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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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Showing with 10 additions and 6 deletions
beta-vae-normalizing-flows/thoracic-surgery/beta-vae-nice-thoracic-surgery.ipynb
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...@@ -39,7 +39,7 @@ ...@@ -39,7 +39,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 198, "execution_count": 200,
"id": "9f0b013a", "id": "9f0b013a",
"metadata": { "metadata": {
"scrolled": true "scrolled": true
...@@ -59,7 +59,7 @@ ...@@ -59,7 +59,7 @@
" (0.7775608374378459, 0.8840392521212448, 0.9452007992345052)]" " (0.7775608374378459, 0.8840392521212448, 0.9452007992345052)]"
] ]
}, },
"execution_count": 198, "execution_count": 200,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -86,7 +86,7 @@ ...@@ -86,7 +86,7 @@
"from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, BatchNormalization\n", "from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, BatchNormalization\n",
"from tensorflow.keras.losses import Loss\n", "from tensorflow.keras.losses import Loss\n",
"import scipy\n", "import scipy\n",
"import ks2d2s\n", "from ks2d2s import ks2d2s\n",
"\n", "\n",
"tfd = tfp.distributions\n", "tfd = tfp.distributions\n",
"tfb = tfp.bijectors\n", "tfb = tfp.bijectors\n",
...@@ -1359,18 +1359,22 @@ ...@@ -1359,18 +1359,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 197, "execution_count": 226,
"id": "b31a919e", "id": "b31a919e",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# KS-test\n", "# KS-test\n",
"prior_sample = prior.sample(X_train_copy.shape[0]).numpy()\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", "\n",
"with open('ks-test.txt', 'a') as f:\n", "with open('ks-test.txt', 'a') as f:\n",
" f.write('NICE FLOW\\n')\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[training - qtrainable] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], 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 - 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()" "f.close()"
] ]
}, },
......
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