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Science prizes #85

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83f124c
updated science prize confusion matrix re-ordering, still issues with…
reneehlozek Apr 22, 2019
3e81fd4
Merge branch 'issue/78/roc-general' of https://github.com/aimalz/proc…
reneehlozek Apr 23, 2019
308f197
indentation util
reneehlozek Apr 23, 2019
7c18574
indendation
reneehlozek Apr 23, 2019
86ed0df
indentation util
reneehlozek Apr 23, 2019
d8fbcf5
metrics code to plot for plasticc science submission
May 21, 2019
e6e46ca
adjusted weight in brier and logloss
Jun 7, 2019
b36347c
resolved conflict in util
reneehlozek Sep 8, 2019
86c5141
fixed merge
reneehlozek Sep 11, 2019
0d5c63b
added metrics plots for results
Sep 11, 2019
b0109fa
fixed merge
Sep 11, 2019
82d48d3
code to adjust csv files
reneehlozek Dec 4, 2019
60061a1
Merge branch 'scienceprizes' of https://github.com/aimalz/proclam int…
reneehlozek Dec 4, 2019
6f2597f
updated paths
reneehlozek Dec 4, 2019
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185 changes: 85 additions & 100 deletions metrics_evaluation.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# import string\n",
Expand All @@ -36,8 +38,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
Expand All @@ -64,8 +68,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"metricslist = ['Brier', 'LogLoss']\n",
Expand All @@ -90,8 +96,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"mystery = {}\n",
Expand All @@ -108,8 +116,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"snphotcc = {}\n",
Expand All @@ -131,8 +141,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plasticc = {}\n",
Expand All @@ -141,38 +153,16 @@
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"# old_snphotcc_names = []\n",
"# for prefix in ['templates_', 'wavelets_']:\n",
"# for suffix in ['boost_forest', 'knn', 'nb', 'neural_network', 'svm']:\n",
"# old_snphotcc_names.append(prefix+suffix+'.dat')\n",
"\n",
"# for i in range(len(snphotcc_names)):\n",
"# name = old_snphotcc_names[i]\n",
"# fileloc = dirname+'classifications/'+name\n",
"# snphotcc_info = pd.read_csv(fileloc, sep=' ')\n",
"# full = snphotcc_info.set_index('Object').join(truth_snphotcc.set_index('Object'))\n",
"# name = snphotcc_names[i]\n",
" \n",
"# truth = full['Type'] - 1\n",
"# snphotcc_truth_table = proclam.metrics.util.det_to_prob(truth)\n",
"# fileloc = 'examples/'+name+'/truth_table_'+name+'.csv'\n",
"# with open(fileloc, 'wb') as truth_place:\n",
"# np.savetxt(fileloc, snphotcc_truth_table, delimiter=' ')\n",
" \n",
"# probs = full[['1', '2', '3']]\n",
"# fileloc = 'examples/'+name+'/predicted_prob_'+name+'.csv'\n",
"# probs.to_csv(fileloc, sep=' ', index=False, header=True)"
]
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# more_names = snphotcc_names\n",
Expand All @@ -183,8 +173,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def make_class_pairs(data_info_dict):\n",
Expand All @@ -201,11 +193,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'label': 'ProClaM', 'names': ['Idealized', 'Guess', 'Tunnel', 'Broadbrush', 'Cruise', 'SubsumedTo', 'SubsumedFrom'], 'dirname': 'examples/ProClaM/', 'classifications': ['Idealized/predicted_prob_Idealized.csv', 'Guess/predicted_prob_Guess.csv', 'Tunnel/predicted_prob_Tunnel.csv', 'Broadbrush/predicted_prob_Broadbrush.csv', 'Cruise/predicted_prob_Cruise.csv', 'SubsumedTo/predicted_prob_SubsumedTo.csv', 'SubsumedFrom/predicted_prob_SubsumedFrom.csv'], 'truth_tables': ['Idealized/truth_table_Idealized.csv', 'Guess/truth_table_Guess.csv', 'Tunnel/truth_table_Tunnel.csv', 'Broadbrush/truth_table_Broadbrush.csv', 'Cruise/truth_table_Cruise.csv', 'SubsumedTo/truth_table_SubsumedTo.csv', 'SubsumedFrom/truth_table_SubsumedFrom.csv']}\n"
]
}
],
"source": [
"for dataset in [mystery, snphotcc, plasticc]:\n",
"for dataset in [ plasticc]: #mystery, snphotcc,\n",
" dataset = make_file_locs(dataset)\n",
" dataset['class_pairs'] = make_class_pairs(dataset)"
]
Expand All @@ -221,8 +223,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def plot_cm(probs, truth, name, loc=''):\n",
Expand All @@ -234,14 +238,17 @@
" plt.ylabel('true class')\n",
" plt.colorbar()\n",
" plt.title(name)\n",
" plt.savefig(loc+name+'_cm.png')\n",
" plt.close()"
" #plt.savefig(loc+name+'_cm.png')\n",
" plt.show()\n",
" #plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
Expand All @@ -263,6 +270,7 @@
" nobj_truth = np.shape(truth_values)[0]\n",
" nclass_truth = np.shape(truth_values)[1]\n",
" tvec = np.where(truth_values==1)[1]\n",
" print(tvec)\n",
"# if nclass_truth!= nclass:\n",
"# print('Truth table of size %i x %i and prob matrix of size %i x %i do not match up in size'%(nobj,nclass,nobj_truth,nclass_truth))\n",
"# else:\n",
Expand All @@ -274,8 +282,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def make_patch_spines_invisible(ax):\n",
Expand Down Expand Up @@ -324,6 +334,7 @@
" plt.legend(handles, metric_names)\n",
" plt.suptitle(title)\n",
" plt.savefig(fileloc)\n",
" plt.show()\n",
" return"
]
},
Expand All @@ -336,13 +347,28 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "KeyError",
"evalue": "'class_pairs'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-15f56f172c88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmystery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msnphotcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplasticc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetricslist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpair\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'class_pairs'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpair\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprobm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruthv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_class_pairs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpair\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#loc=dataset['dirname'], title=dataset['label']+' '+dataset['names'][cc])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'class_pairs'"
]
}
],
"source": [
"for dataset in [mystery, snphotcc, plasticc]:\n",
" data = np.empty((len(metricslist), len(dataset['names'])))\n",
" for cc, pair in enumerate(dataset['class_pairs']):\n",
" print(pair)\n",
" probm, truthv = read_class_pairs(pair, dataset, cc)#loc=dataset['dirname'], title=dataset['label']+' '+dataset['names'][cc])\n",
"# plot_cm(probm, truthv, str(cc), loc='./sandbox/')\n",
" det = proclam.metrics.util.prob_to_det(probm)\n",
Expand All @@ -361,55 +387,14 @@
"# metric_plot(dataset, metricslist, markerlist, colors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# more_data = np.empty((len(metricslist), len(more_names)))\n",
"# for cc, pair in enumerate(more_class_pairs):\n",
"# probm, truthv = read_class_pairs(pair, dirname)\n",
"# for count, metric in enumerate(metricslist):\n",
"# D = getattr(proclam.metrics, metric)()\n",
"# hm = D.evaluate(probm, truthv)\n",
"# more_data[count][cc] = hm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# metric_plot(more_names, metricslist, more_data, markerlist, colors, title='SNPhotCC', fileloc=dirname+'snphotccdata.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# data = np.empty((len(metricslist), len(names)))\n",
"# for cc, pair in enumerate(class_pairs):\n",
"# probm, truthv = read_class_pairs(pair, dirname)\n",
"# for count, metric in enumerate(metricslist):\n",
"# D = getattr(proclam.metrics, metric)()\n",
"# hm = D.evaluate(probm, truthv)\n",
"# data[count][cc] = hm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# metric_plot(names, metricslist, data, markerlist, colors, title='Mystery Dataset', fileloc=dirname+'mysterydata.png')"
]
"source": []
},
{
"cell_type": "code",
Expand Down Expand Up @@ -443,7 +428,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.8"
}
},
"nbformat": 4,
Expand Down
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