Commit bd88a8b7 authored by Paul Primus's avatar Paul Primus
Browse files

add submission packages

parent 666564d1
This diff is collapsed.
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"from pymongo import MongoClient\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"from dcase2020_task2.data_sets.mcm_dataset import INVERSE_CLASS_MAP, TRAINING_ID_MAP, CLASS_MAP\n",
"from scipy.stats import rankdata\n",
"\n",
"baseline_auc = {\n",
" 'name': 'baseline',\n",
" 0: {0: 0.5441, 2: 0.7340, 4: 0.6161, 6: 0.7392},\n",
" 1: {0: 0.6715, 2: 0.6153, 4: 0.8833, 6: 0.7455},\n",
" 2: {0: 0.9619, 2: 0.7897, 4: 0.9430, 6: 0.6959},\n",
" 3: {1: 0.8136, 2: 0.8597, 3: 0.6330, 4: 0.8445},\n",
" 4: {1: 0.7807, 2: 0.6416, 3: 0.7535},\n",
" 5: {0: 0.6876, 2: 0.6818, 4: 0.7430, 6: 0.5390}\n",
" }\n",
" \n",
"baseline_pauc = {\n",
" 'name': 'baseline',\n",
" 0: {0: 0.4937, 2: 0.5481, 4: 0.5326, 6: 0.5235},\n",
" 1: {0: 0.5674, 2: 0.5810, 4: 0.6710, 6: 0.5802},\n",
" 2: {0: 0.8144, 2: 0.6368, 4: 0.7198, 6: 0.4902},\n",
" 3: {1: 0.6840, 2: 0.7772, 3: 0.5521, 4: 0.6897},\n",
" 4: {1: 0.6425, 2: 0.5601, 3: 0.6103},\n",
" 5: {0: 0.5170, 2: 0.5183, 4: 0.5197, 6: 0.4843}\n",
"}\n",
"\n",
"baseline_both = {}\n",
"for t in baseline_auc:\n",
" if t == 'name':\n",
" baseline_both[t] = 'baseline'\n",
" continue\n",
" else:\n",
" baseline_both[t] = {}\n",
" for i in baseline_auc[t]:\n",
" baseline_both[t][i] = np.array([baseline_auc[t][i], baseline_pauc[t][i]])\n",
"\n",
"\n",
"def get_experiment(runs, name):\n",
" experiment_dict = dict()\n",
" for i in range(6):\n",
" experiment_dict[i] = dict()\n",
" \n",
" experiment_dict['name'] = name\n",
" \n",
" for experiment in runs:\n",
" if experiment['config'].get('id') == name:\n",
" machine_dict = experiment_dict.get(experiment['config']['machine_type'])\n",
" result = experiment.get('result')\n",
" machine_type = INVERSE_CLASS_MAP[experiment['config']['machine_type']]\n",
" machine_id = experiment['config']['machine_id']\n",
" \n",
" if result:\n",
" machine_dict[experiment['config']['machine_id']] = result.get(\n",
" machine_type, {}\n",
" ).get(\n",
" f'json://{machine_id}', -1\n",
" ).get('py/tuple', [0, 0])[:2]\n",
" else:\n",
" machine_dict[experiment['config']['machine_id']] = np.array([0, 0])\n",
" return experiment_dict\n",
"\n",
"\n",
"def get_record(experiment):\n",
" record = []\n",
" for i in range(6):\n",
" for j in TRAINING_ID_MAP[i]:\n",
" v = experiment.get(i)\n",
" if v:\n",
" v = v.get(j, [0, 0])\n",
" else:\n",
" v = np.array([0, 0])\n",
" record.append(np.array(v))\n",
" assert len(record) == 23\n",
" return experiment['name'], record"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [
{
"name": "stdout",
"text": [
"Loaded 405 runs.\n"
],
"output_type": "stream"
}
],
"source": [
"client = MongoClient('mongodb://student2.cp.jku.at:27017/')\n",
"experiments = [r for r in client.resnet_gridsearch.runs.find({\"experiment.name\": \"dcase2020_task2_ClassificationExperiment\"})]\n",
"print(f'Loaded {len(experiments)} runs.')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [
{
"name": "stdout",
"text": [
"Loaded 10 distinct experiments.\n"
],
"output_type": "stream"
}
],
"source": [
"descriptors = set()\n",
"for experiment in experiments:\n",
" descriptors = descriptors.union(set([experiment['config']['id']]))\n",
"descriptors = list(descriptors)\n",
"print(f'Loaded {len(descriptors)} distinct experiments.')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [],
"source": [
"# Extract Results\n",
"# Concatenate Baseline Results\n",
"n, m = get_record(baseline_both)\n",
"names = [n]\n",
"metrics = [np.array(m)]\n",
"\n",
"for descriptor in descriptors:\n",
" n, m = get_record(\n",
" get_experiment(\n",
" experiments, \n",
" descriptor\n",
" )\n",
" )\n",
" names.append(n)\n",
" metrics.append(np.array(m))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 33,
"outputs": [
{
"name": "stdout",
"text": [
"Best Model for Machine Type 0: (9, 'resnet_gridsearch_normal_loose_1e-4_100_BCE')\n",
"Best Model for Machine Type 1: (6, 'resnet_gridsearch_a_bit_larger_loose_1e-4_100_BCE')\n",
"Best Model for Machine Type 2: (6, 'resnet_gridsearch_a_bit_larger_loose_1e-4_100_BCE')\n",
"Best Model for Machine Type 3: (5, 'resnet_gridsearch_a_bit_smaller_loose_1e-4_100_BCE')\n",
"Best Model for Machine Type 4: (8, 'resnet_gridsearch_normal_loose_1e-5_100_AUC')\n",
"Best Model for Machine Type 5: (1, 'resnet_gridsearch_a_bit_smaller_loose_1e-4_100_AUC')\n",
"0-3.75: ID-6 resnet_gridsearch_a_bit_larger_loose_1e-4_100_BCE\n",
"1-4.416666666666667: ID-3 resnet_gridsearch_a_bit_larger_loose_1e-4_100_AUC\n",
"2-4.833333333333333: ID-4 resnet_gridsearch_a_bit_larger_loose_1e-5_100_BCE\n",
"3-5.083333333333333: ID-5 resnet_gridsearch_a_bit_smaller_loose_1e-4_100_BCE\n",
"4-5.166666666666667: ID-10 resnet_gridsearch_a_bit_larger_loose_1e-5_100_AUC\n",
"5-5.583333333333333: ID-9 resnet_gridsearch_normal_loose_1e-4_100_BCE\n",
"6-5.916666666666667: ID-2 resnet_gridsearch_normal_loose_1e-5_100_BCE\n",
"7-6.083333333333333: ID-7 resnet_gridsearch_normal_loose_1e-4_100_AUC\n",
"8-6.916666666666667: ID-1 resnet_gridsearch_a_bit_smaller_loose_1e-4_100_AUC\n",
"9-7.416666666666667: ID-8 resnet_gridsearch_normal_loose_1e-5_100_AUC\n",
"10-10.833333333333334: ID-0 baseline\n"
],
"output_type": "stream"
}
],
"source": [
"data = np.array(metrics)\n",
"auc_ranks = []\n",
"pauc_ranks = []\n",
"idxes = [0, 4, 8, 12, 16, 19, 23]\n",
"best_idxes = []\n",
"for type_, (i, j) in enumerate(zip(idxes[:-1], idxes[1:])):\n",
" average_auc = data[:, i:j, 0].mean(axis=1)\n",
" average_pauc = data[:, i:j, 1].mean(axis=1)\n",
" best_idx = np.argmax(average_auc + average_pauc)\n",
" best_idxes.append(\n",
" (best_idx, names[best_idx])\n",
" )\n",
" print(f'Best Model for Machine Type {type_}: {best_idxes[-1]}')\n",
" auc_ranks.append(rankdata(-average_auc))\n",
" pauc_ranks.append(rankdata(-average_pauc))\n",
"\n",
"\n",
"ranks = np.stack([np.array(list(zip(*auc_ranks))), np.array(list(zip(*pauc_ranks)))], axis=-1).mean(axis=-1).mean(axis=-1)\n",
"\n",
"sorted_model_indices = list(np.argsort(ranks))\n",
"names = np.array(names)\n",
"for i, (n, r, j) in enumerate(zip(names[sorted_model_indices], ranks[sorted_model_indices], sorted_model_indices)):\n",
" print(f'{i}-{r}: ID-{j} {n}')\n",
" "
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# Create Submission 1"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 34,
"outputs": [],
"source": [
"from dcase2020_task2.data_sets import INVERSE_CLASS_MAP, EVALUATION_ID_MAP\n",
"import os\n",
"from shutil import copyfile\n",
"\n",
"best_model_folder = names[sorted_model_indices[0]]\n",
"\n",
"for machine_type in range(6):\n",
" for model_id in EVALUATION_ID_MAP[machine_type]:\n",
" machine_type_str = INVERSE_CLASS_MAP[machine_type]\n",
" \n",
" src_path = os.path.join('..', 'experiment_logs', best_model_folder)\n",
" src = os.path.join(src_path, f'anomaly_score_{machine_type_str}_id_{model_id}_mean.csv')\n",
" \n",
" dst_path = os.path.join('..', 'submission_package', 'task2', 'Primus_CP-JKU_task2_1')\n",
" dst = os.path.join(dst_path, f'anomaly_score_{machine_type_str}_id_{model_id}.csv')\n",
"\n",
" copyfile(src, dst)\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 13,
"outputs": [],
"source": [
"# Create Submission 2"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 29,
"outputs": [],
"source": [
"from dcase2020_task2.data_sets import INVERSE_CLASS_MAP, EVALUATION_ID_MAP\n",
"import os\n",
"from shutil import copyfile\n",
"\n",
"for machine_type, (idx, folder_name) in enumerate(best_idxes):\n",
" \n",
" for model_id in EVALUATION_ID_MAP[machine_type]:\n",
" machine_type_str = INVERSE_CLASS_MAP[machine_type]\n",
" \n",
" src_path = os.path.join('..', 'experiment_logs', folder_name)\n",
" src = os.path.join(src_path, f'anomaly_score_{machine_type_str}_id_{model_id}_mean.csv')\n",
" \n",
" dst_path = os.path.join('..', 'submission_package', 'task2', 'Primus_CP-JKU_task2_2')\n",
" dst = os.path.join(dst_path, f'anomaly_score_{machine_type_str}_id_{model_id}.csv')\n",
"\n",
" copyfile(src, dst)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": 30,
"outputs": [
{
"data": {
"text/plain": "[(9, 'resnet_gridsearch_normal_loose_1e-4_100_BCE'),\n (6, 'resnet_gridsearch_a_bit_larger_loose_1e-4_100_BCE'),\n (6, 'resnet_gridsearch_a_bit_larger_loose_1e-4_100_BCE'),\n (5, 'resnet_gridsearch_a_bit_smaller_loose_1e-4_100_BCE'),\n (8, 'resnet_gridsearch_normal_loose_1e-5_100_AUC'),\n (1, 'resnet_gridsearch_a_bit_smaller_loose_1e-4_100_AUC')]"
},
"metadata": {},
"output_type": "execute_result",
"execution_count": 30
}
],
"source": [
"\n",
"\n",
"best_idxes"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n",
"is_executing": false
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
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