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beta-vae-normalizing-flows
Commit c1661dc9 authored by Kaan Güney Keklikçi's avatar Kaan Güney Keklikçi
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correct ks-test for nice

parent 8506e8f4
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beta-vae-normalizing-flows/thoracic-surgery/beta-vae-nice-thoracic-surgery.ipynb
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......@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
"execution_count": 198,
"execution_count": 200,
"id": "9f0b013a",
"metadata": {
"scrolled": true
......@@ -59,7 +59,7 @@
" (0.7775608374378459, 0.8840392521212448, 0.9452007992345052)]"
]
},
"execution_count": 198,
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
......@@ -86,7 +86,7 @@
"from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, BatchNormalization\n",
"from tensorflow.keras.losses import Loss\n",
"import scipy\n",
"import ks2d2s\n",
"from ks2d2s import ks2d2s\n",
"\n",
"tfd = tfp.distributions\n",
"tfb = tfp.bijectors\n",
......@@ -1359,18 +1359,22 @@
},
{
"cell_type": "code",
"execution_count": 197,
"execution_count": 226,
"id": "b31a919e",
"metadata": {},
"outputs": [],
"source": [
"# KS-test\n",
"prior_sample = prior.sample(X_train_copy.shape[0]).numpy()\n",
"post_sample = trainable_dist.sample(X_train_copy.shape[0]).numpy()\n",
"\n",
"with open('ks-test.txt', 'a') as f:\n",
" f.write('NICE FLOW\\n')\n",
" f.write('p-val[prior - target] = {}\\n'.format(ks2d2s(X_train_copy['PRE4'], X_train_copy['PRE5'],prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[trained - target] = {}\\n'.format(ks2d2s(X_train_copy['PRE4'], X_train_copy['PRE5'],samples['PRE4'], samples['PRE5'])))\n",
" f.write('p-val[training - qtrainable] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[training - qtrained] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], post_sample[...,0], post_sample[...,1])))\n",
" \n",
" f.write('p-val[testing - qtrainable] = {}\\n'.format(ks2d2s(X_test_np[...,0], X_test_np[...,1], prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[testing - qtrained] = {}\\n'.format(ks2d2s(X_test_np[...,0], X_test_np[...,1], post_sample[...,0], post_sample[...,1])))\n",
"f.close()"
]
},
......
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