diff --git a/.gitignore b/.gitignore index 1862df543..7b369aa15 100644 --- a/.gitignore +++ b/.gitignore @@ -139,3 +139,6 @@ docs/PROJECTS.md # vim *~ + +# sphinx created csvs +docs/source/*.csv diff --git a/.travis.yml b/.travis.yml index daa3d3ff8..abd16447a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,6 +1,5 @@ language: python python: - - "3.6" - "3.7" - "3.8" - "3.9" diff --git a/ChildProject/annotations.py b/ChildProject/annotations.py index 2994c9add..fa03eeab8 100644 --- a/ChildProject/annotations.py +++ b/ChildProject/annotations.py @@ -13,7 +13,7 @@ from .projects import ChildProject from .converters import * from .tables import IndexTable, IndexColumn, assert_dataframe, assert_columns_presence -from .utils import Segment, intersect_ranges, path_is_parent +from .utils import Segment, intersect_ranges, path_is_parent, TimeInterval class AnnotationManager: @@ -1196,7 +1196,7 @@ def get_within_ranges( return pd.concat(stack) if len(stack) else pd.DataFrame() def get_within_time_range( - self, annotations: pd.DataFrame, start_time: str, end_time: str, errors="raise" + self, annotations: pd.DataFrame, interval : TimeInterval, errors="raise" ): """Clip all input annotations within a given HH:MM clock-time range. Those that do not intersect the input time range at all are filtered out. @@ -1227,24 +1227,12 @@ def get_within_time_range( def get_ms_since_midight(dt): return (dt - dt.replace(hour=0, minute=0, second=0)).total_seconds() * 1000 - try: - start_dt = datetime.datetime.strptime(start_time, "%H:%M") - except: - raise ValueError( - f"invalid value for start_time ('{start_time}'); should have HH:MM format instead" - ) - - try: - end_dt = datetime.datetime.strptime(end_time, "%H:%M") - except: - raise ValueError( - f"invalid value for end_time ('{end_time}'); should have HH:MM format instead" - ) - - assert end_dt > start_dt, "end_time must follow start_time" + #assert end_dt > start_dt, "end_time must follow start_time" + # no reason to keep this condition, 23:00 to 03:00 is completely acceptable - start_ts = get_ms_since_midight(start_dt) - end_ts = get_ms_since_midight(end_dt) + if not isinstance(interval, TimeInterval): raise ValueError("interval must be a TimeInterval object") + start_ts = get_ms_since_midight(interval.start) + end_ts = get_ms_since_midight(interval.stop) annotations = annotations.merge( self.project.recordings[["recording_filename", "start_time"]], how="left" @@ -1272,8 +1260,18 @@ def get_ms_since_midight(dt): matches = [] for annotation in annotations.to_dict(orient="records"): + #onsets = np.arange(start_ts, annotation["range_offset_ts"], 86400 * 1000) + #offsets = onsets + (end_ts - start_ts) + onsets = np.arange(start_ts, annotation["range_offset_ts"], 86400 * 1000) - offsets = onsets + (end_ts - start_ts) + offsets = np.arange(end_ts, annotation["range_offset_ts"], 86400 * 1000) + #treat edge cases when the offset is after the end of annotation, onset before start etc + if len(onsets) > 0 and onsets[0] < annotation["range_onset_ts"] : + if len(offsets) > 0 and offsets[0] < annotation["range_onset_ts"]: onsets = onsets[1:] + else : onsets[0] = annotation["range_onset_ts"] + if len(offsets) > 0 and offsets[0] < annotation["range_onset_ts"] : offsets = offsets[1:] + if len(onsets) > 0 and len(offsets) > 0 and onsets[0] > offsets[0] : onsets = np.append(annotation["range_onset_ts"], onsets) + if (len(onsets) > 0 and len(offsets) > 0 and onsets[-1] > offsets[-1]) or len(onsets) > len(offsets) : offsets = np.append(offsets,annotation["range_offset_ts"]) xs = (Segment(onset, offset) for onset, offset in zip(onsets, offsets)) ys = iter( diff --git a/ChildProject/cmdline.py b/ChildProject/cmdline.py index eb6b489b4..e31db1bc6 100755 --- a/ChildProject/cmdline.py +++ b/ChildProject/cmdline.py @@ -566,6 +566,7 @@ def main(): register_pipeline("eaf-builder", EafBuilderPipeline) register_pipeline("anonymize", AnonymizationPipeline) register_pipeline("metrics", MetricsPipeline) + register_pipeline("metrics-specification", MetricsSpecificationPipeline) args = parser.parse_args() args.func(args) diff --git a/ChildProject/pipelines/__init__.py b/ChildProject/pipelines/__init__.py index c201e0b5a..8d06a834b 100644 --- a/ChildProject/pipelines/__init__.py +++ b/ChildProject/pipelines/__init__.py @@ -2,5 +2,6 @@ from .eafbuilder import EafBuilderPipeline from .zooniverse import ZooniversePipeline from .metrics import MetricsPipeline +from .metrics import MetricsSpecificationPipeline from .processors import AudioProcessingPipeline from .anonymize import AnonymizationPipeline diff --git a/ChildProject/pipelines/metrics.py b/ChildProject/pipelines/metrics.py index b1fc585d5..5f874d0a1 100644 --- a/ChildProject/pipelines/metrics.py +++ b/ChildProject/pipelines/metrics.py @@ -1,31 +1,95 @@ from abc import ABC, abstractmethod +import os import argparse import datetime import multiprocessing as mp import numpy as np import pandas as pd from typing import Union, List +import yaml +from git import Repo +from git.exc import InvalidGitRepositoryError import ChildProject from ChildProject.pipelines.pipeline import Pipeline -pipelines = {} +import ChildProject.pipelines.metricsFunctions as metfunc #used by eval +from ..utils import TimeInterval, time_intervals_intersect +pipelines = {} class Metrics(ABC): + """ + Main class for generating metrics from a project object and a list of desired metrics + + :param project: ChildProject instance of the target dataset. + :type project: ChildProject.projects.ChildProject + :param metrics_list: pandas DataFrame containing the desired metrics (metrics functions are in metricsFunctions.py) + :type metrics_list: pd.DataFrame + :param by: unit to extract metric from (recording_filename, experiment, child_id, session_id), defaults to 'recording_filename' + :type by: str, optional + :param recordings: recordings to sample from; if None, all recordings will be sampled, defaults to None + :type recordings: Union[str, List[str], pd.DataFrame], optional + :param from_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None + :type from_time: str, optional + :param to_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None + :type to_time: str, optional + :param rec_cols: columns from recordings.csv to include in the outputted metrics (optional), recording_filename,session_id,child_id,duration are always included if possible and dont need to be specified. Any column that is not unique for a given unit (eg date_iso for a child_id being recorded on multiple days) will output a value + :type rec_cols: str, optional + :param child_cols: columns from children.csv to include in the outputted metrics (optional), None by default + :type child_cols: str, optional + :param period: time units to aggregate (optional); equivalent to ``pandas.Grouper``'s freq argument. + :type period: str, optional + :param threads: amount of threads to run on, defaults to 1 + :type threads: int, optional + """ def __init__( self, project: ChildProject.projects.ChildProject, + metrics_list: pd.DataFrame, by: str = "recording_filename", recordings: Union[str, List[str], pd.DataFrame] = None, from_time: str = None, to_time: str = None, rec_cols: str = None, child_cols: str = None, + period: str = None, + threads: int = 1, ): self.project = project self.am = ChildProject.annotations.AnnotationManager(self.project) + self.threads = int(threads) + + #check that the callable column is either a callable function or a string that can be found as being part of the list of metrics in ChildProject/pipelines/metricsFunctions.py + def check_callable(row): + if callable(row["callable"]): return row["callable"] + if isinstance(row["callable"],str): + try: + f = getattr(metfunc , row["callable"]) + except Exception: + raise ValueError("{} function is not defined and was not found in ChildProject/pipelines/metricsFunctions.py".format(row["callable"])) + return f + else : + raise ValueError("{} cannot be evaluated as a metric, must be a callable object or a string".format(row["callable"])) + + #block checking presence of required columns and evaluates the callable functions + if isinstance(metrics_list, pd.DataFrame): + if ({'callable','set'}).issubset(metrics_list.columns): + metrics_list["callable"] = metrics_list.apply(check_callable,axis=1) + else: + raise ValueError("metrics_list parameter must contain atleast the columns [callable,set]") + else: + raise ValueError("metrics_list parameter must be a pandas DataFrame") + metrics_list.sort_values(by="set",inplace=True) + + for setname in np.unique(metrics_list['set'].values): + if setname not in self.am.annotations["set"].values: + raise ValueError( + f"annotation set '{setname}' was not found in the index; " + "check spelling and make sure the set was properly imported." + ) + self.metrics_list = metrics_list #necessary columns to construct the metrics join_columns = { @@ -80,46 +144,267 @@ def __init__( self.child_cols = None self.by = by + self.period = period + + #build a dataframe with all the periods we will want for each unit + if self.period: + self.periods = pd.interval_range( + start=datetime.datetime(1900, 1, 1, 0, 0, 0, 0), + end=datetime.datetime(1900, 1, 2, 0, 0, 0, 0), + freq=self.period, + closed="both", + ) + self.periods= pd.DataFrame(self.periods.to_tuples().to_list(),columns=['period_start','period_end']) + self.segments = pd.DataFrame() self.recordings = Pipeline.recordings_from_list(recordings) - - self.from_time = from_time - self.to_time = to_time + + if from_time: + try: + self.from_time = datetime.datetime.strptime(from_time, "%H:%M") + except: + raise ValueError(f"invalid value for from_time ('{from_time}'); should have HH:MM format instead") + else: + self.from_time = None + + if to_time: + try: + self.to_time = datetime.datetime.strptime(to_time, "%H:%M") + except: + raise ValueError(f"invalid value for to_time ('{to_time}'); should have HH:MM format instead") + else: + self.to_time = None + + self._initiate_metrics_df() def __init_subclass__(cls, **kwargs): super().__init_subclass__(**kwargs) pipelines[cls.SUBCOMMAND] = cls - @abstractmethod + def _process_unit(self,row): + """for one unit (i.e. 1 {recording|session|child} [period]) compute the list of required metrics and store the results in the current row of self.metrics + + :param row: index and Series of the unit to process, to be modified with the results + :type row: (int , pandas.Series) + :return: Series containing all the computed metrics result for that unit + :rtype: pandas.Series + """ + #row[0] is the index of the row we are processing + #row[1] is the actual Series containing all the metrics for the currently processed line + prev_set = "" + duration_set = 0 + for i, line in self.metrics_list.iterrows(): + curr_set = line["set"] + if prev_set != curr_set: + index, annotations = self.retrieve_segments([curr_set],row[1]) + if index.shape[0]: + duration_set = ( + index["range_offset"] - index["range_onset"] + ).sum() + else : duration_set = 0 + row[1]["duration_{}".format(line["set"])] = duration_set + prev_set = curr_set + + if 'name' in line and not pd.isnull(line["name"]) and line["name"]: #the 'name' column exists and its value is not NaN or '' => use the name given by the user + row[1][line["name"]] = line["callable"](annotations, duration_set, **line.drop(['callable', 'set','name'],errors='ignore').dropna().to_dict())[1] + else : # use the default name of the metric function + name, value = line["callable"](annotations, duration_set, **line.drop(['callable', 'set','name'],errors='ignore').dropna().to_dict()) + row[1][name] = value + + return row[1] + + def extract(self): - pass + """from the initiated self.metrics, compute each row metrics (handles threading) + Once the Metrics class is initialized, call this function to extract the metrics and populate self.metrics + + :return: DataFrame of computed metrics + :rtype: pandas.DataFrame + """ + if self.threads == 1: + self.metrics = pd.DataFrame( + [self._process_unit(row) for row in self.metrics.iterrows()] + ) + else: + with mp.Pool( + processes=self.threads if self.threads >= 1 else mp.cpu_count() + ) as pool: + self.metrics = pd.DataFrame( + pool.map(self._process_unit, self.metrics.iterrows()) + ) + if self.period: + self.metrics['period_start'] = self.metrics['period_start'].dt.strftime('%H:%M:%S') + self.metrics['period_end'] = self.metrics['period_end'].dt.strftime('%H:%M:%S') + return self.metrics - def retrieve_segments(self, sets: List[str], unit: str): - annotations = self.am.annotations[self.am.annotations[self.by] == unit] + def retrieve_segments(self, sets: List[str], row: str): + """from a list of sets and and a row identifying the unit computed, return the relevant annotation segments + + :param sets: List of annotation sets to keep + :type sets: List[str] + :param row: Series storing the unit to compute information + :type row: pandas.Series + :return: relevant annotation DataFrame and index DataFrame + :rtype: (pandas.DataFrame , pandas.DataFrame) + """ + annotations = self.am.annotations[self.am.annotations[self.by] == row[self.by]] annotations = annotations[annotations["set"].isin(sets)] - + if self.from_time and self.to_time: - annotations = self.am.get_within_time_range( - annotations, self.from_time, self.to_time, errors="coerce" - ) + if self.period: + st_hour = row["period_start"] + end_hour = row["period_end"] + intervals = time_intervals_intersect(TimeInterval(self.from_time,self.to_time),TimeInterval(st_hour,end_hour)) + matches = pd.concat([self.am.get_within_time_range(annotations,i) for i in intervals],ignore_index =True) if intervals else pd.DataFrame() + else: + matches = self.am.get_within_time_range( + annotations, TimeInterval(self.from_time,self.to_time)) + elif self.period: + st_hour = row["period_start"] + end_hour = row["period_end"] + matches = self.am.get_within_time_range( + annotations, TimeInterval(st_hour,end_hour)) + else: + matches = annotations - try: - segments = self.am.get_segments(annotations) - except Exception as e: - print(str(e)) + if matches.shape[0]: + segments = self.am.get_segments(matches) + else: + #no annotations for that unit return pd.DataFrame(), pd.DataFrame() # prevent overflows segments["duration"] = ( - (segments["segment_offset"] / 1000 - segments["segment_onset"] / 1000) + (segments["segment_offset"] - segments["segment_onset"]) .astype(float) .fillna(0) ) - return annotations, segments - + return matches, segments + + def _initiate_metrics_df(self): + """builds a dataframe with all the rows necessary and their labels + eg : - one row per child if --by child_id and no --period + - 48 rows if 2 recordings in the corpus --period 1h --by recording_filename + Then the extract() method should populate the dataframe with actual metrics + """ + recordings = self.project.get_recordings_from_list(self.recordings) + self.metrics = pd.DataFrame(recordings[self.by].unique(), columns=[self.by]) + + if self.period: + #if period, use the self.periods dataframe to build all the list of segments per unit + self.metrics["key"]=0 #with old versions of pandas, we are forced to have a common column to do a cross join, we drop the column after + self.periods["key"]=0 + self.metrics = pd.merge(self.metrics,self.periods,on='key',how='outer').drop('key',axis=1) + + #add info for child_id + self.metrics["child_id"] = self.metrics.apply( + lambda row:self.project.recordings[self.project.recordings[self.by] == row[self.by] + ]["child_id"].iloc[0], + axis=1) + + #get and add to dataframe children.csv columns asked + if self.child_cols: + for label in self.child_cols: + self.metrics[label]= self.metrics.apply(lambda row: + self.project.children[ + self.project.children["child_id"] == row["child_id"] + ][label].iloc[0], axis=1) + + #this loop is for the purpose of checking for name duplicates in the metrics + #we do a dry run on the first line with no annotations bc impractical to check in multiprocessing + df = pd.DataFrame() + duration_set = 0 + names = set() + for i, line in self.metrics_list.iterrows(): + if 'name' in line and not pd.isnull(line["name"]) and line["name"]: + name = line["name"] + else : # use the default name of the metric function + name, value = line["callable"](df, duration_set, **line.drop(['callable', 'set','name'],errors='ignore').dropna().to_dict()) + + if name in names: + raise ValueError('the metric name <{}> is used multiple times, make sure it is unique'.format(name)) + else: + names.add(name) + + #checking that columns added by the user are unique (e.g. date_iso may be different when extract by child_id), replace with NA if they are not + def check_unicity(row, label): + value=self.project.recordings[ + self.project.recordings[self.by] == row[self.by] + ][label].drop_duplicates() + #check that there is only one row remaining (ie this column has a unique value for that unit) + if len(value) == 1: + return value.iloc[0] + #otherwise, leave the column as NA + else: + return np.nan + + #get and add to dataframe recordings.csv columns asked + if self.rec_cols: + for label in self.rec_cols: + self.metrics[label] = self.metrics.apply(lambda row : check_unicity(row,label),axis=1) + +class CustomMetrics(Metrics): + """metrics extraction from a csv file. + Extracts a number of metrics listed in a csv file as a dataframe. + the csv file must contain the columns : + - 'callable' which is the name of the wanted metric from the list of available metrics + - 'set' which is the set of annotations to use for that specific metric (make sure this set has the required columns for that metric) + - 'name' is optional, this is the name to give to that metric (if not given, a default name will be attributed) + - any other necessary argument for the given metrics (eg the voc_speaker_ph metric requires the 'speaker' argument: add a column 'speaker' in the csv file and fill its cells for this metric with the wanted value (CHI|FEM|MAL|OCH)) + + :param project: ChildProject instance of the target dataset. + :type project: ChildProject.projects.ChildProject + :param metrics: name of the csv file listing the metrics to extract + :type metrics: str + :param recordings: recordings to sample from; if None, all recordings will be sampled, defaults to None + :type recordings: Union[str, List[str], pd.DataFrame], optional + :param from_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None + :type from_time: str, optional + :param to_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None + :type to_time: str, optional + :param rec_cols: columns from recordings.csv to include in the outputted metrics (optional), recording_filename,session_id,child_id,duration are always included if possible and dont need to be specified. Any column that is not unique for a given unit (eg date_iso for a child_id being recorded on multiple days) will output a value + :type rec_cols: str, optional + :param child_cols: columns from children.csv to include in the outputted metrics (optional), None by default + :type child_cols: str, optional + :param by: units to sample from, defaults to 'recording_filename' + :type by: str, optional + :param period: time units to aggregate (optional); equivalent to ``pandas.Grouper``'s freq argument. + :type period: str, optional + :param threads: amount of threads to run on, defaults to 1 + :type threads: int, optional + """ + + SUBCOMMAND = "custom" + def __init__( + self, + project: ChildProject.projects.ChildProject, + metrics: str, + recordings: Union[str, List[str], pd.DataFrame] = None, + from_time: str = None, + to_time: str = None, + rec_cols: str = None, + child_cols: str = None, + by: str = "recording_filename", + period: str = None, + threads: int = 1, + ): + + metrics_df = pd.read_csv(metrics) + + super().__init__(project, metrics_df, by=by, recordings=recordings, + from_time=from_time, to_time=to_time, rec_cols=rec_cols, + child_cols=child_cols, period=period, threads=threads) + + @staticmethod + def add_parser(subparsers, subcommand): + parser = subparsers.add_parser(subcommand, help="metrics from a csv file") + parser.add_argument("metrics", + help="name if the csv file containing the list of metrics", + ) + class LenaMetrics(Metrics): """LENA metrics extractor. Extracts a number of metrics from the LENA .its annotations. @@ -128,8 +413,6 @@ class LenaMetrics(Metrics): :type project: ChildProject.projects.ChildProject :param set: name of the set associated to the .its annotations :type set: str - :param types: list of LENA vocalization/noise types (e.g. OLN, TVN) - :type types: list :param recordings: recordings to sample from; if None, all recordings will be sampled, defaults to None :type recordings: Union[str, List[str], pd.DataFrame], optional :param from_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None @@ -142,6 +425,8 @@ class LenaMetrics(Metrics): :type child_cols: str, optional :param by: units to sample from, defaults to 'recording_filename' :type by: str, optional + :param period: time units to aggregate (optional); equivalent to ``pandas.Grouper``'s freq argument. + :type period: str, optional :param threads: amount of threads to run on, defaults to 1 :type threads: int, optional """ @@ -152,21 +437,42 @@ def __init__( self, project: ChildProject.projects.ChildProject, set: str, - types: list = [], recordings: Union[str, List[str], pd.DataFrame] = None, from_time: str = None, to_time: str = None, rec_cols: str = None, child_cols: str = None, by: str = "recording_filename", + period: str = None, threads: int = 1, ): - - super().__init__(project, by, recordings, from_time, to_time, rec_cols, child_cols) - self.set = set - self.types = types - self.threads = int(threads) + + METRICS = pd.DataFrame(np.array( + [["voc_speaker_ph",self.set,'FEM'], + ["voc_speaker_ph",self.set,'MAL'], + ["voc_speaker_ph",self.set,'OCH'], + ["voc_speaker_ph",self.set,'CHI'], + ["voc_dur_speaker_ph",self.set,'FEM'], + ["voc_dur_speaker_ph",self.set,'MAL'], + ["voc_dur_speaker_ph",self.set,'OCH'], + ["voc_dur_speaker_ph",self.set,'CHI'], + ["avg_voc_dur_speaker",self.set,'FEM'], + ["avg_voc_dur_speaker",self.set,'MAL'], + ["avg_voc_dur_speaker",self.set,'OCH'], + ["avg_voc_dur_speaker",self.set,'CHI'], + ["wc_speaker_ph",self.set,'FEM'], + ["wc_speaker_ph",self.set,'MAL'], + ["wc_adu_ph",self.set,pd.NA], + ["lp_n",self.set,pd.NA], + ["lp_dur",self.set,pd.NA], + ]), columns=["callable","set","speaker"]) + + super().__init__(project, METRICS, by=by, recordings=recordings, + period=period, from_time=from_time, to_time=to_time, rec_cols=rec_cols, + child_cols=child_cols, threads=threads) + + if self.set not in self.am.annotations["set"].values: raise ValueError( @@ -174,166 +480,10 @@ def __init__( "check spelling and make sure the set was properly imported." ) - def _process_unit(self, unit: str): - import ast - - metrics = {self.by: unit} - annotations, its = self.retrieve_segments([self.set], unit) - - speaker_types = ["FEM", "MAL", "CHI", "OCH"] - adults = ["FEM", "MAL"] - - if "speaker_type" in its.columns: - its = its[ - (its["speaker_type"].isin(speaker_types)) - | (its["lena_speaker"].isin(self.types)) - ] - else: - return metrics - - if len(its) == 0: - return metrics - - unit_duration = ( - annotations["range_offset"] - annotations["range_onset"] - ).sum() / 1000 - - its_agg = its.groupby("speaker_type").agg( - voc_ph=("duration", "count"), - voc_dur_ph=("duration", "sum"), - avg_voc_dur=("duration", "mean"), - wc_ph=("words", "sum"), - ) - - for speaker in speaker_types: - if speaker not in its_agg.index: - continue - - metrics["voc_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * its_agg.loc[speaker, "voc_ph"] - metrics["voc_dur_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * its_agg.loc[speaker, "voc_dur_ph"] - metrics["avg_voc_dur_{}".format(speaker.lower())] = its_agg.loc[ - speaker, "avg_voc_dur" - ] - - if speaker in adults: - metrics["wc_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * its_agg.loc[speaker, "wc_ph"] - - if len(self.types): - its_agg = its.groupby("lena_speaker").agg( - voc_ph=("duration", "count"), - voc_dur_ph=("duration", "sum"), - avg_voc_dur=("duration", "mean"), - ) - - for lena_type in self.types: - if lena_type not in its_agg.index: - continue - - metrics["voc_{}_ph".format(lena_type.lower())] = ( - 3600 / unit_duration - ) * its_agg.loc[lena_type, "voc_ph"] - metrics["voc_dur_{}_ph".format(lena_type.lower())] = ( - 3600 / unit_duration - ) * its_agg.loc[lena_type, "voc_dur_ph"] - metrics["avg_voc_dur_{}".format(lena_type.lower())] = its_agg.loc[ - lena_type, "avg_voc_dur" - ] - - chi = its[its["speaker_type"] == "CHI"] - cries = chi["cries"].apply(lambda x: len(ast.literal_eval(x))).sum() - vfxs = chi["vfxs"].apply(lambda x: len(ast.literal_eval(x))).sum() - utterances = chi["utterances_count"].sum() - - metrics["lp_n"] = utterances / (utterances + cries + vfxs) - metrics["lp_dur"] = chi["utterances_length"].sum() / ( - chi["child_cry_vfx_len"].sum() + chi["utterances_length"].sum() - ) - - metrics["wc_adu_ph"] = its["words"].sum() * 3600 / unit_duration - - #add info for child_id and duration to the dataframe - metrics["child_id"] = self.project.recordings[ - self.project.recordings[self.by] == unit - ]["child_id"].iloc[0] - metrics["duration"] = unit_duration - - #get and add to dataframe children.csv columns asked - if self.child_cols: - for label in self.child_cols: - metrics[label]=self.project.children[ - self.project.children["child_id"] == metrics["child_id"] - ][label].iloc[0] - - #get and add to dataframe recordings.csv columns asked - if self.rec_cols: - for label in self.rec_cols: - #for every unit drop the duplicates for that column - value=self.project.recordings[ - self.project.recordings[self.by] == unit - ][label].drop_duplicates() - #check that there is only one row remaining (ie this column has a unique value for that unit) - if len(value) == 1: - metrics[label]=value.iloc[0] - #otherwise, leave the column as NA - else: - metrics[label]="NA" - - return metrics - - def extract(self): - recordings = self.project.get_recordings_from_list(self.recordings) - - if self.threads == 1: - self.metrics = pd.DataFrame( - [self._process_unit(unit) for unit in recordings[self.by].unique()] - ) - else: - with mp.Pool( - processes=self.threads if self.threads >= 1 else mp.cpu_count() - ) as pool: - self.metrics = pd.DataFrame( - pool.map(self._process_unit, recordings[self.by].unique()) - ) - - self.metrics.set_index(self.by, inplace=True) - return self.metrics - @staticmethod def add_parser(subparsers, subcommand): parser = subparsers.add_parser(subcommand, help="LENA metrics") parser.add_argument("set", help="name of the LENA its annotations set") - parser.add_argument( - "--types", - help="list of LENA vocalizaton types to include", - choices=[ - "TVF", - "FAN", - "OLN", - "SIL", - "NOF", - "CXF", - "OLF", - "CHF", - "MAF", - "TVN", - "NON", - "CXN", - "CHN", - "MAN", - "FAF", - ], - nargs="+", default=[], - ) - parser.add_argument( - "--threads", help="amount of threads to run on", default=1, type=int - ) - class AclewMetrics(Metrics): """ACLEW metrics extractor. @@ -363,6 +513,8 @@ class AclewMetrics(Metrics): :type child_cols: str, optional :param by: units to sample from, defaults to 'recording_filename' :type by: str, optional + :param period: time units to aggregate (optional); equivalent to ``pandas.Grouper``'s freq argument. + :type period: str, optional :param threads: amount of threads to run on, defaults to 1 :type threads: int, optional """ @@ -380,206 +532,71 @@ def __init__( to_time: str = None, rec_cols: str = None, child_cols: str = None, + period: str = None, by: str = "recording_filename", threads: int = 1, ): - - super().__init__(project, by, recordings, from_time, to_time, rec_cols, child_cols) - + self.vtc = vtc self.alice = alice self.vcm = vcm - self.threads = int(threads) - if self.vtc not in self.am.annotations["set"].values: - raise ValueError( - f"The VTC set '{self.vtc}' was not found in the index; " - "check spelling and make sure the set was properly imported." - ) - - if self.alice not in self.am.annotations["set"].values: + am = ChildProject.annotations.AnnotationManager(project) #temporary instance to check for existing sets. This is suboptimal because an annotation manager will be created by Metrics. However, the metrics classe raises a ValueError for every set passed that does not exist, here we want to check in advance which of the alice and vcm sets exist without raising an error + + METRICS = np.array( + [["voc_speaker_ph",self.vtc,'FEM'], + ["voc_speaker_ph",self.vtc,'MAL'], + ["voc_speaker_ph",self.vtc,'OCH'], + ["voc_speaker_ph",self.vtc,'CHI'], + ["voc_dur_speaker_ph",self.vtc,'FEM'], + ["voc_dur_speaker_ph",self.vtc,'MAL'], + ["voc_dur_speaker_ph",self.vtc,'OCH'], + ["voc_dur_speaker_ph",self.vtc,'CHI'], + ["avg_voc_dur_speaker",self.vtc,'FEM'], + ["avg_voc_dur_speaker",self.vtc,'MAL'], + ["avg_voc_dur_speaker",self.vtc,'OCH'], + ["avg_voc_dur_speaker",self.vtc,'CHI'], + ]) + + if self.alice not in am.annotations["set"].values: print(f"The ALICE set ('{self.alice}') was not found in the index.") - - if self.vcm not in self.am.annotations["set"].values: - print(f"The VCM set ('{self.vcm}') was not found in the index.") - - def _process_unit(self, unit: str): - metrics = {self.by: unit} - annotations, segments = self.retrieve_segments( - [self.vtc, self.alice, self.vcm], unit - ) - - speaker_types = ["FEM", "MAL", "CHI", "OCH"] - adults = ["FEM", "MAL"] - - if "speaker_type" in segments.columns: - segments = segments[segments["speaker_type"].isin(speaker_types)] else: - return metrics - - if len(segments) == 0: - return metrics - - vtc_ann = annotations[annotations["set"] == self.vtc] - unit_duration = (vtc_ann["range_offset"] - vtc_ann["range_onset"]).sum() / 1000 - - vtc = segments[segments["set"] == self.vtc] - alice = segments[segments["set"] == self.alice] - vcm = segments[segments["set"] == self.vcm] - - vtc_agg = vtc.groupby("speaker_type").agg( - voc_ph=("duration", "count"), - voc_dur_ph=("duration", "sum"), - avg_voc_dur=("duration", "mean"), - ) - - for speaker in speaker_types: - if speaker not in vtc_agg.index: - continue - - metrics["voc_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * vtc_agg.loc[speaker, "voc_ph"] - metrics["voc_dur_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * vtc_agg.loc[speaker, "voc_dur_ph"] - metrics["avg_voc_dur_{}".format(speaker.lower())] = vtc_agg.loc[ - speaker, "avg_voc_dur" - ] - - if len(alice): - alice_agg = alice.groupby("speaker_type").agg( - wc_ph=("words", "sum"), - sc_ph=("syllables", "sum"), - pc_ph=("phonemes", "sum"), - ) - - for speaker in adults: - if speaker not in alice_agg.index: - continue - - metrics["wc_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * alice_agg.loc[speaker, "wc_ph"] - metrics["sc_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * alice_agg.loc[speaker, "sc_ph"] - metrics["pc_{}_ph".format(speaker.lower())] = ( - 3600 / unit_duration - ) * alice_agg.loc[speaker, "pc_ph"] - - metrics["wc_adu_ph"] = alice["words"].sum() * 3600 / unit_duration - metrics["sc_adu_ph"] = alice["syllables"].sum() * 3600 / unit_duration - metrics["pc_adu_ph"] = alice["phonemes"].sum() * 3600 / unit_duration - - if len(vcm): - vcm_agg = ( - vcm[vcm["speaker_type"] == "CHI"] - .groupby("vcm_type") - .agg( - voc_chi_ph=("duration", "count"), - voc_dur_chi_ph=("duration", "sum",), - avg_voc_dur_chi=("duration", "mean"), - ) - ) - - metrics["cry_voc_chi_ph"] = (3600 / unit_duration) * ( - vcm_agg.loc["Y", "voc_chi_ph"] if "Y" in vcm_agg.index else 0 - ) - metrics["cry_voc_dur_chi_ph"] = (3600 / unit_duration) * ( - vcm_agg.loc["Y", "voc_dur_chi_ph"] if "Y" in vcm_agg.index else 0 - ) - - if "Y" in vcm_agg.index: - metrics["avg_cry_voc_dur_chi"] = (3600 / unit_duration) * vcm_agg.loc[ - "Y", "avg_voc_dur_chi" - ] - - metrics["can_voc_chi_ph"] = (3600 / unit_duration) * ( - vcm_agg.loc["C", "voc_chi_ph"] if "C" in vcm_agg.index else 0 - ) - metrics["can_voc_dur_chi_ph"] = (3600 / unit_duration) * ( - vcm_agg.loc["C", "voc_dur_chi_ph"] if "C" in vcm_agg.index else 0 - ) - - if "C" in vcm_agg.index: - metrics["avg_can_voc_dur_chi"] = (3600 / unit_duration) * vcm_agg.loc[ - "C", "avg_voc_dur_chi" - ] - - metrics["non_can_voc_chi_ph"] = (3600 / unit_duration) * ( - vcm_agg.loc["N", "voc_chi_ph"] if "N" in vcm_agg.index else 0 - ) - metrics["non_can_voc_dur_chi_ph"] = (3600 / unit_duration) * ( - vcm_agg.loc["N", "voc_dur_chi_ph"] if "N" in vcm_agg.index else 0 - ) - - if "N" in vcm_agg.index: - metrics["avg_non_can_voc_dur_chi"] = ( - 3600 / unit_duration - ) * vcm_agg.loc["N", "avg_voc_dur_chi"] - - speech_voc = metrics["can_voc_chi_ph"] + metrics["non_can_voc_chi_ph"] - speech_dur = ( - metrics["can_voc_dur_chi_ph"] + metrics["non_can_voc_dur_chi_ph"] - ) - - cry_voc = metrics["cry_voc_chi_ph"] - cry_dur = metrics["cry_voc_dur_chi_ph"] - - if speech_voc + cry_voc: - metrics["lp_n"] = speech_voc / (speech_voc + cry_voc) - metrics["cp_n"] = metrics["can_voc_chi_ph"] / speech_voc - - metrics["lp_dur"] = speech_dur / (speech_dur + cry_dur) - metrics["cp_dur"] = metrics["can_voc_dur_chi_ph"] / speech_dur - - #get child_id and duration that are always given - metrics["child_id"] = self.project.recordings[ - self.project.recordings[self.by] == unit - ]["child_id"].iloc[0] - metrics["duration"] = unit_duration - - #get and add to dataframe children.csv columns asked - if self.child_cols: - for label in self.child_cols: - metrics[label]=self.project.children[ - self.project.children["child_id"] == metrics["child_id"] - ][label].iloc[0] - - #get and add to dataframe recordings.csv columns asked - if self.rec_cols: - for label in self.rec_cols: - #for every unit drop the duplicates for that column - value=self.project.recordings[ - self.project.recordings[self.by] == unit - ][label].drop_duplicates() - #check that there is only one row remaining (ie this column has a unique value for that unit) - if len(value) == 1: - metrics[label]=value.iloc[0] - #otherwise, leave the column as NA - else: - metrics[label]="NA" - - return metrics - - def extract(self): - recordings = self.project.get_recordings_from_list(self.recordings) - - if self.threads == 1: - self.metrics = pd.DataFrame( - [self._process_unit(unit) for unit in recordings[self.by].unique()] - ) + METRICS = np.concatenate((METRICS,np.array( + [["wc_speaker_ph",self.alice,'FEM'], + ["wc_speaker_ph",self.alice,'MAL'], + ["sc_speaker_ph",self.alice,'FEM'], + ["sc_speaker_ph",self.alice,'MAL'], + ["pc_speaker_ph",self.alice,'FEM'], + ["pc_speaker_ph",self.alice,'MAL'], + ["wc_adu_ph",self.alice,pd.NA], + ["sc_adu_ph",self.alice,pd.NA], + ["pc_adu_ph",self.alice,pd.NA], + ]))) + + if self.vcm not in am.annotations["set"].values: + print(f"The vcm set ('{self.vcm}') was not found in the index.") else: - with mp.Pool( - processes=self.threads if self.threads >= 1 else mp.cpu_count() - ) as pool: - self.metrics = pd.DataFrame( - pool.map(self._process_unit, recordings[self.by].unique()) - ) + METRICS = np.concatenate((METRICS,np.array( + [["cry_voc_speaker_ph",self.vcm,'CHI'], + ["cry_voc_dur_speaker_ph",self.vcm,'CHI'], + ["avg_cry_voc_dur_speaker",self.vcm,'CHI'], + ["can_voc_speaker_ph",self.vcm,'CHI'], + ["can_voc_dur_speaker_ph",self.vcm,'CHI'], + ["avg_can_voc_dur_speaker",self.vcm,'CHI'], + ["non_can_voc_speaker_ph",self.vcm,'CHI'], + ["non_can_voc_dur_speaker_ph",self.vcm,'CHI'], + ["avg_non_can_voc_dur_speaker",self.vcm,'CHI'], + ["lp_n",self.vcm,pd.NA], + ["lp_dur",self.vcm,pd.NA], + ["cp_n",self.vcm,pd.NA], + ["cp_dur",self.vcm,pd.NA], + ]))) + + METRICS = pd.DataFrame(METRICS, columns=["callable","set","speaker"]) - self.metrics.set_index(self.by, inplace=True) - return self.metrics + super().__init__(project, METRICS,by=by, recordings=recordings, + period=period, from_time=from_time, to_time=to_time, + rec_cols=rec_cols, child_cols=child_cols, threads=threads) @staticmethod def add_parser(subparsers, subcommand): @@ -587,266 +604,6 @@ def add_parser(subparsers, subcommand): parser.add_argument("--vtc", help="vtc set", default="vtc") parser.add_argument("--alice", help="alice set", default="alice") parser.add_argument("--vcm", help="vcm set", default="vcm") - parser.add_argument( - "--threads", help="amount of threads to run on", default=1, type=int - ) - - -class PeriodMetrics(Metrics): - """Time-aggregated metrics extractor. - - Aggregates vocalizations for each time-of-the-day-unit based on a period specified by the user. - For instance, if the period is set to ``15Min`` (i.e. 15 minutes), vocalization rates will be reported for each - recording and time-unit (e.g. 09:00 to 09:15, 09:15 to 09:30, etc.). - - The output dataframe has ``rp`` rows, where ``r`` is the amount of recordings (or children if the ``--by`` option is set to ``child_id``), ``p`` is the - amount of time-bins per day (i.e. 24 x 4 = 96 for a 15-minute period). - - The output dataframe includes a ``period`` column that contains the onset of each time-unit in HH:MM:SS format. - The ``duration`` columns contains the total amount of annotations covering each time-bin, in milliseconds. - - If ``--by`` is set to e.g. ``child_id``, then the values for each time-bin will be the average rates across - all the recordings of every child. - - :param project: ChildProject instance of the target dataset - :type project: ChildProject.projects.ChildProject - :param set: name of the set of annotations to derive the metrics from - :type set: str - :param period: Time-period. Values should be formatted as `pandas offset aliases `__. For instance, `15Min` corresponds to a 15 minute period; `2H` corresponds to a 2 hour period. - :type period: str - :param period_origin: NotImplemented, defaults to None - :type period_origin: str, optional - :param recordings: white-list of recordings to process, defaults to None - :type recordings: Union[str, List[str], pd.DataFrame], optional - :param from_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None - :type from_time: str, optional - :param to_time: If specified (in HH:MM format), ignore annotations outside of the given time-range, defaults to None - :type to_time: str, optional - :param rec_cols: columns from recordings.csv to include in the outputted metrics (optional), recording_filename,session_id,child_id,duration are always included if possible and dont need to be specified. Any column that is not unique for a given unit (eg date_iso for a child_id being recorded on multiple days) will output a value - :type rec_cols: str, optional - :param child_cols: columns from children.csv to include in the outputted metrics (optional), None by default - :type child_cols: str, optional - :param by: units to sample from, defaults to 'recording_filename' - :type by: str, optional - :param threads: amount of threads to run on, defaults to 1 - :type threads: int, optional - """ - - SUBCOMMAND = "period" - - def __init__( - self, - project: ChildProject.projects.ChildProject, - set: str, - period: str, - period_origin: str = None, - recordings: Union[str, List[str], pd.DataFrame] = None, - from_time: str = None, - to_time: str = None, - rec_cols: str = None, - child_cols: str = None, - by: str = "recording_filename", - threads: int = 1, - ): - - super().__init__(project, by, recordings, from_time, to_time, rec_cols, child_cols) - - self.set = set - self.threads = int(threads) - - self.period = period - self.period_origin = period_origin - - if self.period_origin is not None: - raise NotImplementedError("period-origin is not supported yet") - - if self.set not in self.am.annotations["set"].values: - raise ValueError( - f"'{self.set}' was not found in the index; " - "check spelling and make sure the set was properly imported." - ) - - self.periods = pd.date_range( - start=datetime.datetime(1900, 1, 1, 0, 0, 0, 0), - end=datetime.datetime(1900, 1, 2, 0, 0, 0, 0), - freq=self.period, - closed="left", - ) - - def _process_unit(self, unit: str): - annotations, segments = self.retrieve_segments([self.set], unit) - - # retrieve timestamps for each vocalization, ignoring the day of occurence - segments = self.am.get_segments_timestamps(segments, ignore_date=True) - - # dropping segments for which no time information is available - segments.dropna(subset=["onset_time"], inplace=True) - - # update the timestamps so that all vocalizations appear - # to happen on the same day - segments["onset_time"] -= pd.to_timedelta( - 86400 - * ((segments["onset_time"] - self.periods[0]).dt.total_seconds() // 86400), - unit="s", - ) - - if len(segments) == 0: - return pd.DataFrame() - - # calculate length of available annotations within each bin. - # this is necessary in order to calculate correct rates - bins = np.array( - [dt.total_seconds() for dt in self.periods - self.periods[0]] + [86400] - ) - - # retrieve the timestamps for all annotated portions of the recordings - annotations = self.am.get_segments_timestamps( - annotations, ignore_date=True, onset="range_onset", offset="range_offset" - ) - - # calculate time elapsed since the first time bin - annotations["onset_time"] = ( - annotations["onset_time"] - .apply(lambda dt: (dt - self.periods[0]).total_seconds()) - .astype(int) - ) - annotations["offset_time"] = ( - annotations["offset_time"] - .apply(lambda dt: (dt - self.periods[0]).total_seconds()) - .astype(int) - ) - - # split annotations to intervals each within a 0-24h range - annotations["stops"] = annotations.apply( - lambda row: [row["onset_time"]] - + list( - 86400 - * np.arange( - (row["onset_time"] // 86400) + 1, - (row["offset_time"] // 86400) + 1, - 1, - ) - ) - + [row["offset_time"]], - axis=1, - ) - - annotations = annotations.explode("stops") - annotations["onset"] = annotations["stops"] - annotations["offset"] = annotations["stops"].shift(-1) - - annotations.dropna(subset=["offset"], inplace=True) - annotations["onset"] = annotations["onset"].astype(int) % 86400 - annotations["offset"] = (annotations["offset"] - 1e-4) % 86400 - - durations = [ - ( - annotations["offset"].clip(bins[i], bins[i + 1]) - - annotations["onset"].clip(bins[i], bins[i + 1]) - ).sum() - for i, t in enumerate(bins[:-1]) - ] - - durations = pd.Series(durations, index=self.periods) - metrics = pd.DataFrame(index=self.periods) - - grouper = pd.Grouper(key="onset_time", freq=self.period, closed="left") - - speaker_types = ["FEM", "MAL", "CHI", "OCH"] - adults = ["FEM", "MAL"] - - for speaker in speaker_types: - vocs = segments[segments["speaker_type"] == speaker].groupby(grouper) - - vocs = vocs.agg( - voc_ph=("segment_onset", "count"), - voc_dur_ph=("duration", "sum"), - avg_voc_dur=("duration", "mean"), - ) - - metrics["voc_{}_ph".format(speaker.lower())] = ( - vocs["voc_ph"].reindex(self.periods, fill_value=0) * 3600 / durations - ) - metrics["voc_dur_{}_ph".format(speaker.lower())] = ( - vocs["voc_dur_ph"].reindex(self.periods, fill_value=0) - * 3600 - / durations - ) - metrics["avg_voc_dur_{}".format(speaker.lower())] = vocs[ - "avg_voc_dur" - ].reindex(self.periods) - - #add duration and child_id to dataframe as they are always given - metrics["duration"] = (durations * 1000).astype(int) - metrics[self.by] = unit - metrics["child_id"] = self.project.recordings[ - self.project.recordings[self.by] == unit - ]["child_id"].iloc[0] - - #get and add to dataframe children.csv columns asked - if self.child_cols: - for label in self.child_cols: - metrics[label]=self.project.children[ - self.project.children["child_id"] == metrics["child_id"].iloc[0] - ][label].iloc[0] - - #get and add to dataframe recordings.csv columns asked - if self.rec_cols: - for label in self.rec_cols: - #for every unit drop the duplicates for that column - value=self.project.recordings[ - self.project.recordings[self.by] == unit - ][label].drop_duplicates() - #check that there is only one row remaining (ie this column has a unique value for that unit) - if len(value) == 1: - metrics[label]=value.iloc[0] - #otherwise, leave the column as NA - else: - metrics[label]="NA" - - return metrics - - def extract(self): - recordings = self.project.get_recordings_from_list(self.recordings) - - if self.threads == 1: - self.metrics = pd.concat( - [self._process_unit(unit) for unit in recordings[self.by].unique()] - ) - else: - with mp.Pool( - processes=self.threads if self.threads >= 1 else mp.cpu_count() - ) as pool: - self.metrics = pd.concat( - pool.map(self._process_unit, recordings[self.by].unique()) - ) - - if len(self.metrics): - self.metrics["period"] = self.metrics.index.strftime("%H:%M:%S") - self.metrics.set_index(self.by, inplace=True) - - return self.metrics - - @staticmethod - def add_parser(subparsers, subcommand): - parser = subparsers.add_parser(subcommand, help="LENA metrics") - parser.add_argument("--set", help="annotations set", required=True) - - parser.add_argument( - "--period", - help="time units to aggregate (optional); equivalent to ``pandas.Grouper``'s freq argument.", - required=True, - ) - - parser.add_argument( - "--period-origin", - help="time origin of each time period; equivalent to ``pandas.Grouper``'s origin argument.", - default=None, - ) - - parser.add_argument( - "--threads", help="amount of threads to run on", default=1, type=int - ) class MetricsPipeline(Pipeline): @@ -854,8 +611,26 @@ def __init__(self): self.metrics = [] def run(self, path, destination, pipeline, func=None, **kwargs): + self.destination = destination + #build a dictionary with all parameters used + parameters = locals() + parameters = { + key: parameters[key] + for key in parameters + if key not in ["self", "kwargs", "func"] #not sure what func parameter is for, seems unecessary to keep + } + for key in kwargs: #add all kwargs to dictionary + parameters[key] = kwargs[key] + self.project = ChildProject.projects.ChildProject(path) self.project.read() + + try: + datarepo = Repo(path) + parameters['dataset_hash'] = datarepo.head.object.hexsha + except InvalidGitRepositoryError: + print("Your dataset is not currently a git repository") + if pipeline not in pipelines: raise NotImplementedError(f"invalid pipeline '{pipeline}'") @@ -864,7 +639,25 @@ def run(self, path, destination, pipeline, func=None, **kwargs): metrics.extract() self.metrics = metrics.metrics - self.metrics.to_csv(destination) + self.metrics.to_csv(self.destination,index=False) + + # get the df of metrics used from the Metrics class + metrics_df = metrics.metrics_list + metrics_df['callable'] = metrics_df.apply(lambda row: row['callable'].__name__, axis=1) #from the callables used, find their name back + parameters['metrics_list'] = [ {k:v for k,v in m.items() if pd.notnull(v)} for m in metrics_df.to_dict(orient='records')] + date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + # create a yaml file with all the parameters used + self.parameters_path = os.path.splitext(self.destination)[0] + "_parameters_{}.yml".format(date) + print("exported metrics to {}".format(self.destination)) + yaml.dump( + { + "package_version": ChildProject.__version__, + "date": date, + "parameters": parameters, + }, + open(self.parameters_path, "w+"),sort_keys=False, + ) + print("exported sampler parameters to {}".format(self.parameters_path)) return self.metrics @@ -886,9 +679,15 @@ def setup_parser(parser): parser.add_argument( "--by", help="units to sample from (default behavior is to sample by recording)", - choices=["recording_filename", "session_id", "child_id"], + choices=["recording_filename", "session_id", "child_id","experiment"], default="recording_filename", ) + + parser.add_argument( + "--period", + help="time units to aggregate (optional); equivalent to ``pandas.Grouper``'s freq argument. The resulting metrics will be split for each unit across all the resulting periods.", + default=None, + ) parser.add_argument( "-f", @@ -912,6 +711,93 @@ def setup_parser(parser): parser.add_argument( "--child-cols", - help="columns from children.csv to include in the outputted metrics (optional)", + help="columns from children.csv to include in the outputted metrics (optional), NA if ambiguous", default=None, ) + + + parser.add_argument( + "--threads", help="amount of threads to run on", default=1, type=int + ) + + +class MetricsSpecificationPipeline(Pipeline): + def __init__(self): + self.metrics = [] + + def run(self, parameters_input): + #build a dictionary with all parameters used + parameters = None + with open(parameters_input, "r") as stream: + try: + parameters = yaml.safe_load(stream) + if 'parameters' in parameters: parameters = parameters['parameters'] + except yaml.YAMLError as exc: + raise yaml.YAMLError("parsing of the parameters file {} failed. See above exception for more details".format(parameters_input)) from exc + + if parameters: + if "path" not in parameters : + raise ValueError("the parameter file {} must contain at least the 'path' key specifying the path to the dataset".format(parameters_input)) + if "destination" not in parameters : + raise ValueError("the parameter file {} must contain the 'destination' key specifying the file to output the metrics to".format(parameters_input)) + if "metrics_list" not in parameters : + raise ValueError("the parameter file {} must contain the 'metrics_list' key containing the list of the desired metrics".format(parameters_input)) + try: + metrics_df = pd.DataFrame(parameters["metrics_list"]) + except Exception as e: + raise ValueError("The 'metrics_list' key in {} must be a list of elements".format(parameters_input)) from e + else: + raise ValueError("could not find any parameters in {}".format(parameters_input)) + + try: + datarepo = Repo(parameters["path"]) + parameters['dataset_hash'] = datarepo.head.object.hexsha + except InvalidGitRepositoryError: + print("Your dataset is not currently a git repository") + + self.project = ChildProject.projects.ChildProject(parameters["path"]) + self.project.read() + + self.destination = parameters['destination'] + + unwanted_keys = {'metrics', 'pipeline'} + for i in unwanted_keys: + if i in parameters : del parameters[i] + + arguments = { + key: parameters[key] + for key in parameters + if key not in {"metrics_list", "path", "destination","dataset_hash"} + } + try: + metrics = Metrics(self.project, metrics_df, **arguments) + except TypeError as e: + raise ValueError('Unrecognized parameter found {}'.format(e.args[0][46:])) from e + metrics.extract() + + self.metrics = metrics.metrics + self.metrics.to_csv(self.destination,index=False) + + # get the df of metrics used from the Metrics class + metrics_df = metrics.metrics_list + metrics_df['callable'] = metrics_df.apply(lambda row: row['callable'].__name__, axis=1) #from the callables used, find their name back + parameters['metrics_list'] = [ {k:v for k,v in m.items() if pd.notnull(v)} for m in metrics_df.to_dict(orient='records')] + date = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + # create a yaml file with all the parameters used + self.parameters_path = os.path.splitext(self.destination)[0] + "_parameters_{}.yml".format(date) + print("exported metrics to {}".format(self.destination)) + yaml.dump( + { + "package_version": ChildProject.__version__, + "date": date, + "parameters": parameters, + }, + open(self.parameters_path, "w+"),sort_keys=False, + ) + print("exported metrics parameters to {}".format(self.parameters_path)) + + return self.metrics + + @staticmethod + def setup_parser(parser): + parser.add_argument("path", help="path to the yml file with all parameters") \ No newline at end of file diff --git a/ChildProject/pipelines/metricsFunctions.py b/ChildProject/pipelines/metricsFunctions.py new file mode 100644 index 000000000..42a15f3d0 --- /dev/null +++ b/ChildProject/pipelines/metricsFunctions.py @@ -0,0 +1,301 @@ +import pandas as pd +import numpy as np +import ast +import re +import functools +""" +This file lists all the metrics functions commonly used. +New metrics can be added by defining new functions for the Metrics class to use : + - Create a new function using the same arguments (i.e. annotations, duration, **kwargs) + - Define calculation of the metric with: + - annotations, 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 'languaes' 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 'metricFunction' decorator to make it callable by the pipeline, read metricFunction help for more info +""" + +#error message in case of missing columns in annotations +MISSING_COLUMNS = 'The given set <{}> does not have the required column <{}> for computing the {} metric' + +def metricFunction(args: set, columns: set, emptyValue = 0, name : str = None): + """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 + :type args: set + :param columns: set of required columns in the dataframe given, missing columns raise ValueError + :type columns: set + :param name: default name to use for the metric in the resulting dataframe. Every keyword argument found in the name will be replaced by its value (e.g. 'voc_speaker_ph' uses kwarg 'speaker' so if speaker = 'CHI', name will be 'voc_chi_ph'). if no name is given, the __name__ of the function is used + :type name: str + :param emptyValue: value to return when annotations are empty but the unit was annotated (e.g. 0 for counts like voc_speaker_ph , None for proportions like lp_n) + :return: new function to substitute the metric function + :rtype: Callable + """ + def decorator(function): + @functools.wraps(function) + def new_func(annotations: pd.DataFrame, duration: int, **kwargs): + for arg in args: + if arg not in kwargs : raise ValueError('{} metric needs an argument <{}>'.format(function.__name__,arg)) + metname = name + if not name : metname = function.__name__ + for arg in kwargs: + metname = re.sub(arg , str(kwargs[arg]).lower(),metname) + if annotations.shape[0]: + for column in columns: + if column not in annotations.columns : raise ValueError(MISSING_COLUMNS.format(annotations['set'].iloc[0],column,'voc_speaker_ph')) + res = function(annotations, duration, **kwargs) + else: #no annotation for that unit + res = emptyValue if duration else None #duration != 0 => was annotated but not segments there + return metname, res + return new_func + return decorator + + + +@metricFunction({"speaker"},{"speaker_type"}) +def voc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of vocalizations per hour for a given speaker type + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations[annotations["speaker_type"]== kwargs["speaker"]].shape[0] * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","duration"}) +def voc_dur_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """total duration of vocalizations by a given speaker type in milliseconds per hour + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations[annotations["speaker_type"]== kwargs["speaker"]]["duration"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","duration"},np.nan) +def avg_voc_dur_speaker(annotations: pd.DataFrame, duration: int, **kwargs): + """average duration in milliseconds of vocalizations for a given speaker type + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations[annotations["speaker_type"]== kwargs["speaker"]]["duration"].mean() + +@metricFunction({"speaker"},{"speaker_type","words"}) +def wc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of words per hour for a given speaker type + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations[annotations["speaker_type"]== kwargs["speaker"]]["words"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","syllables"}) +def sc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of syllables per hour for a given speaker type + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations[annotations["speaker_type"]== kwargs["speaker"]]["syllables"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","phonemes"}) +def pc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of phonemes per hour for a given speaker type + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations[annotations["speaker_type"]== kwargs["speaker"]]["phonemes"].sum() * (3600000 / duration) + +@metricFunction({},{"words"}) +def wc_adu_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of words per hour for all speakers + + Required keyword arguments: + """ + return annotations["words"].sum() * (3600000 / duration) + +@metricFunction({},{"syllables"}) +def sc_adu_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of syllables per hour for all speakers + + Required keyword arguments: + """ + return annotations["syllables"].sum() * (3600000 / duration) + +@metricFunction({},{"phonemes"}) +def pc_adu_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of phonemes per hour for all speakers + + Required keyword arguments: + """ + return annotations["phonemes"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type"}) +def cry_voc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of cry vocalizations per hour for a given speaker (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "Y")].shape[0] * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type","duration"}) +def cry_voc_dur_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """total duration of cry vocalizations by a given speaker type in milliseconds per hour (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "Y")]["duration"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type","duration"},np.nan) +def avg_cry_voc_dur_speaker(annotations: pd.DataFrame, duration: int, **kwargs): + """average duration of cry vocalizations by a given speaker type (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + value = annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "Y")]["duration"].mean() * (3600000 / duration) + if pd.isnull(value) : value = 0 + return value + +@metricFunction({"speaker"},{"speaker_type","vcm_type"}) +def can_voc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of canonical vocalizations per hour for a given speaker type (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "C")].shape[0] * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type","duration"}) +def can_voc_dur_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """total duration of canonical vocalizations by a given speaker type in milliseconds per hour (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "C")]["duration"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type","duration"},np.nan) +def avg_can_voc_dur_speaker(annotations: pd.DataFrame, duration: int, **kwargs): + """average duration of canonical vocalizations for a given speaker type (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + value = annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "C")]["duration"].mean() * (3600000 / duration) + if pd.isnull(value) : value = 0 + return value + +@metricFunction({"speaker"},{"speaker_type","vcm_type"}) +def non_can_voc_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """number of non canonical vocalizations per hour for a given speaker type (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "N")].shape[0] * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type","duration"}) +def non_can_voc_dur_speaker_ph(annotations: pd.DataFrame, duration: int, **kwargs): + """total duration of non canonical vocalizations by a given speaker type in milliseconds per hour (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + return annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "N")]["duration"].sum() * (3600000 / duration) + +@metricFunction({"speaker"},{"speaker_type","vcm_type","duration"},np.nan) +def avg_non_can_voc_dur_speaker(annotations: pd.DataFrame, duration: int, **kwargs): + """average duration of non canonical vocalizations for a given speaker type (based on vcm_type) + + Required keyword arguments: + - speaker : speaker_type to use + """ + value = annotations.loc[(annotations["speaker_type"]== kwargs["speaker"]) & (annotations["vcm_type"]== "N")]["duration"].mean() * (3600000 / duration) + if pd.isnull(value) : value = 0 + return value + +@metricFunction({},{},np.nan) +def lp_n(annotations: pd.DataFrame, duration: int, **kwargs): + """linguistic proportion on the number of vocalizations for CHI (based on vcm_type or [cries,vfxs,utterances_count] if vcm_type does not exist) + + Required keyword arguments: + """ + if "vcm_type" in annotations.columns: + speech_voc = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"].isin(["N","C"]))].shape[0] + cry_voc = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"]== "Y")].shape[0] + value = speech_voc / (speech_voc + cry_voc) + total = speech_voc + cry_voc + if total: + value = speech_voc / total + else: + value = np.nan + elif set(["cries","vfxs","utterances_count"]).issubset(annotations.columns): + annotations = annotations[annotations["speaker_type"] == "CHI"] + cries = annotations["cries"].apply(lambda x: len(ast.literal_eval(x))).sum() + vfxs = annotations["vfxs"].apply(lambda x: len(ast.literal_eval(x))).sum() + utterances = annotations["utterances_count"].sum() + total = (utterances + cries + vfxs) + if total: + value = utterances / total + else: + value = np.nan + else: + raise ValueError("the given set does not have the neccessary columns for this metric, choose a set that contains either [vcm_type] or [cries,vfxs,utterances_count]") + return value + +@metricFunction({},{"speaker_type","vcm_type"},np.nan) +def cp_n(annotations: pd.DataFrame, duration: int, **kwargs): + """canonical proportion on the number of vocalizations for CHI (based on vcm_type) + + Required keyword arguments: + """ + speech_voc = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"].isin(["N","C"]))].shape[0] + can_voc = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"]== "C")].shape[0] + if speech_voc: + value = can_voc / speech_voc + else: + value = np.nan + return value + +@metricFunction({},{},np.nan) +def lp_dur(annotations: pd.DataFrame, duration: int, **kwargs): + """linguistic proportion on the duration of vocalizations for CHI (based on vcm_type or [child_cry_vfxs_len,utterances_length] if vcm_type does not exist) + + Required keyword arguments: + """ + if "vcm_type" in annotations.columns: + speech_dur = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"].isin(["N","C"]))]["duration"].sum() + cry_dur = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"]== "Y")]["duration"].sum() + value = speech_dur / (speech_dur + cry_dur) + total = speech_dur + cry_dur + if total: + value = speech_dur / total + else: + value = np.nan + elif set(["child_cry_vfx_len","utterances_length"]).issubset(annotations.columns): + annotations = annotations[annotations["speaker_type"] == "CHI"] + utter_len = annotations["utterances_length"].sum() + total = annotations["child_cry_vfx_len"].sum() + utter_len + if total: + value = utter_len / total + else: + value = np.nan + else: + raise ValueError("the {} set does not have the neccessary columns for this metric, choose a set that contains either [vcm_type] or [child_cry_vfx_len,utterances_length]") + return value + +@metricFunction({},{"speaker_type","vcm_type","duration"},np.nan) +def cp_dur(annotations: pd.DataFrame, duration: int, **kwargs): + """canonical proportion on the number of vocalizations for CHI (based on vcm_type) + + Required keyword arguments: + """ + speech_dur = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"].isin(["N","C"]))]["duration"].sum() + can_dur = annotations.loc[(annotations["speaker_type"]== "CHI") & (annotations["vcm_type"]== "C")]["duration"].sum() + if speech_dur: + value = can_dur / speech_dur + else: + value = np.nan + return value \ No newline at end of file diff --git a/ChildProject/projects.py b/ChildProject/projects.py index a48138667..78b06995a 100644 --- a/ChildProject/projects.py +++ b/ChildProject/projects.py @@ -634,6 +634,15 @@ def get_recordings_from_list( _recordings = _recordings[ _recordings["recording_filename"].isin(recordings) ] + + if _recordings.shape[0] < len(recordings): + recs = pd.Series(recordings) + missing_recs = recs[~recs.isin(self.recordings['recording_filename'])].tolist() + #self.recordings[~self.recordings['recording_filename'].isin(recordings)]['recording_filename'].tolist() + raise ValueError("recordings {} were not found in the dataset index.\ + Check the names and make sure they exist in\ + 'metadata/recordings.csv'".format(missing_recs)) + return _recordings diff --git a/ChildProject/utils.py b/ChildProject/utils.py index d11cee252..7520f5b86 100644 --- a/ChildProject/utils.py +++ b/ChildProject/utils.py @@ -1,4 +1,5 @@ import os +from datetime import datetime import librosa from scipy.fftpack import fft, ifft import numpy as np @@ -48,6 +49,51 @@ def intersect_ranges(xs, ys): except StopIteration: return +class TimeInterval: + def __init__(self, start : datetime, stop : datetime): + #remove the day/month/year component + self.start = start.replace(year=1900, month=1, day=1) + self.stop = stop.replace(year=1900, month=1, day=1) + + def length(self): + return self.stop - self.start + + def __repr__(self): + return "TimeInterval([{}, {}])".format(self.start, self.stop) + +def time_intervals_intersect(ti1 : TimeInterval, ti2 : TimeInterval): + """ + given 2 time intervals (those do not take in consideration days, only time in the day), return an array of new interval(s) representing the intersections of the original ones. + eg : + - time_intervals_intersect(TimeInterval(datetime(1900,1,1,8,57),datetime(1900,1,1,21,4)),TimeInterval(datetime(1900,1,1,10,36),datetime(1900,1,1,22,1))) => [TimeInterval(10:36 , 21:04)] + - time_intervals_intersect(TimeInterval(datetime(1900,1,1,8,57),datetime(1900,1,1,22,1)),TimeInterval(datetime(1900,1,1,21,4),datetime(1900,1,1,10,36))) => [TimeInterval(08:57 , 10:36),TimeInterval(21:04 , 22:01)] + """ + #The calculation and boolean evaluation is done that way to optimize the process, those expressions were obtained using a Karnaugh table. Given the relations between the different start and ending times, the boolean relations used below gives the correct intervals + a = ti1.start <= ti1.stop + b = ti2.start <= ti2.stop + c = ti1.stop <= ti2.stop + d = ti1.start <= ti2.start + e = ti1.start <= ti2.stop + f = ti2.start <= ti1.stop + r = [] + #case where correct resulting interval is [start of the 2nd interval : end of the 1st interval] + if c and (d and (not e or f) or not e and f) or d and not e and f : r = [TimeInterval(ti2.start,ti1.stop)] + #case where correct resulting interval is [start of the 2nd interval : end of the 2nd interval] + elif not c and (d and (b or not a) or not a and b) or not a and b and d : r = [ti2] + #case where correct resulting interval is [start of the 1st interval : end of the 2nd interval] + elif not c and (not d and (not e and not f or e) or e and not f) or not d and e and not f : r = [TimeInterval(ti1.start,ti2.stop)] + #case where correct resulting interval is [start of the 1st interval : end of the 1st interval] + elif c and (not d and (not a and not b or a) or a and not b) or a and not b and not d : r = [ti1] + # !! here the expression was simplified because previous statements should already have caught their relevant cases (ie this statement should always be last or changed) + #case where correct resulting interval is [start of the 1st interval : end of the 2nd interval] U [start of the 2nd interval : end of the 1st interval] + elif not a and (not b or e) or d and e and f : r = [TimeInterval(ti1.start,ti2.stop),TimeInterval(ti2.start,ti1.stop)] + + #remove the intervals having equal values (3:00 to 3:00) + i = 0 + while i < len(r): + if r[i].start == r[i].stop : r.pop(i) + else : i += 1 + return r def get_audio_duration(filename): import sox diff --git a/docs/source/_ext/directives.py b/docs/source/_ext/directives.py index 4b351d78e..e15efa086 100644 --- a/docs/source/_ext/directives.py +++ b/docs/source/_ext/directives.py @@ -3,11 +3,15 @@ from docutils.parsers.rst.directives.tables import CSVTable from docutils import Component +from inspect import getmembers, isfunction, cleandoc + from sphinx.directives.code import CodeBlock from ChildProject.projects import ChildProject from ChildProject.annotations import AnnotationManager +from ChildProject.pipelines import metricsFunctions + import subprocess # I think this is a sort of hack (smart hack, but a hack nonetheless !) @@ -57,7 +61,7 @@ def __init__(self, *args, **kwargs): elif array == 'annotations': table = [c for c in AnnotationManager.INDEX_COLUMNS if (c.generated or c.required)] elif array == 'documentation': - table = ChildProject.DOCUMENTATION_COLUMNS + table = ChildProject.DOCUMENTATION_COLUMNS if not table: raise Exception("invalid table '{}'".format(array)) @@ -90,14 +94,46 @@ def __init__(self, *args, **kwargs): dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') pd.DataFrame(df).to_csv(os.path.join(dir_path, self.options['file']), index = False) + +class CustomTable(CSVTable): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) - + if 'header' not in self.options: + raise KeyError("IndexTable is missing a header attribute.") + + array = self.options.pop('header') + + df = [] + if array == 'list-metrics': + ignores = {'metricFunction'} + metrics = getmembers(metricsFunctions, isfunction) + for name, func in metrics: + if name in ignores : continue + doc = func.__doc__.split('Required keyword arguments:',1) + description = cleandoc(doc[0]) + arguments = cleandoc(doc[1]) if len(doc) > 1 else "" + df_entry = { + 'Callable': name, + 'Description': wrap(description, 50), + 'Required arguments': wrap(arguments,25), + } + df.append(df_entry) + + self.options['file'] = '{}.csv'.format(array) + self.options['header-rows'] = 1 + self.options['widths'] = [20, 50, 25] + + dir_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..') + pd.DataFrame(df).to_csv(os.path.join(dir_path, self.options['file']), index = False) + def setup(app): app.add_directive("clidoc", CliDoc) app.add_directive("index-table", IndexTable) + app.add_directive("custom-table", CustomTable) return { - 'version': '0.1', + 'version': '0.2', 'parallel_read_safe': True, 'parallel_write_safe': True, } \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index fc4522cc6..ab33e86b4 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ author = 'Lucas Gautheron' # The full version, including alpha/beta/rc tags -release = '0.0.1' +release = '0.0.4' # -- General configuration --------------------------------------------------- diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst index 6f0a19e67..cad6e8554 100644 --- a/docs/source/metrics.rst +++ b/docs/source/metrics.rst @@ -6,48 +6,36 @@ Overview This package allows to extract metrics that are commonly used from annotations produced by the LENA or other pipelines. +A csv file containing the metrics is produced along with a YML parameter file storing all the options used .. clidoc:: child-project metrics --help +The Period option aggregates vocalizations for each time-of-the-day-unit based on a period specified by the user. +For instance, if the period is set to ``15Min`` (i.e. 15 minutes), vocalization rates will be reported for each +recording and time-unit (e.g. 09:00 to 09:15, 09:15 to 09:30, etc.). + +The output dataframe has :math:`r \times p` rows, where :math:`r` is the amount of recordings (or children if the ``-by`` option is set to ``child_id`` etc.), and :math:`p` is the +amount of time-bins per day (i.e. :math:`24 \times 4=96` for a 15-minute period). + +The output dataframe includes a ``period_start`` and a ``period_end`` columns that contain the onset and offset of each time-unit in HH:MM:SS format. +The ``duration_`` columns contain the total amount of annotated time covering each time-bin and each set, in milliseconds. + +If ``--by`` is set to e.g. ``child_id``, then the values for each time-bin will be the average rates across +all the recordings of every child. + The list of supported metrics is shown below: -.. csv-table:: - :header: "Variable", "Description", "pipelines" - :widths: 15, 50, 5 - - voc_fem/mal/och_ph,number of vocalizations by different talker types per hour,"ACLEW,LENA,Period" - voc_dur_fem/mal/och_ph,total duration of vocalizations by different talker types in seconds per hour,"ACLEW,LENA,Period" - avg_voc_dur_fem/mal/och,average vocalization length (conceptually akin to MLU) by different talker types,"ACLEW,LENA,Period" - wc_adu_ph,adult word count (collapsing across males and females),"ACLEW,LENA" - wc_fem/mal_ph,adult word count by different talker types,"ACLEW,LENA" - sc_adu_ph,adult syllable count (collapsing across males and females),ACLEW - sc_fem/mal_ph,adult syllable count by different talker types,ACLEW - pc_adu_ph,adult phoneme count (collapsing across males and females),ACLEW - pc_fem/mal_ph,adult phoneme count by different talker types,ACLEW - freq_n,frequency of child voc out of all vocs based on number of vocalizations,"ACLEW,LENA" - freq_dur,frequency of child voc out of all vocs based on duration of vocalizations,"ACLEW,LENA" - cry_voc_chi_ph,number of child vocalizations that are crying,"ACLEW,LENA" - can_voc_chi_ph,number of child vocs that are canonical,ACLEW - non_can_vpc_chi_ph,number of child vocs that are non-canonical,ACLEW - sp_voc_chi_ph,number of child vocs that are speech-like (can+noncan for ACLEW),"ACLEW,LENA" - cry_voc_dur_chi_ph,total duration of child vocalizations that are crying,"ACLEW,LENA" - can_voc_dur_chi_ph,total duration of child vocs that are canonical,ACLEW - non_can_voc_dur_chi_ph,total duration of child vocs that are non-canonical,ACLEW - sp_voc_dur_chi_ph,total duration of child vocs that are speech-like (can+noncan for ACLEW),"ACLEW,LENA" - avg_cry_voc_dur_chi,average duration of child vocalizations that are crying,"ACLEW,LENA" - avg_cran_voc_dur_chi,average duration of child vocs that are canonical,ACLEW - avg_non_can_voc_dur_chi,average duration of child vocs that are non-canonical,ACLEW - avg_sp_voc_dur_chi,average duration of child vocs that are speech-like (can+noncan for ACLEW),"ACLEW,LENA" - lp_n,linguistic proportion = (speech)/(cry+speech) based on number of vocalizations,"ACLEW,LENA" - cp_n,canonical proportion = canonical /(can+noncan) based on number of vocalizations,ACLEW - lp_dur,linguistic proportion = (speech)/(cry+speech) based on duration of vocalizations,"ACLEW,LENA" - cp_dur,canonical proportion = canonical /(can+noncan) based on duration of vocalizations,ACLEW - -.. note:: - - Average rates are expressed in counts/hour (for events) or in seconds/hour (for durations). +.. warning:: + + Be aware that numerous metrics are rates (every metric ending with 'ph' is) and not absolute counts! + This can differ with results from other methods of extraction (e.g. LENA metrics). + Rates are expressed in counts/hour (for events) or in milliseconds/hour (for durations). + +.. _list-metrics: +.. custom-table:: + :header: list-metrics LENA Metrics ~~~~~~~~~~~~ @@ -63,26 +51,44 @@ ACLEW Metrics child-project metrics /path/to/dataset output.csv aclew --help -Period-aggregated metrics -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The Period Metrics pipeline aggregates vocalizations for each time-of-the-day-unit based on a period specified by the user. -For instance, if the period is set to ``15Min`` (i.e. 15 minutes), vocalization rates will be reported for each -recording and time-unit (e.g. 09:00 to 09:15, 09:15 to 09:30, etc.). +Custom metrics +~~~~~~~~~~~~~~ -The output dataframe has :math:`r \times p` rows, where :math:`r` is the amount of recordings (or children if the ``-by`` option is set to ``child_id``), and :math:`p` is the -amount of time-bins per day (i.e. :math:`24 \times 4=96` for a 15-minute period). +The Custom metrics pipeline allows you to provide your own list of desired metrics to the pipeline to be extracted. +The list must be in a csv file containing the following colums: + - callable (required) : name of the metric to extract, see :ref:`list-metrics` + - set (required) : name of the set to extract from, make sure this annotations set is capable (has the required information) to extract this specific metric + - name (optional) : name to use in the resulting metrics. If none is given, a default name will be used. Use this to extract the same metric for different sets and avoid name clashes. + - (depending on the requirements of the metric you chose) : For each required argument of a metric, add a column of that argument's name. -The output dataframe includes a ``period`` column that contains the onset of each time-unit in HH:MM:SS format. -The ``duration`` columns contains the total amount of annotations covering each time-bin, in milliseconds. +This is an example of a csv file we use to extract metrics. +We want to extract the number of vocalizations per hour of the key child (CHI), male adult (MAL) and female adult (FEM) on 2 different sets to compare their result. +So we write 3 lines per set (vtc and its), each having a different speaker and we also give each metric an explicit name because the default names `voc_chi_ph`, `voc_mal_ph` and `voc_fem_ph` would have clashed between the 2 sets. +Additionaly, we extract linguistic proportion on number of vocalizations and on duration separately from the vcm set. the default names won't clash and no speaker is needed (linguistic proportion is used on CHI) so we leave those columns empty. -If ``--by`` is set to e.g. ``child_id``, then the values for each time-bin will be the average rates across -all the recordings of every child. +.. csv-table:: + :header: "callable", "set", "name", "speaker" + :widths: 20, 10, 20,20 + + voc_speaker_ph,vtc,voc_chi_ph_vtc,CHI + voc_speaker_ph,vtc,voc_mal_ph_vtc,MAL + voc_speaker_ph,vtc,voc_fem_ph_vtc,FEM + voc_speaker_ph,its,voc_chi_ph_its,CHI + voc_speaker_ph,its,voc_mal_ph_its,MAL + voc_speaker_ph,its,voc_fem_ph_its,FEM + lp_n,vcm,, + lp_dur,vcm,, .. clidoc:: - child-project metrics /path/to/dataset output.csv period --help + child-project metrics /path/to/dataset output.csv custom --help + +Metrics from parameter file +~~~~~~~~~~~~~~~~~~~~~~~~~~~ -..note:: +To facilitate the extraction of metrics, one can simply use an exhaustive yml parameter file to launch a new extraction. +This file has the exact same structure as the one produced by the pipeline. So you can use an output parameter file to rerun the same analysis. + +.. clidoc:: - Average rates are expressed in seconds/hour regardless of the period. + child-project metrics-specification --help \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index fff1c2c68..4de6c0168 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,7 @@ chardet==3.0.4 click==7.1.1 datalad +GitPython keyring==21.4.0; python_version < "3.8" jinja2 librosa diff --git a/setup.py b/setup.py index eb183f479..729282010 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ requires = { "core": ["pandas>=0.25.0", "jinja2", "numpy>=1.16.5", "sox", "datalad"], "annotations": ["lxml", "pympi-ling", "pylangacq", "python-dateutil>=2.8.1"], - "metrics": ['pyannote.metrics; python_version >= "3.7.0"', "nltk", "sklearn"], + "metrics": ['pyannote.metrics; python_version >= "3.7.0"', "nltk", "sklearn", "GitPython"], "audio": ["librosa", "pydub", "pysoundfile"], "samplers": ["PyYAML"], "zooniverse": ["panoptes-client"], @@ -36,6 +36,7 @@ "Topic :: Scientific/Engineering", ], packages=find_packages(), + python_requires=">=3.7", install_requires=requires["core"] + requires["annotations"] + requires["audio"] diff --git a/tests/data/lena_its.csv b/tests/data/lena_its.csv new file mode 100644 index 000000000..0808c52e6 --- /dev/null +++ b/tests/data/lena_its.csv @@ -0,0 +1,23290 @@ +segment_onset,segment_offset,speaker_type,lena_speaker,words,lena_block_number,lena_block_type,lena_conv_status,lena_response_count,lena_conv_turn_type,lena_conv_floor_type,utterances_count,utterances_length,average_db,peak_db,utterances,non_speech_length,child_cry_vfx_len,cries,vfxs,raw_filename +0,1300,NA,NOF,0.0,1,pause,NA,NA,NA,NA,0.0,0,-40.37,-23.15,[],0,0,[],[],example_lena_new.its +1300,2650,FEM,FAN,6.99,1,AMF,BC,0,NT,FI,0.0,0,-22.26,-13.89,[],0,0,[],[],example_lena_new.its +2650,4310,NA,NOF,0.0,1,AMF,NA,NA,NA,NA,0.0,0,-44.81,-35.04,[],0,0,[],[],example_lena_new.its 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000000000..8e1a37392 --- /dev/null +++ b/tests/data/parameters_metrics.yml @@ -0,0 +1,36 @@ +destination : "met_yaml.csv" +by : "child_id" +path : output/metrics +destination : output/metrics/extra/specs_metrics.csv +child_cols : child_dob,experiment +rec_cols : date_iso +from_time : "8:15" +to_time : "16:45" +period : 2h +metrics_list : + - + callable : voc_speaker_ph + set : specs_its + speaker : FEM + - + name : voc_och_ph_its + callable : voc_speaker_ph + set : specs_its + speaker : OCH + - + callable : voc_speaker_ph + set : specs_its + speaker : CHI + - + name : voc_dur_chi_ph_its + callable : voc_dur_speaker_ph + set : specs_its + speaker : CHI + - + callable : wc_speaker_ph + set : specs_its + speaker : MAL + - + callable : wc_speaker_ph + set : specs_its + speaker : FEM diff --git a/tests/test_annotations.py b/tests/test_annotations.py index cdcddb2a7..2de4fff75 100644 --- a/tests/test_annotations.py +++ b/tests/test_annotations.py @@ -316,11 +316,12 @@ def test_clipping(project): def test_within_time_range(project): + from ChildProject.utils import TimeInterval am = AnnotationManager(project) am.project.recordings = pd.read_csv("tests/data/time_range_recordings.csv") annotations = pd.read_csv("tests/data/time_range_annotations.csv") - matches = am.get_within_time_range(annotations, "09:00", "20:00") + matches = am.get_within_time_range(annotations, TimeInterval(datetime.datetime(1900,1,1,9,0),datetime.datetime(1900,1,1,20,0))) truth = pd.read_csv("tests/truth/time_range.csv") diff --git a/tests/test_metrics.py b/tests/test_metrics.py index b65274bac..c883c739a 100644 --- a/tests/test_metrics.py +++ b/tests/test_metrics.py @@ -7,7 +7,7 @@ from ChildProject.projects import ChildProject from ChildProject.annotations import AnnotationManager -from ChildProject.pipelines.metrics import LenaMetrics, AclewMetrics, PeriodMetrics +from ChildProject.pipelines.metrics import LenaMetrics, AclewMetrics, CustomMetrics, MetricsSpecificationPipeline def fake_vocs(data, filename): @@ -65,61 +65,112 @@ def test_aclew(project): "range_offset": 4000, "format": "rttm", } - for set in ["vtc", "alice", "vcm"] + for set in ["aclew_vtc", "aclew_alice", "aclew_vcm"] ] ), import_function=partial(fake_vocs, data), ) - aclew = AclewMetrics(project, by="child_id") + aclew = AclewMetrics(project, by="child_id", rec_cols='date_iso', child_cols='experiment,child_dob',vtc='aclew_vtc',alice='aclew_alice',vcm='aclew_vcm') aclew.extract() - truth = pd.read_csv("tests/truth/aclew_metrics.csv", index_col="child_id") + truth = pd.read_csv("tests/truth/aclew_metrics.csv") - pd.testing.assert_frame_equal(aclew.metrics, truth) + pd.testing.assert_frame_equal(aclew.metrics, truth, check_like=True) +def test_lena(project): + data = pd.read_csv("tests/data/lena_its.csv") -def test_period(project): am = AnnotationManager(project) + am.import_annotations( + pd.DataFrame( + [ + { + "set": "lena_its", + "raw_filename": "file.its", + "time_seek": 0, + "recording_filename": "sound.wav", + "range_onset": 0, + "range_offset": 100000000, + "format": "its", + } + ] + ), + import_function=partial(fake_vocs, data), + ) - range_onset = 0 - range_offset = 86400 - 3600 + lena = LenaMetrics(project, set="lena_its", period='1h', from_time='10:00' , to_time= '16:00') + lena.extract() - onsets = np.arange(range_onset, range_offset, 5) - offsets = onsets + 1 + truth = pd.read_csv("tests/truth/lena_metrics.csv") - onsets = onsets * 1000 - offsets = offsets * 1000 + pd.testing.assert_frame_equal(lena.metrics, truth, check_like=True) - data = pd.DataFrame( - { - "segment_onset": onsets, - "segment_offset": offsets, - "speaker_type": ["FEM"] * len(onsets), - } - ) +def test_custom(project): + am = AnnotationManager(project) + + data = pd.read_csv("tests/data/lena_its.csv") am.import_annotations( pd.DataFrame( [ { - "set": "test", - "raw_filename": "file.rttm", + "set": "custom_its", + "raw_filename": "file.its", "time_seek": 0, "recording_filename": "sound.wav", - "range_onset": range_onset * 1000, - "range_offset": range_offset * 1000, - "format": "rttm", + "range_onset": 0, + "range_offset": 100000000, + "format": "its", } ] ), import_function=partial(fake_vocs, data), ) + + parameters="tests/data/list_metrics.csv" + cmm = CustomMetrics(project, parameters) + cmm.extract() - period = PeriodMetrics(project, by="child_id", period="2H", set="test") - period.extract() + truth = pd.read_csv("tests/truth/custom_metrics.csv") - truth = pd.read_csv("tests/truth/period_metrics.csv", index_col=["child_id"]) + pd.testing.assert_frame_equal(cmm.metrics, truth, check_like=True) - pd.testing.assert_frame_equal(period.metrics, truth) +def test_specs(project): + data = pd.read_csv("tests/data/lena_its.csv") + + am = AnnotationManager(project) + am.import_annotations( + pd.DataFrame( + [ + { + "set": "specs_its", + "raw_filename": "file.its", + "time_seek": 0, + "recording_filename": "sound.wav", + "range_onset": 0, + "range_offset": 100000000, + "format": "its", + } + ] + ), + import_function=partial(fake_vocs, data), + ) + + msp = MetricsSpecificationPipeline() + + parameters = "tests/data/parameters_metrics.yml" + msp.run(parameters) + + output = pd.read_csv(msp.destination) + truth = pd.read_csv("tests/truth/specs_metrics.csv") + + pd.testing.assert_frame_equal(output, truth, check_like=True) + + new_params = msp.parameters_path + msp.run(new_params) + + output = pd.read_csv(msp.destination) + + pd.testing.assert_frame_equal(output, truth, check_like=True) diff --git a/tests/truth/aclew_metrics.csv b/tests/truth/aclew_metrics.csv index db5b08803..caa1877ba 100644 --- a/tests/truth/aclew_metrics.csv +++ b/tests/truth/aclew_metrics.csv @@ -1,2 +1,2 @@ -child_id,voc_fem_ph,voc_dur_fem_ph,avg_voc_dur_fem,voc_chi_ph,voc_dur_chi_ph,avg_voc_dur_chi,wc_fem_ph,sc_fem_ph,pc_fem_ph,wc_adu_ph,sc_adu_ph,pc_adu_ph,cry_voc_chi_ph,cry_voc_dur_chi_ph,can_voc_chi_ph,can_voc_dur_chi_ph,avg_can_voc_dur_chi,non_can_voc_chi_ph,non_can_voc_dur_chi_ph,avg_non_can_voc_dur_chi,lp_n,cp_n,lp_dur,cp_dur,duration -1,7200.0,720.0000000000005,0.10000000000000006,18000.0,1800.0,0.1,7200.0,14400.0,28800.0,7200.0,14400.0,28800.0,0.0,0.0,9000.0,899.9999999999999,89.99999999999999,9000.0,900.0,90.0,1.0,0.5,1.0,0.49999999999999994,4.0 +child_id,child_dob,experiment,date_iso,duration_aclew_alice,pc_fem_ph,pc_mal_ph,pc_adu_ph,sc_mal_ph,sc_fem_ph,wc_mal_ph,wc_fem_ph,wc_adu_ph,sc_adu_ph,duration_aclew_vcm,can_voc_dur_chi_ph,cry_voc_dur_chi_ph,cry_voc_chi_ph,avg_can_voc_dur_chi,non_can_voc_chi_ph,non_can_voc_dur_chi_ph,avg_non_can_voc_dur_chi,lp_n,lp_dur,can_voc_chi_ph,avg_cry_voc_dur_chi,cp_dur,cp_n,duration_aclew_vtc,avg_voc_dur_chi,avg_voc_dur_och,avg_voc_dur_mal,avg_voc_dur_fem,voc_dur_chi_ph,voc_dur_och_ph,voc_dur_mal_ph,voc_dur_fem_ph,voc_chi_ph,voc_och_ph,voc_mal_ph,voc_fem_ph +1,2020-01-01,test,,4000,28800.0,0.0,28800.0,0.0,14400.0,0.0,7200.0,7200.0,14400.0,4000,900000.0,0.0,0.0,90000.0,9000.0,900000.0,90000.0,1.0,1.0,9000.0,0,0.5,0.5,4000,100.0,,,100.0,1800000.0,0.0,0.0,720000.0,18000.0,0.0,0.0,7200.0 diff --git a/tests/truth/custom_metrics.csv b/tests/truth/custom_metrics.csv new file mode 100644 index 000000000..68e3f4b55 --- /dev/null +++ b/tests/truth/custom_metrics.csv @@ -0,0 +1,3 @@ +recording_filename,child_id,duration_custom_its,voc_chi_ph_its,voc_fem_ph_its,voc_mal_ph_its,voc_dur_chi_ph,voc_dur_fem_ph,voc_dur_mal_ph +sound.wav,1,100000000,66.88799999999999,75.276,23.651999999999997,64576.079999999994,90352.79999999999,34702.2 +sound2.wav,1,0,,,,,, diff --git a/tests/truth/lena_metrics.csv b/tests/truth/lena_metrics.csv new file mode 100644 index 000000000..9ea14557a --- /dev/null +++ b/tests/truth/lena_metrics.csv @@ -0,0 +1,49 @@ +recording_filename,period_start,period_end,child_id,duration_lena_its,voc_fem_ph,wc_adu_ph,wc_mal_ph,wc_fem_ph,avg_voc_dur_chi,avg_voc_dur_och,avg_voc_dur_mal,lp_n,avg_voc_dur_fem,voc_dur_och_ph,voc_dur_mal_ph,voc_dur_fem_ph,voc_chi_ph,voc_och_ph,voc_mal_ph,voc_dur_chi_ph,lp_dur +sound.wav,00:00:00,01:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,01:00:00,02:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,02:00:00,03:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,03:00:00,04:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,04:00:00,05:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,05:00:00,06:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,06:00:00,07:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,07:00:00,08:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,08:00:00,09:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,09:00:00,10:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,10:00:00,11:00:00,1,7200000.0,102.0,741.075,311.145,429.92999999999995,1004.1871921182266,1053.5555555555557,1584.8076923076924,0.717948717948718,1182.450980392157,23705.0,82410.0,120610.0,101.5,22.5,52.0,101925.0,0.7238733172920596 +sound.wav,11:00:00,12:00:00,1,7200000.0,0.0,0.0,0.0,0.0,,960.0,,,,480.0,0.0,0.0,0.0,0.5,0.0,0.0, +sound.wav,12:00:00,13:00:00,1,6400000.0,141.1875,812.0474999999999,204.48,607.5674999999999,863.3908045977012,936.7647058823529,1409.3846153846155,0.7277486910994765,1195.2589641434263,35831.25,51530.625,168755.625,97.875,38.25,36.5625,84504.375,0.7167696744952616 +sound.wav,13:00:00,14:00:00,1,3600000.0,247.0,1532.3999999999999,447.11,1085.29,946.9456066945606,1087.3239436619717,1423.9325842696628,0.7350746268656716,1218.421052631579,77200.0,126730.0,300950.0,239.0,71.0,89.0,226320.0,0.6677801053048746 +sound.wav,14:00:00,15:00:00,1,3600000.0,177.0,1364.33,486.90999999999997,877.4200000000001,954.8780487804878,1028.4,2055.8333333333335,0.7536231884057971,1285.4237288135594,25710.0,148020.0,227520.0,123.0,25.0,72.0,117450.0,0.7291602741127136 +sound.wav,15:00:00,16:00:00,1,3600000.0,100.0,521.83,36.620000000000005,485.21000000000004,1323.8961038961038,784.5454545454545,1183.75,0.7078651685393258,1235.3,8630.0,9470.0,123530.0,77.0,11.0,8.0,101940.0,0.5633201749706604 +sound.wav,16:00:00,17:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,17:00:00,18:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,18:00:00,19:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,19:00:00,20:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound.wav,20:00:00,21:00:00,1,0.0,,,,,,,,,,,,,,,,, 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+sound2.wav,15:00:00,16:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,16:00:00,17:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,17:00:00,18:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,18:00:00,19:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,19:00:00,20:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,20:00:00,21:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,21:00:00,22:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,22:00:00,23:00:00,1,0.0,,,,,,,,,,,,,,,,, +sound2.wav,23:00:00,00:00:00,1,0.0,,,,,,,,,,,,,,,,, diff --git a/tests/truth/period_metrics.csv b/tests/truth/period_metrics.csv deleted file mode 100644 index 4db321a07..000000000 --- a/tests/truth/period_metrics.csv +++ /dev/null @@ -1,13 +0,0 @@ -child_id,voc_fem_ph,voc_dur_fem_ph,avg_voc_dur_fem,voc_mal_ph,voc_dur_mal_ph,avg_voc_dur_mal,voc_chi_ph,voc_dur_chi_ph,avg_voc_dur_chi,voc_och_ph,voc_dur_och_ph,avg_voc_dur_och,duration,period -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,00:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,02:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,04:00:00 -"1",720.0000100000007,720.0000100000007,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7199999,06:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,3600000,08:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,10:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,12:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,14:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,16:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,18:00:00 -"1",720.0,720.0,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7200000,20:00:00 -"1",720.0000100000007,720.0000100000007,1.0,0.0,0.0,,0.0,0.0,,0.0,0.0,,7199999,22:00:00 diff --git a/tests/truth/specs_metrics.csv b/tests/truth/specs_metrics.csv new file mode 100644 index 000000000..a6a16ca3b --- /dev/null +++ b/tests/truth/specs_metrics.csv @@ -0,0 +1,13 @@ +child_id,period_start,period_end,child_dob,experiment,date_iso,duration_specs_its,voc_fem_ph,voc_och_ph_its,voc_chi_ph,voc_dur_chi_ph_its,wc_mal_ph,wc_fem_ph +1,00:00:00,02:00:00,2020-01-01,test,,0.0,,,,,, +1,02:00:00,04:00:00,2020-01-01,test,,0.0,,,,,, +1,04:00:00,06:00:00,2020-01-01,test,,0.0,,,,,, +1,06:00:00,08:00:00,2020-01-01,test,,0.0,,,,,, +1,08:00:00,10:00:00,2020-01-01,test,,9900000.0,88.0,23.636363636363637,99.63636363636364,89203.63636363637,145.5236363636364,348.6581818181818 +1,10:00:00,12:00:00,2020-01-01,test,,14400000.0,51.0,11.5,50.75,50962.5,155.5725,214.96499999999997 +1,12:00:00,14:00:00,2020-01-01,test,,10000000.0,179.28,50.04,148.32,135558.0,291.82680000000005,779.5475999999999 +1,14:00:00,16:00:00,2020-01-01,test,,7200000.0,138.5,18.0,100.0,109695.0,261.765,681.3149999999999 +1,16:00:00,18:00:00,2020-01-01,test,,2700000.0,0.0,0.0,0.0,0.0,0.0,0.0 +1,18:00:00,20:00:00,2020-01-01,test,,0.0,,,,,, +1,20:00:00,22:00:00,2020-01-01,test,,0.0,,,,,, +1,22:00:00,00:00:00,2020-01-01,test,,0.0,,,,,,