|
|
@@ -0,0 +1,334 @@
|
|
|
+#!/usr/bin/env python3
|
|
|
+
|
|
|
+from ChildProject.projects import ChildProject
|
|
|
+from ChildProject.annotations import AnnotationManager
|
|
|
+from ChildProject.metrics import segments_to_annotation
|
|
|
+
|
|
|
+import argparse
|
|
|
+
|
|
|
+import datalad.api
|
|
|
+from os.path import join as opj
|
|
|
+from os.path import basename, exists
|
|
|
+from functools import partial
|
|
|
+from itertools import combinations
|
|
|
+import copy
|
|
|
+
|
|
|
+import multiprocessing as mp
|
|
|
+
|
|
|
+import numpy as np
|
|
|
+import pandas as pd
|
|
|
+import pickle
|
|
|
+from pyannote.core import Annotation, Segment, Timeline
|
|
|
+
|
|
|
+import stan
|
|
|
+
|
|
|
+parser = argparse.ArgumentParser(description="model3")
|
|
|
+parser.add_argument("--group", default="child", choices=["corpus", "child"])
|
|
|
+parser.add_argument("--chains", default=4, type=int)
|
|
|
+parser.add_argument("--samples", default=2000, type=int)
|
|
|
+parser.add_argument("--require-agreement", action='store_true')
|
|
|
+args = parser.parse_args()
|
|
|
+
|
|
|
+
|
|
|
+def extrude(self, removed, mode: str = "intersection"):
|
|
|
+ if isinstance(removed, Segment):
|
|
|
+ removed = Timeline([removed])
|
|
|
+
|
|
|
+ truncating_support = removed.gaps(support=self.extent())
|
|
|
+ # loose for truncate means strict for crop and vice-versa
|
|
|
+ if mode == "loose":
|
|
|
+ mode = "strict"
|
|
|
+ elif mode == "strict":
|
|
|
+ mode = "loose"
|
|
|
+
|
|
|
+ return self.crop(truncating_support, mode=mode)
|
|
|
+
|
|
|
+
|
|
|
+def largest_intersection(corpus):
|
|
|
+ corpus, annotators = corpus
|
|
|
+
|
|
|
+ project = ChildProject(annotators["path"].iloc[0])
|
|
|
+ am = AnnotationManager(project)
|
|
|
+ am.read()
|
|
|
+
|
|
|
+ largest_duration = 0
|
|
|
+ best_pair = None
|
|
|
+ for x, y in combinations(annotators['annotator'].values, r = 2):
|
|
|
+ intersection = am.intersection(am.annotations, [x, y])
|
|
|
+ intersection['duration'] = intersection['range_offset']-intersection['range_onset']
|
|
|
+ duration = intersection['duration'].sum()/2
|
|
|
+
|
|
|
+ if duration > largest_duration:
|
|
|
+ best_pair = (x, y)
|
|
|
+ largest_duration = duration
|
|
|
+
|
|
|
+ if best_pair is None:
|
|
|
+ return None
|
|
|
+ else:
|
|
|
+ return annotators[annotators['annotator'].isin(best_pair)]
|
|
|
+
|
|
|
+
|
|
|
+def process_pair(pair):
|
|
|
+ corpus, parameters = pair
|
|
|
+ annotators = parameters["annotator"].values
|
|
|
+ speakers = ["CHI", "OCH", "FEM", "MAL"]
|
|
|
+
|
|
|
+ project = ChildProject(parameters["path"].iloc[0])
|
|
|
+ am = AnnotationManager(project)
|
|
|
+ am.read()
|
|
|
+
|
|
|
+ intersection = AnnotationManager.intersection(am.annotations, [annotators[0], annotators[1], 'vtc'])
|
|
|
+
|
|
|
+ intersection["path"] = intersection.apply(
|
|
|
+ lambda r: opj(
|
|
|
+ project.path, "annotations", r["set"], "converted", r["annotation_filename"]
|
|
|
+ ),
|
|
|
+ axis=1,
|
|
|
+ )
|
|
|
+ datalad.api.get(list(intersection["path"].unique()))
|
|
|
+
|
|
|
+ intersection = intersection.merge(
|
|
|
+ project.recordings[["recording_filename", "child_id"]], how="left"
|
|
|
+ )
|
|
|
+ intersection["child"] = corpus + "_" + intersection["child_id"].astype(str)
|
|
|
+
|
|
|
+ data = []
|
|
|
+ for child, ann in intersection.groupby("child"):
|
|
|
+ print(corpus, child)
|
|
|
+
|
|
|
+ segments = am.get_collapsed_segments(ann)
|
|
|
+ if "speaker_type" not in segments.columns:
|
|
|
+ continue
|
|
|
+
|
|
|
+ segments = segments[segments["speaker_type"].isin(speakers)]
|
|
|
+
|
|
|
+ vtc = {
|
|
|
+ speaker: segments_to_annotation(
|
|
|
+ segments[
|
|
|
+ (segments["set"] == "vtc") & (segments["speaker_type"] == speaker)
|
|
|
+ ],
|
|
|
+ "speaker_type",
|
|
|
+ ).get_timeline()
|
|
|
+ for speaker in speakers
|
|
|
+ }
|
|
|
+
|
|
|
+ truth1 = {
|
|
|
+ speaker: segments_to_annotation(
|
|
|
+ segments[
|
|
|
+ (segments["set"] == annotators[0])
|
|
|
+ & (segments["speaker_type"] == speaker)
|
|
|
+ ],
|
|
|
+ "speaker_type",
|
|
|
+ ).get_timeline()
|
|
|
+ for speaker in speakers
|
|
|
+ }
|
|
|
+
|
|
|
+ truth2 = {
|
|
|
+ speaker: segments_to_annotation(
|
|
|
+ segments[
|
|
|
+ (segments["set"] == annotators[1])
|
|
|
+ & (segments["speaker_type"] == speaker)
|
|
|
+ ],
|
|
|
+ "speaker_type",
|
|
|
+ ).get_timeline()
|
|
|
+ for speaker in speakers
|
|
|
+ }
|
|
|
+
|
|
|
+ for speaker_A in speakers:
|
|
|
+ union = copy.copy(truth1[speaker_A])
|
|
|
+ union.update(truth2[speaker_A])
|
|
|
+
|
|
|
+ vtc[f"{speaker_A}_vocs_explained"] = vtc[speaker_A].crop(
|
|
|
+ union, mode="loose"
|
|
|
+ )
|
|
|
+
|
|
|
+ vtc[f"{speaker_A}_vocs_fp"] = extrude(
|
|
|
+ vtc[speaker_A], vtc[f"{speaker_A}_vocs_explained"]
|
|
|
+ )
|
|
|
+ vtc[f"{speaker_A}_vocs_fn"] = extrude(
|
|
|
+ union, union.crop(vtc[speaker_A], mode="loose")
|
|
|
+ )
|
|
|
+
|
|
|
+ for speaker_B in speakers:
|
|
|
+ intersect_B = truth1[speaker_B]
|
|
|
+ intersect_B = intersect_B.crop(truth2[speaker_B], mode="loose")
|
|
|
+
|
|
|
+ vtc[f"{speaker_A}_vocs_fp_{speaker_B}"] = vtc[
|
|
|
+ f"{speaker_A}_vocs_fp"
|
|
|
+ ].crop(intersect_B, mode="loose")
|
|
|
+
|
|
|
+ for speaker_C in speakers:
|
|
|
+ intersect_C = truth1[speaker_C]
|
|
|
+ intersect_C = intersect_C.crop(truth2[speaker_C], mode="loose")
|
|
|
+ if speaker_C != speaker_B and speaker_C != speaker_A:
|
|
|
+ vtc[f"{speaker_A}_vocs_fp_{speaker_B}"] = extrude(
|
|
|
+ vtc[f"{speaker_A}_vocs_fp_{speaker_B}"],
|
|
|
+ vtc[f"{speaker_A}_vocs_fp_{speaker_B}"].crop(
|
|
|
+ intersect_C, mode="loose"
|
|
|
+ ),
|
|
|
+ )
|
|
|
+
|
|
|
+ d = {}
|
|
|
+ for i, speaker_A in enumerate(speakers):
|
|
|
+ truth_len = len(truth1[speaker_A].crop(truth2[speaker_A], mode = "loose"))
|
|
|
+ for j, speaker_B in enumerate(speakers):
|
|
|
+ if i != j:
|
|
|
+ z = len(vtc[f"{speaker_A}_vocs_fp_{speaker_B}"])
|
|
|
+ else:
|
|
|
+ z = min(
|
|
|
+ len(vtc[f"{speaker_A}_vocs_explained"]), truth_len
|
|
|
+ )
|
|
|
+
|
|
|
+ d[f"vtc_{i}_{j}"] = z
|
|
|
+
|
|
|
+ d[f"truth_{i}"] = truth_len
|
|
|
+ d["child"] = child
|
|
|
+
|
|
|
+ data.append(d)
|
|
|
+
|
|
|
+ return pd.DataFrame(data).assign(corpus=corpus)
|
|
|
+
|
|
|
+
|
|
|
+stan_code = """
|
|
|
+data {
|
|
|
+ int<lower=1> n_clips; // number of clips
|
|
|
+ int<lower=1> n_groups; // number of groups
|
|
|
+ int<lower=1> n_classes; // number of classes
|
|
|
+ int group[n_clips];
|
|
|
+ int vtc[n_clips,n_classes,n_classes];
|
|
|
+ int truth[n_clips,n_classes];
|
|
|
+
|
|
|
+ int<lower=1> n_sim;
|
|
|
+
|
|
|
+ real<lower=0> rates_alphas[n_classes];
|
|
|
+ real<lower=0> rates_betas[n_classes];
|
|
|
+}
|
|
|
+
|
|
|
+parameters {
|
|
|
+ matrix<lower=0,upper=1>[n_classes,n_classes] mus;
|
|
|
+ matrix<lower=1>[n_classes,n_classes] etas;
|
|
|
+ matrix<lower=0,upper=1>[n_classes,n_classes] group_confusion[n_groups];
|
|
|
+}
|
|
|
+
|
|
|
+transformed parameters {
|
|
|
+ matrix<lower=0>[n_classes,n_classes] alphas;
|
|
|
+ matrix<lower=0>[n_classes,n_classes] betas;
|
|
|
+
|
|
|
+ alphas = mus * etas;
|
|
|
+ betas = (1-mus) * etas;
|
|
|
+}
|
|
|
+
|
|
|
+model {
|
|
|
+ for (k in 1:n_clips) {
|
|
|
+ for (i in 1:n_classes) {
|
|
|
+ for (j in 1:n_classes) {
|
|
|
+ vtc[k,i,j] ~ binomial(truth[k,j], group_confusion[group[k],j,i]);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ for (i in 1:n_classes) {
|
|
|
+ for (j in 1:n_classes) {
|
|
|
+ mus[i,j] ~ beta(1,1);
|
|
|
+ etas[i,j] ~ pareto(1,1.5);
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ for (c in 1:n_groups) {
|
|
|
+ for (i in 1:n_classes) {
|
|
|
+ for (j in 1:n_classes) {
|
|
|
+ group_confusion[c,i,j] ~ beta(alphas[i,j], betas[i,j]);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+}
|
|
|
+
|
|
|
+generated quantities {
|
|
|
+ matrix[n_classes,n_classes] probs[n_groups];
|
|
|
+ int sim_truth[n_sim,n_classes];
|
|
|
+ int sim_vtc[n_sim,n_classes];
|
|
|
+ vector[n_classes] lambdas;
|
|
|
+ real chi_adu_coef;
|
|
|
+
|
|
|
+ if (uniform_rng(0,1) > 0.2) {
|
|
|
+ chi_adu_coef = uniform_rng(0,1);
|
|
|
+ }
|
|
|
+ else {
|
|
|
+ chi_adu_coef = 0;
|
|
|
+ }
|
|
|
+
|
|
|
+ for (c in 1:n_groups) {
|
|
|
+ for (i in 1:n_classes) {
|
|
|
+ for (j in 1:n_classes) {
|
|
|
+ probs[c,i,j] = beta_rng(alphas[i,j], betas[i,j]);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+
|
|
|
+ real lambda;
|
|
|
+ for (k in 1:n_sim) {
|
|
|
+ for (i in 2:n_classes) {
|
|
|
+ lambda = gamma_rng(rates_alphas[i], rates_betas[i]);
|
|
|
+ sim_truth[k,i] = poisson_rng(lambda);
|
|
|
+ }
|
|
|
+ lambda = gamma_rng(rates_alphas[1], rates_betas[1]);
|
|
|
+ sim_truth[k,1] = poisson_rng(lambda + chi_adu_coef*(sim_truth[k,3]+sim_truth[k,4]));
|
|
|
+ }
|
|
|
+
|
|
|
+ for (k in 1:n_sim) {
|
|
|
+ for (i in 1:n_classes) {
|
|
|
+ sim_vtc[k,i] = 0;
|
|
|
+ for (j in 1:n_classes) {
|
|
|
+ real p = beta_rng(alphas[i,j], betas[i,j]);
|
|
|
+ sim_vtc[k,i] += binomial_rng(sim_truth[k,j], p);
|
|
|
+ }
|
|
|
+ }
|
|
|
+ }
|
|
|
+}
|
|
|
+"""
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ annotators = pd.read_csv("input/annotators.csv")
|
|
|
+ annotators["path"] = annotators["corpus"].apply(lambda c: opj("input", c))
|
|
|
+
|
|
|
+ with mp.Pool(processes=8) as pool:
|
|
|
+ pairs = pd.concat(pool.map(largest_intersection, annotators.groupby('corpus')))
|
|
|
+ print(pairs)
|
|
|
+ data = pd.concat(pool.map(process_pair, pairs.groupby('corpus')))
|
|
|
+
|
|
|
+ data = data.sample(frac=1)
|
|
|
+
|
|
|
+ vtc = np.moveaxis(
|
|
|
+ [[data[f"vtc_{j}_{i}"].values for i in range(4)] for j in range(4)], -1, 0
|
|
|
+ )
|
|
|
+ truth = np.transpose([data[f"truth_{i}"].values for i in range(4)])
|
|
|
+
|
|
|
+ print(vtc.shape)
|
|
|
+
|
|
|
+ rates = pd.read_csv("output/speech_dist.csv")
|
|
|
+
|
|
|
+ data = {
|
|
|
+ "n_clips": truth.shape[0],
|
|
|
+ "n_classes": truth.shape[1],
|
|
|
+ "n_groups": data[args.group].nunique(),
|
|
|
+ "n_sim": 100,
|
|
|
+ "group": 1 + data[args.group].astype("category").cat.codes.values,
|
|
|
+ "truth": truth.astype(int),
|
|
|
+ "vtc": vtc.astype(int),
|
|
|
+ "rates_alphas": rates["alpha"].values,
|
|
|
+ "rates_betas": rates["beta"].values,
|
|
|
+ }
|
|
|
+
|
|
|
+ print(f"clips: {data['n_clips']}")
|
|
|
+ print(f"groups: {data['n_groups']}")
|
|
|
+ print("true vocs: {}".format(np.sum(data["truth"])))
|
|
|
+ print("vtc vocs: {}".format(np.sum(data["vtc"])))
|
|
|
+
|
|
|
+ with open("data_model3_errors.pickle", "wb") as fp:
|
|
|
+ pickle.dump(data, fp, pickle.HIGHEST_PROTOCOL)
|
|
|
+
|
|
|
+ posterior = stan.build(stan_code, data=data)
|
|
|
+ fit = posterior.sample(num_chains=args.chains, num_samples=args.samples)
|
|
|
+ df = fit.to_frame()
|
|
|
+ df.to_parquet("fit_model3_errors.parquet")
|
|
|
+
|
