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“kjmayer”
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misc figure edits
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+343
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‎interpret/interpret-confvsacc-plot.ipynb‎

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‎interpret/interpret-data.ipynb‎

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"execution_count": 1,
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"id": "4b66c4fa-5299-467a-809d-cb3fc8d30589",
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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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"2023-12-06 16:03:51.283762: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
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"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
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"2023-12-06 16:03:52.716386: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import xarray as xr\n",
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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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"execution_count": 2,
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"id": "09f14733-e325-454d-9f75-245f84158ac5",
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"metadata": {},
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"outputs": [],
@@ -145,6 +135,260 @@
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"### Analyze Data"
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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": 7,
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"id": "d823bea1-4b20-4fc0-8a71-4784aad63040",
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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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"LEAD: 5\n",
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"all samples:\n",
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"(11534,)\n",
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"361/361 [==============================] - 0s 785us/step\n",
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"confident shape:\n",
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"(2307,)\n",
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"LEAD: 6\n",
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"all samples:\n",
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"(11538,)\n",
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"361/361 [==============================] - 0s 883us/step\n",
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"confident shape:\n",
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"(2308,)\n",
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"LEAD: 7\n",
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"all samples:\n",
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"(11532,)\n",
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"361/361 [==============================] - 0s 878us/step\n",
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"confident shape:\n",
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"(2307,)\n",
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"LEAD: 8\n",
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"all samples:\n",
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"(11514,)\n",
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"360/360 [==============================] - 0s 950us/step\n",
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"confident shape:\n",
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"(2303,)\n",
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"LEAD: 9\n",
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"all samples:\n",
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"(11502,)\n",
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"360/360 [==============================] - 0s 1ms/step\n",
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"confident shape:\n",
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"(2301,)\n",
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"LEAD: 10\n",
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"all samples:\n",
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"(11502,)\n",
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"360/360 [==============================] - 0s 922us/step\n",
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"confident shape:\n",
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"(2301,)\n",
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"LEAD: 11\n",
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"all samples:\n",
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"(11500,)\n",
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"360/360 [==============================] - 0s 824us/step\n",
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"confident shape:\n",
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"(2300,)\n",
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"LEAD: 12\n",
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"all samples:\n",
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"(11500,)\n",
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"360/360 [==============================] - 0s 868us/step\n",
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"confident shape:\n",
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"(2300,)\n",
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"LEAD: 13\n",
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"all samples:\n",
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"(11502,)\n",
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"360/360 [==============================] - 0s 913us/step\n",
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"confident shape:\n",
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"(2301,)\n",
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"LEAD: 14\n",
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"all samples:\n",
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"(11520,)\n",
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"360/360 [==============================] - 1s 2ms/step\n",
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"confident shape:\n",
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"(2304,)\n",
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"LEAD: 15\n",
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"all samples:\n",
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"(11538,)\n",
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"361/361 [==============================] - 0s 998us/step\n",
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"confident shape:\n",
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"(2308,)\n",
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"LEAD: 16\n",
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"all samples:\n",
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"(11548,)\n",
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"361/361 [==============================] - 0s 823us/step\n",
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"confident shape:\n",
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"(2310,)\n",
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"LEAD: 17\n",
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"all samples:\n",
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"(11542,)\n",
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"361/361 [==============================] - 0s 866us/step\n",
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"confident shape:\n",
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"(2309,)\n",
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"LEAD: 18\n",
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"all samples:\n",
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"(11556,)\n",
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"362/362 [==============================] - 1s 1ms/step\n",
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"confident shape:\n",
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"(2311,)\n",
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"LEAD: 19\n",
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"all samples:\n",
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"(11564,)\n",
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"362/362 [==============================] - 0s 925us/step\n",
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"confident shape:\n",
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"(2313,)\n",
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"LEAD: 20\n",
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"all samples:\n",
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"(11570,)\n",
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"362/362 [==============================] - 0s 839us/step\n",
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"confident shape:\n",
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"(2314,)\n",
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"LEAD: 21\n",
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"all samples:\n",
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"(11580,)\n",
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"362/362 [==============================] - 0s 899us/step\n",
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"confident shape:\n",
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"(2316,)\n",
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"LEAD: 22\n",
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"all samples:\n",
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"(11588,)\n",
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"363/363 [==============================] - 0s 1ms/step\n",
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"confident shape:\n",
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"(2318,)\n",
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"LEAD: 23\n",
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"all samples:\n",
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"(11596,)\n",
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"363/363 [==============================] - 0s 829us/step\n",
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"confident shape:\n",
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"(2319,)\n",
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"LEAD: 24\n",
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"all samples:\n",
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"(11592,)\n",
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"363/363 [==============================] - 0s 859us/step\n",
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"confident shape:\n",
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"(2319,)\n",
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"LEAD: 25\n",
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"all samples:\n",
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"(11600,)\n",
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"363/363 [==============================] - 0s 798us/step\n",
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"confident shape:\n",
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"(2320,)\n",
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"LEAD: 26\n",
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"all samples:\n",
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"(11594,)\n",
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"363/363 [==============================] - 0s 832us/step\n",
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"confident shape:\n",
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"(2319,)\n",
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"LEAD: 27\n",
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"all samples:\n",
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"(11602,)\n",
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"363/363 [==============================] - 0s 1ms/step\n",
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"confident shape:\n",
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"(2321,)\n",
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"LEAD: 28\n",
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"all samples:\n",
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"(11604,)\n",
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"363/363 [==============================] - 0s 902us/step\n",
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"confident shape:\n",
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"(2321,)\n",
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"LEAD: 29\n",
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"all samples:\n",
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"(11616,)\n",
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"363/363 [==============================] - 0s 785us/step\n",
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"confident shape:\n",
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"(2323,)\n",
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"LEAD: 30\n",
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"all samples:\n",
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"(11618,)\n",
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"364/364 [==============================] - 0s 999us/step\n",
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"confident shape:\n",
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"(2324,)\n"
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]
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}
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],
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"source": [
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"# print sizes of testing data for all and 20%:\n",
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"for l in LEADS:\n",
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" print('LEAD: '+str(l)) #+'\\nAVG: '+str(a))\n",
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" for a in AVGS[:1]:\n",
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" X1test, X2test, Ytest = get_testing(N_z500runmean=a,\n",
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" LEAD=l)\n",
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" print('all samples:')\n",
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" print(np.shape(Ytest))\n",
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"\n",
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" INPUT_SHAPE1 = np.shape(X1test)[1:][0]\n",
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" INPUT_SHAPE2 = np.shape(X2test)[1:][0]\n",
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"\n",
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" for s in SEEDS[:1]:\n",
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" # ENSO MODEL\n",
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" model1, input1 = build_model(s,\n",
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" DROPOUT_RATE,\n",
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" RIDGE1,\n",
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" HIDDENS1,\n",
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" INPUT_SHAPE1,\n",
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" MODELNAME1)\n",
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" # MJO MODEL\n",
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" model2, input2 = build_model(s,\n",
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" DROPOUT_RATE,\n",
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" RIDGE2,\n",
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" HIDDENS2,\n",
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" INPUT_SHAPE2,\n",
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" MODELNAME2) \n",
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" # COMBINE ENSO & MJO MODEL\n",
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" model = fullmodel(model1, model2,\n",
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" input1, input2,\n",
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" s)\n",
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"\n",
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" MODEL_FINAME = 'DOY_LEAD_'+str(l)+'_AVG_'+str(a)+'__0000'+str(s)+'.h5'\n",
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" model.load_weights(MODEL_DIR+MODEL_FINAME)\n",
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"\n",
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" model_rawpreds = model.predict((X1test,X2test)) \n",
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" \n",
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" conf = np.max(model_rawpreds,axis=-1)\n",
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" predval = np.argmax(model_rawpreds,axis=-1)\n",
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" \n",
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" # ------- confident predictions --------------------------------------------------------\n",
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" per = 80\n",
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" conf_thresh = np.percentile(conf,q=per)\n",
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" # -------- confident [i_conf_predval] --------\n",
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" i_conf_predval = np.where(conf > conf_thresh)[0]\n",
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" print('confident shape:')\n",
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" print(np.shape(i_conf_predval))\n",
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" \n",
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"\n",
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" "
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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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"id": "4ea1d3dd-e07f-471d-95c6-cf51bde8b57a",
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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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"cell_type": "code",
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"execution_count": null,
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"id": "9b02cf81-e50c-43fc-b80e-0f3a70777a4a",
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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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"cell_type": "code",
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"execution_count": null,
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"id": "73b0f921-6e58-4ae7-b1a5-25dfe77da21e",
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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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"cell_type": "code",
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"execution_count": null,
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"id": "f8a92820-907d-4fb2-8b92-155f8105d6cb",
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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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"cell_type": "code",
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"execution_count": 3,

‎interpret/interpret-modelcont-plot.ipynb‎

Lines changed: 47 additions & 44 deletions
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