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@@ -1,278 +0,0 @@
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-#!/usr/bin/env python3
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-
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-from ChildProject.projects import ChildProject
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-from ChildProject.annotations import AnnotationManager
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-from ChildProject.metrics import segments_to_annotation
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-
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-import argparse
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-
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-import datalad.api
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-from os.path import join as opj
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-from os.path import basename, exists
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-
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-import multiprocessing as mp
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-
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-import numpy as np
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-import pandas as pd
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-import pickle
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-from pyannote.core import Annotation, Segment, Timeline
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-
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-import stan
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-
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-parser = argparse.ArgumentParser(description = 'main model described throughout the notes.')
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-parser.add_argument('--group', default = 'child', choices = ['corpus', 'child'])
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-parser.add_argument('--chains', default = 4, type = int)
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-parser.add_argument('--samples', default = 2000, type = int)
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-parser.add_argument('--validation', default = 0, type = float)
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-parser.add_argument('--output', default = 'model3')
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-args = parser.parse_args()
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-
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-def extrude(self, removed, mode: str = 'intersection'):
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- if isinstance(removed, Segment):
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- removed = Timeline([removed])
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-
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- truncating_support = removed.gaps(support=self.extent())
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- # loose for truncate means strict for crop and vice-versa
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- if mode == "loose":
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- mode = "strict"
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- elif mode == "strict":
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- mode = "loose"
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-
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- return self.crop(truncating_support, mode=mode)
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-
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-def compute_counts(parameters):
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- corpus = parameters['corpus']
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- annotator = parameters['annotator']
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- speakers = ['CHI', 'OCH', 'FEM', 'MAL']
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-
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- project = ChildProject(parameters['path'])
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- am = AnnotationManager(project)
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- am.read()
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-
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- intersection = AnnotationManager.intersection(
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- am.annotations, ['vtc', annotator]
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- )
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-
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- intersection['path'] = intersection.apply(
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- lambda r: opj(project.path, 'annotations', r['set'], 'converted', r['annotation_filename']),
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- axis = 1
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- )
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- datalad.api.get(list(intersection['path'].unique()))
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-
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- intersection = intersection.merge(project.recordings[['recording_filename', 'child_id']], how = 'left')
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- intersection['child'] = corpus + '_' + intersection['child_id'].astype(str)
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- intersection['duration'] = intersection['range_offset']-intersection['range_onset']
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- print(corpus, annotator, (intersection['duration']/1000/2).sum()/3600)
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-
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- data = []
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- for child, ann in intersection.groupby('child'):
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- #print(corpus, child)
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-
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- segments = am.get_collapsed_segments(ann)
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- if 'speaker_type' not in segments.columns:
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- continue
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-
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- segments = segments[segments['speaker_type'].isin(speakers)]
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-
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- vtc = {
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- speaker: segments_to_annotation(segments[(segments['set'] == 'vtc') & (segments['speaker_type'] == speaker)], 'speaker_type').get_timeline()
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- for speaker in speakers
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- }
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-
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- truth = {
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- speaker: segments_to_annotation(segments[(segments['set'] == annotator) & (segments['speaker_type'] == speaker)], 'speaker_type').get_timeline()
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- for speaker in speakers
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- }
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-
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- for speaker_A in speakers:
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- vtc[f'{speaker_A}_vocs_explained'] = vtc[speaker_A].crop(truth[speaker_A], mode = 'loose')
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- vtc[f'{speaker_A}_vocs_fp'] = extrude(vtc[speaker_A], vtc[f'{speaker_A}_vocs_explained'])
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- vtc[f'{speaker_A}_vocs_fn'] = extrude(truth[speaker_A], truth[speaker_A].crop(vtc[speaker_A], mode = 'loose'))
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-
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- for speaker_B in speakers:
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- vtc[f'{speaker_A}_vocs_fp_{speaker_B}'] = vtc[f'{speaker_A}_vocs_fp'].crop(truth[speaker_B], mode = 'loose')
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-
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- for speaker_C in speakers:
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- if speaker_C != speaker_B and speaker_C != speaker_A:
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- vtc[f'{speaker_A}_vocs_fp_{speaker_B}'] = extrude(
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- vtc[f'{speaker_A}_vocs_fp_{speaker_B}'],
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- vtc[f'{speaker_A}_vocs_fp_{speaker_B}'].crop(truth[speaker_C], mode = 'loose')
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- )
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-
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-
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- d = {}
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- for i, speaker_A in enumerate(speakers):
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- for j, speaker_B in enumerate(speakers):
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- if i != j:
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- z = len(vtc[f'{speaker_A}_vocs_fp_{speaker_B}'])
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- else:
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- z = min(len(vtc[f'{speaker_A}_vocs_explained']), len(truth[speaker_A]))
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-
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- d[f'vtc_{i}_{j}'] = z
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-
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- d[f'truth_{i}'] = len(truth[speaker_A])
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- d['child'] = child
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-
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- d['duration'] = ann['duration'].sum()/2/1000
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- data.append(d)
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-
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- return pd.DataFrame(data).assign(
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- corpus = corpus,
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- )
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-
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-stan_code = """
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-data {
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- int<lower=1> n_clips; // number of clips
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- int<lower=1> n_groups; // number of groups
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- int<lower=1> n_classes; // number of classes
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- int group[n_clips];
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- int vtc[n_clips,n_classes,n_classes];
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- int truth[n_clips,n_classes];
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-
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- int<lower=1> n_validation;
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- int<lower=1> n_sim;
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-
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- real<lower=0> rates_alphas[n_classes];
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- real<lower=0> rates_betas[n_classes];
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-}
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-
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-parameters {
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- matrix<lower=0,upper=1>[n_classes,n_classes] mus;
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- matrix<lower=1>[n_classes,n_classes] etas;
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- matrix<lower=0,upper=1>[n_classes,n_classes] group_confusion[n_groups];
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-}
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-
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-transformed parameters {
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- matrix<lower=0>[n_classes,n_classes] alphas;
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- matrix<lower=0>[n_classes,n_classes] betas;
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-
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- alphas = mus * etas;
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- betas = (1-mus) * etas;
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-}
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-
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-model {
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- for (k in n_validation:n_clips) {
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- for (i in 1:n_classes) {
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- for (j in 1:n_classes) {
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- vtc[k,i,j] ~ binomial(truth[k,j], group_confusion[group[k],j,i]);
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- }
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- }
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- }
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-
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- for (i in 1:n_classes) {
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- for (j in 1:n_classes) {
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- mus[i,j] ~ beta(1,1);
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- etas[i,j] ~ pareto(1,1.5);
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- }
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- }
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-
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- for (c in 1:n_groups) {
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- for (i in 1:n_classes) {
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- for (j in 1:n_classes) {
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- group_confusion[c,i,j] ~ beta(alphas[i,j], betas[i,j]);
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- }
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- }
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- }
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-}
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-
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-generated quantities {
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- int pred[n_clips,n_classes,n_classes];
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- matrix[n_classes,n_classes] probs[n_groups];
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- matrix[n_classes,n_classes] log_lik[n_clips];
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-
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- int sim_truth[n_sim,n_classes];
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- int sim_vtc[n_sim,n_classes];
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- vector[n_classes] lambdas;
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- real chi_adu_coef = 0; // null hypothesis
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-
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- for (c in 1:n_groups) {
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- for (i in 1:n_classes) {
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- for (j in 1:n_classes) {
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- probs[c,i,j] = beta_rng(alphas[i,j], betas[i,j]);
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- }
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- }
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- }
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-
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- for (k in 1:n_clips) {
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- for (i in 1:n_classes) {
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- for (j in 1:n_classes) {
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- if (k >= n_validation) {
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- pred[k,i,j] = binomial_rng(truth[k,j], group_confusion[group[k],i,j]);
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- log_lik[k,i,j] = binomial_lpmf(vtc[k,i,j] | truth[k,j], group_confusion[group[k],j,i]);
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- }
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- else {
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- pred[k,i,j] = binomial_rng(truth[k,j], probs[group[k],j,i]);
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- log_lik[k,i,j] = beta_lpdf(probs[group[k],j,i] | alphas[j,i], betas[j,i]);
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- log_lik[k,i,j] += binomial_lpmf(vtc[k,i,j] | truth[k,j], probs[group[k],j,i]);
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- }
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- }
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- }
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- }
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-
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- real lambda;
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- for (k in 1:n_sim) {
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- for (i in 2:n_classes) {
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- lambda = gamma_rng(rates_alphas[i], rates_betas[i]);
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- sim_truth[k,i] = poisson_rng(lambda);
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- }
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- lambda = gamma_rng(rates_alphas[1], rates_betas[1]);
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- sim_truth[k,1] = poisson_rng(lambda + chi_adu_coef*(sim_truth[k,3]+sim_truth[k,4]));
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- }
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-
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- for (k in 1:n_sim) {
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- for (i in 1:n_classes) {
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- sim_vtc[k,i] = 0;
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- for (j in 1:n_classes) {
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- real p = beta_rng(alphas[j,i], betas[j,i]);
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- sim_vtc[k,i] += binomial_rng(sim_truth[k,j], p);
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- }
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- }
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- }
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-}
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-"""
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-
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-if __name__ == "__main__":
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- annotators = pd.read_csv('input/annotators.csv')
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- annotators['path'] = annotators['corpus'].apply(lambda c: opj('input', c))
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-
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- with mp.Pool(processes = 8) as pool:
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- data = pd.concat(pool.map(compute_counts, annotators.to_dict(orient = 'records')))
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-
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- data = data.sample(frac = 1)
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- duration = data['duration'].sum()
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-
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- vtc = np.moveaxis([[data[f'vtc_{j}_{i}'].values for i in range(4)] for j in range(4)], -1, 0)
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- truth = np.transpose([data[f'truth_{i}'].values for i in range(4)])
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-
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- print(vtc.shape)
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-
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- rates = pd.read_csv('output/speech_dist.csv')
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-
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- data = {
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- 'n_clips': truth.shape[0],
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- 'n_classes': truth.shape[1],
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- 'n_groups': data[args.group].nunique(),
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- 'n_validation': max(1, int(truth.shape[0]*args.validation)),
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- 'n_sim': 40,
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- 'group': 1+data[args.group].astype('category').cat.codes.values,
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- 'truth': truth.astype(int),
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- 'vtc': vtc.astype(int),
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- 'rates_alphas': rates['alpha'].values,
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- 'rates_betas': rates['beta'].values
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- }
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-
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- print(f"clips: {data['n_clips']}")
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- print(f"groups: {data['n_groups']}")
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- print("true vocs: {}".format(np.sum(data['truth'])))
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- print("vtc vocs: {}".format(np.sum(data['vtc'])))
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- print("duration: {}".format(duration))
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-
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- with open(f'data_{args.output}.pickle', 'wb') as fp:
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- pickle.dump(data, fp, pickle.HIGHEST_PROTOCOL)
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-
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- posterior = stan.build(stan_code, data = data)
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- fit = posterior.sample(num_chains = args.chains, num_samples = args.samples)
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- df = fit.to_frame()
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- df.to_parquet(f'fit_{args.output}.parquet')
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-
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-
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