>          ]
# =>      }
# =>  ]

You can also access other attributes in the response object to get information about the queries used or available:

print(response.plan_queries_allowed)
# =>  300

print(response.plan_queries_remaining)
# =>  240

print(response.request_queries_used)
# =>  2

Errors

Endpoint calls may raise exceptions. Here is an example on how to handle them:

from monkeylearn.exceptions import PlanQueryLimitError, MonkeyLearnException

try:
    response = ml.classifiers.classify('[MODEL_ID]', data=['My text'])
except PlanQueryLimitError as e:
    # No monthly queries left
    # e.response contains the MonkeyLearnResponse object
    print(e.error_code, e.detail)
except MonkeyLearnException:
    raise

Available exceptions:

class Description
MonkeyLearnException Base class for every exception below.
RequestParamsError An invalid parameter was sent. Check the exception message or response object for more information.
AuthenticationError Authentication failed, usually because an invalid token was provided. Check the exception message. More about Authentication.
ForbiddenError You don't have permissions to perform the action on the given resource.
ModelLimitError You have reached the custom model limit for your plan.
ModelNotFound The model does not exist. Check the model_id.
TagNotFound The tag does not exist. Check the tag_id parameter.
PlanQueryLimitError You have reached the monthly query limit for your plan. Consider upgrading your plan. More about Plan query limits.
PlanRateLimitError You have sent too many requests in the last minute. Check the exception detail. More about Plan rate limit.
ConcurrencyRateLimitError You have sent too many requests in the last second. Check the exception detail. More about Concurrency rate limit.
ModelStateError The state of the model is invalid. Check the exception detail.

Auto-batching

Classify and Extract endpoints might require more than one request to the MonkeyLearn API in order to process every text in the data parameter. If the auto_batch parameter is True (which is the default value), you won't have to keep the data length below the max allowed value (200). You can just pass the full list and the library will handle the batching and make the necessary requests. If the retry_if_throttled parameter is True (which is the default value), it will also wait and retry if the API throttled a request.

Let's say you send a data parameter with 300 texts and auto_batch is enabled. The list will be split internally and two requests will be sent to MonkeyLearn with 200 and 100 texts, respectively. If all requests respond with a 200 status code, the responses will be appended and you will get the 300 classifications as usual in the MonkeyLearnResponse.body attribute:

data = ['Text to classify'] * 300
response = ml.classifiers.classify('[MODEL_ID]', data)
assert len(response.body) == 300  # => True

Now, let's say you only had 200 queries left when trying the previous example, the second internal request would fail since you wouldn't have queries left after the first batch and a PlanQueryLimitError exception would be raised. The first 200 (successful) classifications will be in the exception object. However, if you don't manage this exception with an except clause, those first 200 successful classifications will be lost. Here's how you should handle that case:

from monkeylearn.exceptions import PlanQueryLimitError

data = ['Text to classify'] * 300
batch_size = 200

try:
    response = ml.classifiers.classify('[MODEL_ID]', data, batch_size=batch_size)
except PlanQueryLimitError as e:
    partial_predictions = e.response.body  # The body of the successful responses
    non_2xx_raw_responses = r.response.failed_raw_responses  # List of requests responses objects
else:
    predictions = response.body

This is very convenient and usually should be enough. If you need more flexibility, you can manage batching and rate limits yourself.

from time import sleep
from monkeylearn.exceptions import PlanQueryLimitError, ConcurrencyRateLimitError, PlanRateLimitError

data = ['Text to classify'] * 300
batch_size = 200
predictions = []

for i in range(0, len(data), batch_size):
    batch_data = data[i:i + batch_size]

    retry = True
    while retry:
        try:
            retry = True
            response = ml.classifiers.classify('[MODEL_ID]', batch_data, auto_batch=False,
                                               retry_if_throttled=False)
        except PlanRateLimitError as e:
            sleep(e.seconds_to_wait)
        except ConcurrencyRateLimitError:
            sleep(2)
        except PlanQueryLimitError:
            raise
        else:
            retry = False

    predictions.extend(response.body)

This way you'll be able to control every request that is sent to the MonkeyLearn API.

Available endpoints

These are all the endpoints of the API. For more information about each endpoint, check out the API documentation.

Classifiers

Classify

def MonkeyLearn.classifiers.classify(model_id, data, production_model=False, batch_size=200,
                                     auto_batch=True, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
data list[str or dict] A list of up to 200 data elements to classify. Each element must be a string with the text or a dict with the required text key and the text as the value. You can provide an optional external_id key with a string that will be included in the response.
production_model bool Indicates if the classifications are performed by the production model. Only use this parameter with custom models (not with the public ones). Note that you first need to deploy your model to production either from the UI model settings or by using the Classifier deploy endpoint.
batch_size int Max number of texts each request will send to MonkeyLearn. A number from 1 to 200.
auto_batch bool Split the data list into smaller valid lists, send each one in separate request to MonkeyLearn, and merge the responses.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

data = ['First text', {'text': 'Second text', 'external_id': '2'}]
response = ml.classifiers.classify('[MODEL_ID]', data)

Classifier detail

def MonkeyLearn.classifiers.detail(model_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.detail('[MODEL_ID]')

Create Classifier

def MonkeyLearn.classifiers.create(name, description='', algorithm='nb', language='en',
                                   max_features=10000, ngram_range=(1, 1), use_stemming=True,
                                   preprocess_numbers=True, preprocess_social_media=False,
                                   normalize_weights=True, stopwords=True, whitelist=None,
                                   retry_if_throttled=True)

Parameters:

Parameter Type Description
name str The name of the model.
description str The description of the model.
algorithm str The algorithm used when training the model. It can be either "nb" or "svm".
language str The language of the model. Full list of supported languages.
max_features int The maximum number of features used when training the model. Between 10 and 100000.
ngram_range tuple(int,int) Indicates which n-gram range used when training the model. A list of two numbers between 1 and 3. They indicate the minimum and the maximum n for the n-grams used.
use_stemming bool Indicates whether stemming is used when training the model.
preprocess_numbers bool Indicates whether number preprocessing is done when training the model.
preprocess_social_media bool Indicates whether preprocessing of social media is done when training the model.
normalize_weights bool Indicates whether weights will be normalized when training the model.
stopwords bool or list The list of stopwords used when training the model. Use False for no stopwords, True for the default stopwords, or a list of strings for custom stopwords.
whitelist list The whitelist of words used when training the model.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.create(name='New classifier', stopwords=True)

Edit Classifier

def MonkeyLearn.classifiers.edit(model_id, name=None, description=None, algorithm=None,
                                 language=None, max_features=None, ngram_range=None,
                                 use_stemming=None, preprocess_numbers=None,
                                 preprocess_social_media=None, normalize_weights=None,
                                 stopwords=None, whitelist=None, retry_if_throttled=None)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
name str The name of the model.
description str The description of the model.
algorithm str The algorithm used when training the model. It can be either "nb" or "svm".
language str The language of the model. Full list of supported languages.
max_features int The maximum number of features used when training the model. Between 10 and 100000.
ngram_range tuple(int,int) Indicates which n-gram range used when training the model. A list of two numbers between 1 and 3. They indicate the minimum and the maximum n for the n-grams used.
use_stemming bool Indicates whether stemming is used when training the model.
preprocess_numbers bool Indicates whether number preprocessing is done when training the model.
preprocess_social_media bool Indicates whether preprocessing of social media is done when training the model.
normalize_weights bool Indicates whether weights will be normalized when training the model.
stopwords bool or list The list of stopwords used when training the model. Use False for no stopwords, True for the default stopwords, or a list of strings for custom stopwords.
whitelist list The whitelist of words used when training the model.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.edit('[MODEL_ID]', description='The new description of the classifier')

Delete classifier

def MonkeyLearn.classifiers.delete(model_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.delete('[MODEL_ID]')

List Classifiers

def MonkeyLearn.classifiers.list(page=1, per_page=20, order_by='-created', retry_if_throttled=True)

Parameters:

Parameter Type Description
page int Specifies which page to get.
per_page int Specifies how many items per page will be returned.
order_by string or list Specifies the ordering criteria. It can either be a string for single criteria ordering or a list of strings for more than one. Each string must be a valid field name; if you want inverse/descending order of the field prepend a - (dash) character. Some valid examples are: 'is_public', '-name' or ['-is_public', 'name'].
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.list(page=2, per_page=5, order_by=['-is_public', 'name'])

Deploy

def MonkeyLearn.classifiers.deploy(model_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.deploy('[MODEL_ID]')

Train

def MonkeyLearn.classifiers.train(model_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.train('[MODEL_ID]')

Tag detail

def MonkeyLearn.classifiers.tags.detail(model_id, tag_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
tag_id int Tag ID.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.tags.detail('[MODEL_ID]', TAG_ID)

Create tag

def MonkeyLearn.classifiers.tags.create(model_id, name, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
name str The name of the new tag.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.tags.create('[MODEL_ID]', 'Positive')

Edit tag

def MonkeyLearn.classifiers.tags.edit(model_id, tag_id, name=None,
                                      retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
tag_id int Tag ID.
name str The new name of the tag.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.tags.edit('[MODEL_ID]', TAG_ID, 'New name')

Delete tag

def MonkeyLearn.classifiers.tags.delete(model_id, tag_id, move_data_to=None,
                                        retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
tag_id int Tag ID.
move_data_to int An optional tag ID. If provided, training data associated with the tag to be deleted will be moved to the specified tag before deletion.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.classifiers.tags.delete('[MODEL_ID]', TAG_ID)

Upload data

def MonkeyLearn.classifiers.upload_data(model_id, data, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Classifier ID. It always starts with 'cl', for example, 'cl_oJNMkt2V'.
data list[dict] A list of dicts with the keys described below.
input_duplicates_strategy str Indicates what to do with duplicate texts in this request. Must be one of merge, keep_first or keep_last.
existing_duplicates_strategy str Indicates what to do with texts of this request that already exist in the model. Must be one of overwrite or ignore.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

data dict keys:

Key Description
text A string of the text to upload.
tags A list of tags that can be refered to by their numeric ID or their name. The text will be tagged with each tag in the list when created (in case it doesn't already exist on the model). Otherwise, its tags will be updated to the new ones. New tags will be created if they don't already exist.
markers An optional list of string. Each one represents a marker that will be associated with the text. New markers will be created if they don't already exist.

Example:

response = ml.classifiers.upload_data(
    model_id='[MODEL_ID]',
    data=[{'text': 'text 1', 'tags': [TAG_ID_1, '[tag_name]']},
          {'text': 'text 2', 'tags': [TAG_ID_1, TAG_ID_2]}]
)

Extractors

Extract

def MonkeyLearn.extractors.extract(model_id, data, production_model=False, batch_size=200,
                                   retry_if_throttled=True, extra_args=None)

Parameters:

Parameter Type Description
model_id str Extractor ID. It always starts with 'ex', for example, 'ex_oJNMkt2V'.
data list[str or dict] A list of up to 200 data elements to extract from. Each element must be a string with the text or a dict with the required text key and the text as the value. You can also provide an optional external_id key with a string that will be included in the response.
production_model bool Indicates if the extractions are performed by the production model. Only use this parameter with custom models (not with the public ones). Note that you first need to deploy your model to production from the UI model settings.
batch_size int Max number of texts each request will send to MonkeyLearn. A number from 1 to 200.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

data = ['First text', {'text': 'Second text', 'external_id': '2'}]
response = ml.extractors.extract('[MODEL_ID]', data=data)

Extractor detail

def MonkeyLearn.extractors.detail(model_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Extractor ID. It always starts with 'ex', for example, 'ex_oJNMkt2V'.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.extractors.detail('[MODEL_ID]')

List extractors

def MonkeyLearn.extractors.list(page=1, per_page=20, order_by='-created', retry_if_throttled=True)

Parameters:

Parameter Type Description
page int Specifies which page to get.
per_page int Specifies how many items per page will be returned.
order_by string or list Specifies the ordering criteria. It can either be a string for single criteria ordering or a list of strings for more than one. Each string must be a valid field name; if you want inverse/descending order of the field prepend a - (dash) character. Some valid examples are: 'is_public', '-name' or ['-is_public', 'name'].
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.extractors.list(page=2, per_page=5, order_by=['-is_public', 'name'])

Workflows

Workflow detail

def MonkeyLearn.workflows.detail(model_id, step_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
step_id int Step ID.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.workflows.detail('[MODEL_ID]', '[STEP_ID]')

Create workflow

def MonkeyLearn.workflows.create(name, db_name, steps, description='', webhook_url=None,
                                 custom_fields=None, sources=None, retry_if_throttled=True)

Parameters:

Parameter Type Description
name str The name of the model.
db_name str The name of the database where the data will be stored. The name must not already be in use by another database.
steps list[dict] A list of step dicts.
description str The description of the model.
webhook_url str An URL that will be called when an action is triggered.
custom_fields [] A list of custom_field dicts that represent user defined fields that come with the input data and that will be saved. It does not include the mandatory text field.
sources {} An object that represents the data sources of the workflow.

Example:

response = ml.workflows.create(
    name='Example Workflow',
    db_name='example_workflow',
    steps=[{
        name: 'sentiment',
        model_id: 'cl_pi3C7JiL'
    }, {
        name: 'keywords',
        model_id: 'ex_YCya9nrn'
    }])

Delete workflow

def MonkeyLearn.workflows.delete(model_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.workflows.delete('[MODEL_ID]')

Step detail

def MonkeyLearn.workflows.steps.detail(model_id, step_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
step_id int Step ID.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.workflows.steps.detail('[MODEL_ID]', STEP_ID)

Create step

def MonkeyLearn.workflows.steps.create(model_id, name, step_model_id, input=None,
                                         conditions=None, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
name str The name of the new step.
step_model_id str The ID of the MonkeyLearn model that will run in this step. Must be an existing classifier or extractor.
input str Where the input text to use in this step comes from. It can be either the name of a step or input_data (the default), which means that the input will be the original text.
conditions list[dict] A list of condition dicts that indicate whether this step should execute or not. All the conditions in the list must be true for the step to execute.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.workflows.steps.create(model_id='[MODEL_ID]',  name='sentiment',
                                     step_model_id='cl_pi3C7JiL')

Delete step

def MonkeyLearn.workflows.steps.delete(model_id, step_id, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
step_id int Step ID.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.workflows.steps.delete('[MODEL_ID]', STEP_ID)

Upload workflow data

def MonkeyLearn.workflows.data.create(model_id, data, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
data list[dict] A list of dicts with the keys described below.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

data dict keys:

Key Description
text A string of the text to upload.
[custom field name] The value for a custom field for this text. The type of the value must be the one specified when the field was created.

Example:

response = ml.workflows.data.create(
    model_id='[MODEL_ID]',
    data=[{'text': 'text 1', 'rating': 3},
          {'text': 'text 2', 'rating': 4}]
)

List workflow data

def MonkeyLearn.workflows.data.list(model_id, batch_id=None, is_processed=None,
                                    sent_to_process_date_from=None, sent_to_process_date_to=None,
                                    page=None, per_page=None, retry_if_throttled=True)

Parameters:

Parameter Type Description
page int The page number to be retrieved.
per_page int The maximum number of items the page should have. The maximum allowed value is 50.
batch_id int The ID of the batch to retrieve. If unspecified, data from all batches is shown.
is_processed bool Whether to return data that has been processed or data that has not been processed yet. If unspecified, both are shown indistinctly.
sent_to_process_date_from str An ISO formatted date which specifies the oldest sent_date of the data to be retrieved.
sent_to_process_date_to str An ISO formatted date which specifies the most recent sent_date of the data to be retrieved.

Example:

response = ml.workflows.data.list('[MODEL_ID]', batch_id=1839, page=1)

Create custom field

def MonkeyLearn.workflows.custom_fields.create(model_id, name, data_type, retry_if_throttled=True)

Parameters:

Parameter Type Description
model_id str Workflow ID. It always starts with 'wf', for example, 'wf_oJNMkt2V'.
name str The name of the new custom field.
data_type str The type of the data of the field. It must be one of string, date, text, integer, float, bool.
retry_if_throttled bool If a request is throttled, sleep and retry the request.

Example:

response = ml.workflows.custom_fields.create(model_id='[MODEL_ID]',  name='rating',
                                             data_type='integer')
Issues
  • Bug  CLASSIFICATION_ENDPOINT

    Bug CLASSIFICATION_ENDPOINT

    >>> from monkeylearn import MonkeyLearn
    >>> ml = MonkeyLearn('API')
    >>> res = ml.classifiers.create('Test Classifier')
    >>> module_id = res.result['classifier']['hashed_id']
    >>> res = ml.classifiers.detail(module_id)
    >>> root_id = res.result['sandbox_categories'][0]['id']
    >>> res = ml.classifiers.categories.create(module_id, 'Negative', root_id)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "C:\py34\lib\site-packages\monkeylearn\classification.py", line 20, in ca
    tegories
        return Categories(self.token)
      File "C:\py34\lib\site-packages\monkeylearn\classification.py", line 110, in _
    _init__
        self.endpoint = CLASSIFICATION_ENDPOINT
    NameError: name 'CLASSIFICATION_ENDPOINT' is not defined
    

    I using Python 3.4.3 64 bit for windows. Thank you.

    opened by wannaphong 2
  • Calls to classify end point seem to use 1 API call per string not per list of strings

    Calls to classify end point seem to use 1 API call per string not per list of strings

    If I understand the "classify" API documentation correctly, I should be able to pass multiple text strings in a single call and only use 1 API call, correct? I'm basing this on

    This endpoint allows you to perform the classification of many text samples using only one request to a custom or public module.

    Assuming my understanding is accurate, I am seeing different behavior. Instead of a single API call for a list of text strings, I see one API call per text string.

    This is my sample code

    from monkeylearn import MonkeyLearn
    import user_settings
    
    MONKEYLEARN_API_KEY = user_settings.KEY
    MODULE_ID = user_settings.MODULE_ID
    
    ml = MonkeyLearn(MONKEYLEARN_API_KEY)
    res = ml.classifiers.detail(MODULE_ID)
    samples = ['this is a string',
               'a dog barks on tuesday',
               'i like purple muffins',
               'apples are funny fruit',
               'the house is up a mountain',
               'cats do not like trees',
               'computers will take over',
               'you sank my battleship',
               'duck, duck, goose',
               'yellow baby buggy bumpers']
    
    res = ml.classifiers.classify(MODULE_ID, samples, sandbox=True, debug=False)
    print("Quota Remaining")
    print("-"*20)
    pprint.pprint(res.query_limit_remaining, indent=4)
    

    Two runs, back to back, of this print the following:

    RUN 1: Quota Remaining

    '99940'


    RUN 2: Quota Remaining

    '99930'

    Notice that the remaining quota is 10 apart between runs. This is the same number of sample text strings. If I comment out 5 of them and run it again, I get this output:

    Quota Remaining

    '99915'

    To me, this is confirming that I am using 1 API call to the classify endpoint per string, not per group of strings.

    Is this the expected behavior, or should my list of 10 strings only be costing 1 API call?

    opened by AWegnerGitHub 2
  • Adding description

    Adding description

    Adding description field

    opened by lowks 1
  • fix CLASSIFICATION_ENDPOINT

    fix CLASSIFICATION_ENDPOINT

    CLASSIFICATION_ENDPOINT to DEFAULT_BASE_ENDPOINT

    opened by wannaphong 1
  • bug Resource not found

    bug Resource not found

    I doing like https://github.com/monkeylearn/monkeylearn-python but , It's have bug.

    >>> from monkeylearn import MonkeyLearn
    >>> ml = MonkeyLearn('API')
    >>> res = ml.classifiers.create('Test Classifier')
    >>> module_id = res.result['classifier']['hashed_id']
    >>> res = ml.classifiers.detail(module_id)
    >>> root_id = res.result['sandbox_categories'][0]['id']
    >>> res = ml.classifiers.categories.create(module_id, 'Negative', root_id)
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "C:\py34\lib\site-packages\monkeylearn\classification.py", line 119, in c
    reate
        self.handle_errors(response)
      File "C:\py34\lib\site-packages\monkeylearn\utils.py", line 78, in handle_erro
    rs
        raise MonkeyLearnException(json.dumps(res['detail']))
    monkeylearn.exceptions.MonkeyLearnException: Error: "Resource not found. Check y
    our URL."
    
    opened by wannaphong 1
  • extraction.py (line 28): AttributeError: 'dict' object has no attribute 'iteritems' in python 3

    extraction.py (line 28): AttributeError: 'dict' object has no attribute 'iteritems' in python 3

    File "extraction.py", line 28, in extract
        for key, value in kwargs.iteritems():
    AttributeError: 'dict' object has no attribute 'iteritems'
    

    A similar issue is raised in http://stackoverflow.com/a/30418498/2952688 iteritems() being deprecated in python 3, items() has to be used instead.

    Commit https://github.com/monkeylearn/monkeylearn-python/commit/f3eee368e13ee37048d52bde0d067efea057fef8 seems to be the culprit.

    opened by RomainBrunias 1
  • Proxy settings

    Proxy settings

    It would be nice to have the possibility to define proxy settings. In corporate environment, the request is not possible easily.

    opened by aj-centexbel 1
  • Python 3 support

    Python 3 support

    opened by gonz 0
  • how to use

    how to use

    how to use plz tutorial language Afsomali

    Somalia, is a country in the Horn of Africa

    opened by Yazkacade 0
Releases(v3.6.0)
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