Convokit 3.0 Mega Pull Request

README.md

@@ -4,13 +4,13 @@
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 [![pypi](https://img.shields.io/pypi/v/convokit.svg)](https://pypi.org/pypi/convokit/)
-[![py\_versions](https://img.shields.io/badge/python-3.7%2B-blue)](https://pypi.org/pypi/convokit/)
+[![py\_versions](https://img.shields.io/badge/python-3.8%2B-blue)](https://pypi.org/pypi/convokit/)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![license](https://img.shields.io/badge/license-MIT-green)](https://github.com/CornellNLP/ConvoKit/blob/master/LICENSE.md)
 [![Slack Community](https://img.shields.io/static/v1?logo=slack&style=flat&color=red&label=slack&message=community)](https://join.slack.com/t/convokit/shared_invite/zt-1axq34qrp-1hDXQrvSXClIbJOqw4S03Q)
 
 
-This toolkit contains tools to extract conversational features and analyze social phenomena in conversations, using a [single unified interface](https://convokit.cornell.edu/documentation/architecture.html) inspired by (and compatible with) scikit-learn.  Several large [conversational datasets](https://github.com/CornellNLP/ConvoKit#datasets) are included together with scripts exemplifying the use of the toolkit on these datasets. The latest version is [2.5.3](https://github.com/CornellNLP/ConvoKit/releases/tag/v2.5.2) (released 16 Jan 2022); follow the [project on GitHub](https://github.com/CornellNLP/ConvoKit) to keep track of updates.
+This toolkit contains tools to extract conversational features and analyze social phenomena in conversations, using a [single unified interface](https://convokit.cornell.edu/documentation/architecture.html) inspired by (and compatible with) scikit-learn.  Several large [conversational datasets](https://github.com/CornellNLP/ConvoKit#datasets) are included together with scripts exemplifying the use of the toolkit on these datasets. The latest version is [3.0.0](https://github.com/CornellNLP/ConvoKit/releases/tag/v3.0.0) (released July 17, 2023); follow the [project on GitHub](https://github.com/CornellNLP/ConvoKit) to keep track of updates.
 
 Read our [documentation](https://convokit.cornell.edu/documentation) or try ConvoKit in our [interactive tutorial](https://colab.research.google.com/github/CornellNLP/ConvoKit/blob/master/examples/Introduction_to_ConvoKit.ipynb).
 

convokit/convokitConfig.py

@@ -4,13 +4,13 @@
 
 
 DEFAULT_CONFIG_CONTENTS = (
-    "# Default Storage Parameters\n"
+    "# Default Backend Parameters\n"
     "db_host: localhost:27017\n"
     "data_directory: ~/.convokit/saved-corpora\n"
-    "default_storage_mode: mem"
+    "default_backend: mem"
 )
 
-ENV_VARS = {"db_host": "CONVOKIT_DB_HOST", "default_storage_mode": "CONVOKIT_STORAGE_MODE"}
+ENV_VARS = {"db_host": "CONVOKIT_DB_HOST", "default_backend": "CONVOKIT_BACKEND"}
 
 
 class ConvoKitConfig:
@@ -52,5 +52,5 @@ def data_directory(self):
         return self.config_contents.get("data_directory", "~/.convokit/saved-corpora")
 
     @property
-    def default_storage_mode(self):
-        return self._get_config_from_env_or_file("default_storage_mode", "mem")
+    def default_backend(self):
+        return self._get_config_from_env_or_file("default_backend", "mem")

convokit/coordination/coordination.py

@@ -1,5 +1,6 @@
 from collections import defaultdict
 from typing import Callable, Tuple, List, Dict, Optional, Collection, Union
+import copy
 
 import pkg_resources
 
@@ -108,11 +109,22 @@ def transform(self, corpus: Corpus) -> Corpus:
             utterance_thresh_func=self.utterance_thresh_func,
         )
 
+        # Keep record of all score update for all (speakers, target) pairs to avoid redundant operations
+        todo = {}
+
         for (speaker, target), score in pair_scores.items():
             if self.coordination_attribute_name not in speaker.meta:
                 speaker.meta[self.coordination_attribute_name] = {}
-            speaker.meta[self.coordination_attribute_name][target.id] = score
-
+            key = (speaker, target.id)
+            todo.update({key: score})
+
+        for key, score in todo.items():
+            speaker = key[0]
+            target = key[1]
+            # For avoiding mutability for the sake of DB corpus
+            temp_dict = copy.deepcopy(speaker.meta[self.coordination_attribute_name])
+            temp_dict[target] = score
+            speaker.meta[self.coordination_attribute_name] = temp_dict
             assert isinstance(speaker, Speaker)
 
         return corpus