Conversations/summary

CHANGELOG.md

@@ -6,6 +6,7 @@ All notable changes to this project will be documented in this file.
 
 ### Added
 
+- conversations summary extraction pipeline
 - docs and tests for init command
 - docs and tests for automated-import command
 

ChildProject/annotations.py

@@ -11,7 +11,7 @@
 import logging
 
 from . import __version__
-from .pipelines.derivations import DERIVATIONS
+from .pipelines.derivations import DERIVATIONS, conversations
 from .projects import ChildProject
 from .converters import *
 from .tables import IndexTable, IndexColumn, assert_dataframe, assert_columns_presence
@@ -1967,8 +1967,8 @@ def clip_segments(segments: pd.DataFrame, start: int, stop: int) -> pd.DataFrame
         start = int(start)
         stop = int(stop)
 
-        segments["segment_onset"].clip(lower=start, upper=stop, inplace=True)
-        segments["segment_offset"].clip(lower=start, upper=stop, inplace=True)
+        segments["segment_onset"] = segments["segment_onset"].clip(lower=start, upper=stop)
+        segments["segment_offset"] = segments["segment_offset"].clip(lower=start, upper=stop)
 
         segments = segments[segments["segment_offset"] > segments["segment_onset"]]
 

ChildProject/cmdline.py

@@ -1,6 +1,8 @@
 #!/usr/bin/env python3
 from .projects import ChildProject, RAW_RECORDINGS, METADATA_FOLDER, RECORDINGS_CSV, CHILDREN_CSV
 from .annotations import AnnotationManager
+from .pipelines.conversations import ConversationsPipeline
+from .pipelines.conversations import ConversationsSpecificationPipeline
 from .converters import extensions
 from .pipelines.samplers import SamplerPipeline
 from .pipelines.eafbuilder import EafBuilderPipeline
@@ -744,6 +746,8 @@ def main():
     register_pipeline("anonymize", AnonymizationPipeline)
     register_pipeline("metrics", MetricsPipeline)
     register_pipeline("metrics-specification", MetricsSpecificationPipeline)
+    register_pipeline("conversations-summary", ConversationsPipeline)
+    register_pipeline("conversations-specification", ConversationsSpecificationPipeline)
 
     args = parser.parse_args()
     args.func(args)

ChildProject/pipelines/conversationFunctions.py

@@ -0,0 +1,180 @@
+import pandas as pd
+import numpy as np
+import ast
+import re
+import functools
+from typing import Union, Set, Tuple
+
+"""
+This file lists all the metrics functions commonly used.
+New metrics can be added by defining new functions for the Conversations class to use :
+ - Create a new function using the same arguments (i.e. segments, duration, **kwargs)
+ - Define calculation of the metric with:
+     - segments, which is a dataframe containing all the relevant annotated segments  to use. It contains the
+       annotation content (https://childproject.readthedocs.io/en/latest/format.html#id10) joined with the annotation
+       index info (https://childproject.readthedocs.io/en/latest/format.html#id11) as well as any column that was
+       requested to be added to the results by the user using --child-cols or --rec-cols (eg --child-cols child_dob,
+       languages will make columns 'child_dob' and 'languages' available)
+     - duration which is the duration of audio annotated in milliseconds
+     - kwargs, whatever keyword parameter you chose to pass to the function (except 'name', 'callable', 'set' which can 
+       not be used). This will need to be given with the list of metrics when called
+ - Wrap you function with the 'conversationFunction' decorator to make it callable by the pipeline, read conversationFunction help 
+   for more info
+
+!! Metrics functions should still behave and return the correct result when receiving an empty dataframe
+"""
+RESERVED = {'name', 'callable'}  # arguments reserved usage. use other keyword labels.
+
+
+def conversationFunction(args: set = set()):
+    """Decorator for all metrics functions to make them ready to be called by the pipeline.
+
+    :param args: set of required keyword arguments for that function, raise ValueError if were not given \
+    you cannot use keywords [name, callable, set] as they are reserved
+    :type args: set
+    :return: new function to substitute the metric function
+    :rtype: Callable
+    """
+
+    def decorator(function):
+        for a in args:
+            if a in RESERVED:
+                raise ValueError(
+                    'Error when defining {} with required argument {}, you cannot use reserved keywords {},\
+                     change your required argument name'.format(
+                        function.__name__, a, RESERVED))
+
+        @functools.wraps(function)
+        def new_func(segments: pd.DataFrame, **kwargs):
+            for arg in args:
+                if arg not in kwargs:
+                    raise ValueError(f"{function.__name__} metric needs an argument <{arg}>")
+
+            res = function(segments, **kwargs)
+
+            return res
+
+        return new_func
+
+    return decorator
+
+
+@conversationFunction()
+def who_initiated(segments: pd.DataFrame):
+    """speaker type who spoke first in the conversation
+
+    Required keyword arguments:
+    """
+    return segments.iloc[0]['speaker_type']
+
+
+@conversationFunction()
+def who_finished(segments: pd.DataFrame):
+    """speaker type who spoke last in the conversation
+
+    Required keyword arguments:
+    """
+    return segments[segments['segment_offset'] == segments['segment_offset'].max()].iloc[0]['speaker_type']
+
+@conversationFunction()
+def participants(segments: pd.DataFrame):
+    """list of speakers participating in the conversation, '/' separated
+
+    Required keyword arguments:
+    """
+    return '/'.join(segments['speaker_type'].unique())
+
+@conversationFunction()
+def voc_total_dur(segments: pd.DataFrame):
+    """summed duration of all speech in the conversation (ms) N.B. can be higher than conversation duration as
+    speakers may speak at the same time, resulting in multiple spoken segments happening simultaneously
+
+    Required keyword arguments:
+    """
+    return segments['voc_duration'].sum()
+
+
+@conversationFunction({'speaker'})
+def is_speaker(segments: pd.DataFrame, **kwargs):
+    """is a specific speaker type present in the conversation
+
+    Required keyword arguments:
+        - speaker : speaker_type label
+    """
+    return kwargs["speaker"] in segments['speaker_type'].tolist()
+
+
+@conversationFunction({'speaker'})
+def voc_speaker_count(segments: pd.DataFrame, **kwargs):
+    """number of vocalizations produced by a given speaker
+
+    Required keyword arguments:
+        - speaker : speaker_type label
+    """
+    return segments[segments['speaker_type'] == kwargs["speaker"]]['speaker_type'].count()
+
+
+@conversationFunction({'speaker'})
+def voc_speaker_dur(segments: pd.DataFrame, **kwargs):
+    """summed duration of speech for a given speaker in the conversation
+
+    Required keyword arguments:
+        - speaker : speaker_type label
+    """
+    return segments[segments['speaker_type'] == kwargs["speaker"]]['voc_duration'].sum(min_count=1)
+
+
+@conversationFunction({'speaker'})
+def voc_dur_contribution(segments: pd.DataFrame, **kwargs):
+    """contribution of a given speaker in the conversation compared to others, in terms of total speech duration
+
+    Required keyword arguments:
+        - speaker : speaker_type label
+    """
+    speaker_total = segments[segments['speaker_type'] == kwargs["speaker"]]['voc_duration'].sum(min_count=1)
+    total = segments['voc_duration'].sum()
+    return speaker_total / total
+
+
+@conversationFunction()
+def assign_conv_type(segments: pd.DataFrame):
+    """Compute the conversation type (overheard, dyadic_XXX, peer, parent, triadic_XXX, multiparty) depending on the
+    participants
+
+    Required keyword arguments:
+    """
+    #pd.Categorical(['overheard', 'dyadic_FEM', 'dyadic_MAL', 'peer', 'parent', 'triadic_FEM', 'triadic_MAL', 'multiparty'])
+    speaker_present = {}
+    for speaker in ['CHI', 'FEM', 'MAL', 'OCH']:
+        speaker_present[speaker] = [speaker in segments['speaker_type'].tolist()]
+    speaker_df = pd.DataFrame.from_dict(speaker_present).iloc[0, :]
+
+    if not speaker_df['CHI']:
+        return 'overheard'
+
+    elif speaker_df['CHI']:
+        if not speaker_df['OCH'] and speaker_df[['FEM', 'MAL']].sum() == 1:
+            if speaker_df['FEM']:
+                return 'dyadic_FEM'
+
+            if speaker_df['MAL']:
+                return 'dyadic_MAL'
+
+        if speaker_df['OCH'] and speaker_df[['FEM', 'MAL']].sum() == 0:
+            return 'peer'
+
+        if not speaker_df['OCH'] and speaker_df[['FEM', 'MAL']].sum() == 2:
+            return 'parent'
+
+        if speaker_df['OCH'] and speaker_df[['FEM', 'MAL']].sum() == 1:
+            if speaker_df['FEM']:
+                return 'triadic_FEM'
+            if speaker_df['MAL']:
+                return 'triadic_MAL'
+
+        if speaker_df[['OCH', 'FEM', 'MAL']].sum() == 3:
+            return 'multiparty'
+    return np.nan
+
+
+