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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,55 +1,84 @@ | ||
| import pandas as pd, numpy as np, os, sys | ||
| from tqdm import tqdm | ||
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| def resample(data, indices): | ||
| new_data = [] | ||
| for i in indices: | ||
| new_data.append(data[i]) | ||
| return new_data | ||
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| # takes a list of tuples of precision, recall, f1, support | ||
| # returns the 95% confidence interval for each | ||
| def get_confidence_intervals(accs, precs, recs, f1s): | ||
| accuracy_int = ("Accuracy: %s" % ' '.join(["%f" % (x) for x in np.percentile(accs, [2.5, 50, 97.5])])) | ||
| precision_int = ("Precision: %s" % ' '.join(["%f" % (x) for x in np.percentile(precs, [2.5, 50, 97.5])])) | ||
| recall_int = ("Recall: %s" % ' '.join(["%f" % (x) for x in np.percentile(recs, [2.5, 50, 97.5])])) | ||
| f1_int = ("F1: %s" % ' '.join(["%f" % (x) for x in np.percentile(f1s, [2.5, 50, 97.5])])) | ||
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| return accuracy_int, precision_int, recall_int, f1_int | ||
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| def get_metrics(tp_times,fp_times,tn_times,fn_times): | ||
| tp_time = sum(tp_times) | ||
| fp_time = sum(fp_times) | ||
| tn_time = sum(tn_times) | ||
| fn_time = sum(fn_times) | ||
| accuracy = (tp_time + tn_time) / (tp_time + fp_time + tn_time + fn_time) | ||
| precision = tp_time / (tp_time + fp_time) | ||
| recall = tp_time / (tp_time + fn_time) | ||
| f1 = 2*(precision*recall)/(precision+recall) | ||
| return accuracy, precision, recall, f1 | ||
|
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| def bootstrap_metrics(tp_times,fp_times,tn_times,fn_times,n_samples=1000): | ||
| accuracies = []; precisions = []; recalls = []; f1s = [] | ||
| for _ in tqdm(range(n_samples)): | ||
| sample=np.random.choice(list(range(0,len(tp_times))),len(tp_times)) | ||
| sample_tp_times = resample(tp_times, sample) | ||
| sample_fp_times = resample(fp_times, sample) | ||
| sample_tn_times = resample(tn_times, sample) | ||
| sample_fn_times = resample(fn_times, sample) | ||
| metrics = get_metrics(sample_tp_times, sample_fp_times, sample_tn_times, sample_fn_times) | ||
| accuracies.append(metrics[0]); precisions.append(metrics[1]); recalls.append(metrics[2]); f1s.append(metrics[3]) | ||
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| intervals = get_confidence_intervals(accuracies, precisions, recalls, f1s) | ||
| return intervals | ||
|
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| # Baseline on AudioSet | ||
| baseline_audioset_results = pd.read_csv('baseline_audioset_results.csv') | ||
| tp_time = sum(baseline_audioset_results.tp_time) | ||
| fp_time = sum(baseline_audioset_results.fp_time) | ||
| tn_time = sum(baseline_audioset_results.tn_time) | ||
| fn_time = sum(baseline_audioset_results.fn_time) | ||
| accuracy = (tp_time + tn_time) / (tp_time + fp_time + tn_time + fn_time) | ||
| precision = tp_time / (tp_time + fp_time) | ||
| recall = tp_time / (tp_time + fn_time) | ||
| f1 = 2*(precision*recall)/(precision+recall) | ||
| print("Baseline results on Audioset:") | ||
| print(f"Accuracy: {accuracy}") | ||
| print(f"Precision: {precision}") | ||
| print(f"Recall: {recall}") | ||
| print(f"f1: {f1}") | ||
|
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| print();print() | ||
| # Baseline on Audio Set | ||
| print("Baseline results on Audio Set...") | ||
| baseline_audioset_results = pd.read_csv('baseline_audioset_results.csv') | ||
|
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| intervals = bootstrap_metrics( | ||
| baseline_audioset_results.tp_time, baseline_audioset_results.fp_time, | ||
| baseline_audioset_results.tn_time, baseline_audioset_results.fn_time,n_samples=1000) | ||
|
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| for interval in intervals: | ||
| print(interval) | ||
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| print();print() | ||
| # Baseline on SWB Validation Set | ||
| print("Baseline results on SWB Validation Set...") | ||
| baseline_swv_val_results = pd.read_csv('baseline_switchboard_val_results.csv') | ||
| tp_time = sum(baseline_swv_val_results.tp_time) | ||
| fp_time = sum(baseline_swv_val_results.fp_time) | ||
| tn_time = sum(baseline_swv_val_results.tn_time) | ||
| fn_time = sum(baseline_swv_val_results.fn_time) | ||
| accuracy = (tp_time + tn_time) / (tp_time + fp_time + tn_time + fn_time) | ||
| precision = tp_time / (tp_time + fp_time) | ||
| recall = tp_time / (tp_time + fn_time) | ||
| f1 = 2*(precision*recall)/(precision+recall) | ||
| print("Baseline results on SWB Validation Set:") | ||
| print(f"Accuracy: {accuracy}") | ||
| print(f"Precision: {precision}") | ||
| print(f"Recall: {recall}") | ||
| print(f"f1: {f1}") | ||
|
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| print();print() | ||
| intervals = bootstrap_metrics( | ||
| baseline_swv_val_results.tp_time, baseline_swv_val_results.fp_time, | ||
| baseline_swv_val_results.tn_time, baseline_swv_val_results.fn_time,n_samples=1000) | ||
|
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| for interval in intervals: | ||
| print(interval) | ||
|
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| """ | ||
|
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| print();print() | ||
| # Baseline on SWB Test Set | ||
| print("Baseline results on SWB Test Set...") | ||
| baseline_swv_test_results = pd.read_csv('baseline_switchboard_test_results.csv') | ||
| tp_time = sum(baseline_swv_test_results.tp_time) | ||
| fp_time = sum(baseline_swv_test_results.fp_time) | ||
| tn_time = sum(baseline_swv_test_results.tn_time) | ||
| fn_time = sum(baseline_swv_test_results.fn_time) | ||
| accuracy = (tp_time + tn_time) / (tp_time + fp_time + tn_time + fn_time) | ||
| precision = tp_time / (tp_time + fp_time) | ||
| recall = tp_time / (tp_time + fn_time) | ||
| f1 = 2*(precision*recall)/(precision+recall) | ||
| print("Baseline results on SWB Test Set:") | ||
| print(f"Accuracy: {accuracy}") | ||
| print(f"Precision: {precision}") | ||
| print(f"Recall: {recall}") | ||
| print(f"f1: {f1}") | ||
| """ | ||
|
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| intervals = bootstrap_metrics( | ||
| baseline_swv_test_results.tp_time, baseline_swv_test_results.fp_time, | ||
| baseline_swv_test_results.tn_time, baseline_swv_test_results.fn_time,n_samples=1000) | ||
|
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| for interval in intervals: | ||
| print(interval) |
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