Merge pull request RasaHQ#1641 from RasaHQ/py3-only

remove python 2 support

.travis.yml

@@ -5,7 +5,6 @@ cache:
   - $HOME/.cache/pip
   - /tmp/cached/
 python:
-  - '2.7'
   - '3.5'
   - '3.6'
 env:

CHANGELOG.rst

@@ -4,6 +4,22 @@ Change Log
 All notable changes to this project will be documented in this file.
 This project adheres to `Semantic Versioning`_ starting with version 0.7.0.
 
+[Unreleased 0.15.0.aX] - `master`_
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Added
+-----
+
+Changed
+-------
+
+Removed
+-------
+- **removed python 2.7 support**
+
+Fixed
+-----
+
 .. _v0-14-0:
 
 [0.14.1] - 2018-01-23

README.md

@@ -1,8 +1,9 @@
 # Rasa NLU
 [![Join the forum at https://forum.rasa.com](https://img.shields.io/badge/forum-join%20discussions-brightgreen.svg)](https://forum.rasa.com/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![PyPI version](https://badge.fury.io/py/rasa-nlu.svg)](https://badge.fury.io/py/rasa-nlu)
+[![Supported Python Versions](https://img.shields.io/pypi/pyversions/rasa_nlu.svg)](https://pypi.python.org/pypi/rasa_nlu)
 [![Build Status](https://travis-ci.com/RasaHQ/rasa_nlu.svg?branch=master)](https://travis-ci.com/RasaHQ/rasa_nlu)
 [![Coverage Status](https://coveralls.io/repos/github/RasaHQ/rasa_nlu/badge.svg?branch=master)](https://coveralls.io/github/RasaHQ/rasa_nlu?branch=master)
-[![PyPI version](https://badge.fury.io/py/rasa-nlu.svg)](https://badge.fury.io/py/rasa-nlu)
 [![Documentation Status](https://img.shields.io/badge/docs-stable-brightgreen.svg)](https://nlu.rasa.com/)
 [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2FRasaHQ%2Frasa_nlu.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2FRasaHQ%2Frasa_nlu?ref=badge_shield)
 
@@ -39,6 +40,13 @@ If you are new to Rasa NLU and want to create a bot, you should start with the [
 
 - **I would like to contribute 🤗** [How to contribute](#how-to-contribute)
 
+### Important Note
+
+Current github master version does NOT support python 2.7 anymore (neither
+will the next major release). If you want to use Rasa NLU with python 
+2.7, please install the most recent version from pypi (0.14).
+
+
 # Quick Install
 
 For the full installation instructions, please head over to the documenation: [Installation](https://nlu.rasa.com/installation.html)

alt_requirements/requirements_bare.txt

@@ -3,7 +3,6 @@ klein==17.10.0
 hyperlink==17.3.1
 typing==3.6.2
 future==0.16.0
-six==1.11.0
 jsonschema==2.6.0
 matplotlib==2.1.0
 requests==2.20.0

docker/Dockerfile_mitie

@@ -1,4 +1,4 @@
-FROM python:2.7-slim
+FROM python:3.6-slim
 
 ENV RASA_NLU_DOCKER="YES" \
     RASA_NLU_HOME=/app \

docs/faq.rst

@@ -6,31 +6,41 @@ Frequently Asked Questions
 
 Which languages does the Rasa NLU support?
 ------------------------------------------
-Rasa NLU can be used to understand any language that can be tokenized (on whitespace or using a custom tokenizer),
+Rasa NLU can be used to understand any language that can be
+tokenized (on whitespace or using a custom tokenizer),
 but some backends are restricted to specific languages.
 
-The ``tensorflow_embedding`` pipeline can be used for any language because it trains custom word embeddings for your domain using the data you provide in the NLU training examples.
+The ``tensorflow_embedding`` pipeline can be used for any language
+because it trains custom word embeddings for your domain using the
+data you provide in the NLU training examples.
 
-Other backends use pre-trained word vectors and therefore are restricted to languages which have pre-trained
-vectors available. 
+Other backends use pre-trained word vectors and therefore are
+restricted to languages which have pre-trained vectors available.
 
-You can read more about the Rasa NLU supported languages in :ref:`section_languages`.
+You can read more about the Rasa NLU supported languages in
+:ref:`section_languages`.
 
 
 How many training examples do I need?
 -------------------------------------
 Unfortunately, the answer is *it depends*. 
 
-A good starting point is to have 10 examples for each intent and build up from there.
+A good starting point is to have 10 examples for each intent
+and build up from there.
 
-If you have intents that are easily confusable, you will need more training data. Accordingly, as you add more
-intents, you also want to add more training examples for each intent. If you quickly write 20-30 unique expressions for
+If you have intents that are easily confusable, you will need more
+training data. Accordingly, as you add more
+intents, you also want to add more training examples for each intent.
+If you quickly write 20-30 unique expressions for
 each intent, you should be good for the beginning.
 
-The same holds true for entities. the number of training examples you will need depends on how closely related your different entity types are and how clearly
-entities are distinguishable from non-entities in your use case.
+The same holds true for entities. the number of training examples you
+will need depends on how closely related your different entity types
+are and how clearly entities are distinguishable from non-entities in
+your use case.
 
-To assess your model's performance, use the :ref:`evaluation script <section_evaluation>`.
+To assess your model's performance, use the
+:ref:`evaluation script <section_evaluation>`.
 
 
 .. _section_faq_version:
@@ -43,26 +53,38 @@ To find out which Rasa version you are running, you can execute
 
    python -c "import rasa_nlu; print(rasa_nlu.__version__);"
 
-If you are using a virtual environment to run your python code, make sure you are using the correct python to execute the above code.
+If you are using a virtual environment to run your python code, make sure
+you are using the correct python to execute the above code.
 
 Why am I getting an ``UndefinedMetricWarning``?
 -----------------------------------------------
-The complete warning is: ``UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.``
-The warning is a result of a lack of training data. During the training the dataset will be splitted multiple times, if there are to few training samples for any of the intents, the splitting might result in splits that do not contain any examples for this intent.
+The complete warning is:
+``UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.``
+The warning is a result of a lack of training data. During the training
+the dataset will be splitted multiple times, if there are to few training
+samples for any of the intents, the splitting might result in splits that
+do not contain any examples for this intent.
 
-Hence, the solution is to add more training samples. As this is only a warning, training will still succeed, but the resulting models predictions might be weak on the intents where you are lacking training data.  
+Hence, the solution is to add more training samples. As this is only a
+warning, training will still succeed, but the resulting models predictions
+might be weak on the intents where you are lacking training data.  
 
 
 I have an issue, can you help me?
 ---------------------------------
-We'd love to help you. If you are unsure if your issue is related to your setup, you should state your problem in the `Rasa Community Forum <https://forum.rasa.com>`_.
-If you found an issue with the framework, please file a report on `github issues <https://github.com/RasaHQ/rasa_nlu/issues>`_
+We'd love to help you. If you are unsure if your issue is related to your
+setup, you should state your problem in the
+`Rasa Community Forum <https://forum.rasa.com>`_.
+If you found an issue with the framework, please file a report on
+`github issues <https://github.com/RasaHQ/rasa_nlu/issues>`_
 including all the information needed to reproduce the problem.
 
 
 Does it run with python 3?
 --------------------------
-Yes it does, Rasa NLU supports python 2.7 as well as python 3.5 and 3.6. If there are any issues with a specific python version, feel free to create an issue or directly provide a fix.
+Yes it does, Rasa NLU supports python 3.5 and 3.6 (supported for python 2.7
+up until version 0.14). If there are any issues with a specific python version,
+feel free to create an issue or directly provide a fix.
 
 
 .. include:: feedback.inc

rasa_nlu/__init__.py

@@ -1,8 +1,3 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import logging
 
 import rasa_nlu.version

rasa_nlu/classifiers/__init__.py

@@ -1,8 +1,3 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 # How many intents are at max put into the output intent
 # ranking, everything else will be cut off
 INTENT_RANKING_LENGTH = 10

rasa_nlu/classifiers/embedding_intent_classifier.py

@@ -1,8 +1,3 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import io
 import copy
 import logging

rasa_nlu/classifiers/keyword_intent_classifier.py

@@ -1,7 +1,3 @@
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
 from builtins import map
 from typing import Any
 from typing import Dict

rasa_nlu/classifiers/mitie_intent_classifier.py

@@ -1,8 +1,3 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import os
 
 import typing

rasa_nlu/classifiers/sklearn_intent_classifier.py

@@ -1,8 +1,3 @@
-from __future__ import unicode_literals
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-
 import logging
 import typing
 from builtins import zip
@@ -124,9 +119,9 @@ def train(self, training_data, cfg, **kwargs):
                   for e in training_data.intent_examples]
 
         if len(set(labels)) < 2:
-            logger.warn("Can not train an intent classifier. "
-                        "Need at least 2 different classes. "
-                        "Skipping training of intent classifier.")
+            logger.warning("Can not train an intent classifier. "
+                           "Need at least 2 different classes. "
+                           "Skipping training of intent classifier.")
         else:
             y = self.transform_labels_str2num(labels)
             X = np.stack([example.get("text_features")

rasa_nlu/components.py

@@ -1,8 +1,3 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import logging
 
 import typing
@@ -43,7 +38,7 @@ def find_unavailable_packages(package_names):
 
 
 def validate_requirements(component_names):
-    # type: (List[Text], Text) -> None
+    # type: (List[Text]) -> None
     """Ensures that all required python packages are installed to
     instantiate and used the passed components."""
     from rasa_nlu import registry
@@ -206,7 +201,7 @@ def load(cls,
              model_dir=None,   # type: Optional[Text]
              model_metadata=None,   # type: Optional[Metadata]
              cached_component=None,   # type: Optional[Component]
-             **kwargs  # type: **Any
+             **kwargs  # type: Any
              ):
         # type: (...) -> Component
         """Load this component from file.
@@ -370,7 +365,6 @@ def __get_cached_component(self, component_name, model_metadata):
 
         Returns the component, if found, and the cache key."""
         from rasa_nlu import registry
-        from rasa_nlu.model import Metadata
 
         component_class = registry.get_component_class(component_name)
         cache_key = component_class.cache_key(model_metadata)
@@ -409,7 +403,6 @@ def load_component(self,
             Component: the loaded component.
         """
         from rasa_nlu import registry
-        from rasa_nlu.model import Metadata
 
         try:
             cached_component, cache_key = self.__get_cached_component(

rasa_nlu/config.py

@@ -1,13 +1,7 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import copy
 import logging
 import os
 
-import six
 import ruamel.yaml as yaml
 from builtins import object
 # Describes where to search for the config file if no location is specified
@@ -118,7 +112,7 @@ def __init__(self, configuration_values=None):
         self.override(DEFAULT_CONFIG)
         self.override(configuration_values)
 
-        if isinstance(self.__dict__['pipeline'], six.string_types):
+        if isinstance(self.__dict__['pipeline'], str):
             from rasa_nlu import registry
 
             template_name = self.__dict__['pipeline']
@@ -188,8 +182,9 @@ def set_component_attr(self, name, **kwargs):
             if c.get("name") == name:
                 c.update(kwargs)
         else:
-            logger.warn("Tried to set configuration value for component '{}' "
-                        "which is not part of the pipeline.".format(name))
+            logger.warning("Tried to set configuration value for component "
+                           "'{}' which is not part of the pipeline."
+                           "".format(name))
 
     def override(self, config):
         if config:

rasa_nlu/convert.py

@@ -1,8 +1,3 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-
 import argparse
 
 from rasa_nlu import training_data