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b/examples/cinc17/notebooks/cinc17_eval.ipynb |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"Using TensorFlow backend.\n" |
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] |
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} |
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], |
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"source": [ |
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"import collections\n", |
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"import json\n", |
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"import keras\n", |
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"import numpy as np\n", |
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"import os\n", |
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"import sys\n", |
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"sys.path.append(\"../../../ecg\")\n", |
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"import scipy.stats as sst\n", |
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"\n", |
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"import util\n", |
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"import load" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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"output_type": "stream", |
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"text": [ |
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"100%|██████████| 852/852 [00:00<00:00, 931.25it/s]\n" |
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] |
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} |
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], |
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"source": [ |
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"model_path = \"/deep/group/awni/ecg_models/default/1528249597-44/0.412-0.870-015-0.309-0.892.hdf5\"\n", |
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"data_path = \"../dev.json\"\n", |
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"\n", |
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"data = load.load_dataset(data_path)\n", |
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"preproc = util.load(os.path.dirname(model_path))\n", |
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"model = keras.models.load_model(model_path)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 31, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"data_path = \"../train.json\"\n", |
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"with open(\"../train.json\", 'r') as fid:\n", |
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" train_labels = [json.loads(l)['labels'] for l in fid]\n", |
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"counts = collections.Counter(preproc.class_to_int[l[0]] for l in train_labels)\n", |
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"counts = sorted(counts.most_common(), key=lambda x: x[0])\n", |
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"counts = zip(*counts)[1]\n", |
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"smooth = 500\n", |
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"counts = np.array(counts)[None, None, :]\n", |
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"total = np.sum(counts) + counts.shape[1]\n", |
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"prior = (counts + smooth) / float(total)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"probs = []\n", |
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"labels = []\n", |
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"for x, y in zip(*data):\n", |
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" x, y = preproc.process([x], [y])\n", |
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" probs.append(model.predict(x))\n", |
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" labels.append(y)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 38, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"preds = []\n", |
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"ground_truth = []\n", |
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"for p, g in zip(probs, labels):\n", |
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" preds.append(sst.mode(np.argmax(p / prior, axis=2).squeeze())[0][0])\n", |
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" ground_truth.append(sst.mode(np.argmax(g, axis=2).squeeze())[0][0])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 39, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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" precision recall f1-score support\n", |
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"\n", |
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" A 0.859 0.912 0.885 80\n", |
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" N 0.914 0.923 0.919 508\n", |
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" O 0.803 0.785 0.794 233\n", |
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" ~ 0.731 0.613 0.667 31\n", |
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"\n", |
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"avg / total 0.872 0.873 0.872 852\n", |
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"\n", |
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"CINC Average 0.865827\n" |
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] |
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} |
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], |
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"source": [ |
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"import sklearn.metrics as skm\n", |
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"report = skm.classification_report(\n", |
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" ground_truth, preds,\n", |
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" target_names=preproc.classes,\n", |
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" digits=3)\n", |
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"scores = skm.precision_recall_fscore_support(\n", |
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" ground_truth,\n", |
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" preds,\n", |
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" average=None)\n", |
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"print(report)\n", |
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"print \"CINC Average {:3f}\".format(np.mean(scores[2][:3]))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 2", |
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"language": "python", |
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"name": "python2" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 2 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython2", |
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"version": "2.7.12" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 2 |
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} |