diff --git a/code/simclr-pytorch-reefs/evaluation/embeddings/cluster.ipynb b/code/simclr-pytorch-reefs/evaluation/embeddings/cluster.ipynb index e6aeb61..9e12120 100644 --- a/code/simclr-pytorch-reefs/evaluation/embeddings/cluster.ipynb +++ b/code/simclr-pytorch-reefs/evaluation/embeddings/cluster.ipynb @@ -123,16 +123,6 @@ " print(f\"Locations: {list(locations.keys())}\\n\")" ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#specific_df = datasets['ReefCLR']['australia']\n", - "#print(specific_df.head())" - ] - }, { "cell_type": "code", "execution_count": 17, @@ -192,219 +182,6 @@ "results" ] }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CategoryLocationNum_ClustersChi2_StatP_Value
0ImageNetaustralia64516.4450822.032088e-72
1ImageNetbermuda10789.0553423.808284e-131
2ImageNetflorida532823.6988350.000000e+00
3ImageNetfrench_polynesia245760.5813850.000000e+00
4ImageNetindonesia351453.3478377.585708e-284
5ImageNetkenya1018.8624262.638840e-02
6ReefCLRaustralia61254.2418499.528436e-26
7ReefCLRbermuda81307.8796708.622588e-247
8ReefCLRflorida482647.5200800.000000e+00
9ReefCLRfrench_polynesia315820.8339450.000000e+00
10ReefCLRindonesia381459.2731081.153653e-282
11ReefCLRkenya27290.8692451.403893e-46
12VGGishaustralia691574.0726356.953275e-284
13VGGishbermuda352439.3354220.000000e+00
14VGGishflorida523568.1298600.000000e+00
15VGGishfrench_polynesia347972.7095990.000000e+00
16VGGishindonesia381970.6422480.000000e+00
17VGGishkenya26168.6350322.857384e-23
\n", - "
" - ], - "text/plain": [ - " Category Location Num_Clusters Chi2_Stat P_Value\n", - "0 ImageNet australia 64 516.445082 2.032088e-72\n", - "1 ImageNet bermuda 10 789.055342 3.808284e-131\n", - "2 ImageNet florida 53 2823.698835 0.000000e+00\n", - "3 ImageNet french_polynesia 24 5760.581385 0.000000e+00\n", - "4 ImageNet indonesia 35 1453.347837 7.585708e-284\n", - "5 ImageNet kenya 10 18.862426 2.638840e-02\n", - "6 ReefCLR australia 61 254.241849 9.528436e-26\n", - "7 ReefCLR bermuda 8 1307.879670 8.622588e-247\n", - "8 ReefCLR florida 48 2647.520080 0.000000e+00\n", - "9 ReefCLR french_polynesia 31 5820.833945 0.000000e+00\n", - "10 ReefCLR indonesia 38 1459.273108 1.153653e-282\n", - "11 ReefCLR kenya 27 290.869245 1.403893e-46\n", - "12 VGGish australia 69 1574.072635 6.953275e-284\n", - "13 VGGish bermuda 35 2439.335422 0.000000e+00\n", - "14 VGGish florida 52 3568.129860 0.000000e+00\n", - "15 VGGish french_polynesia 34 7972.709599 0.000000e+00\n", - "16 VGGish indonesia 38 1970.642248 0.000000e+00\n", - "17 VGGish kenya 26 168.635032 2.857384e-23" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, { "cell_type": "code", "execution_count": 37, diff --git a/code/simclr-pytorch-reefs/evaluation/embeddings/simple_ml.ipynb b/code/simclr-pytorch-reefs/evaluation/embeddings/simple_ml.ipynb index dd999ce..f858e28 100644 --- a/code/simclr-pytorch-reefs/evaluation/embeddings/simple_ml.ipynb +++ b/code/simclr-pytorch-reefs/evaluation/embeddings/simple_ml.ipynb @@ -351,344 +351,6 @@ "Adds embedding and country column then sorts by these" ] }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "results_df = pd.read_csv('/home/ben/reef-audio-representation-learning/code/simclr-pytorch-reefs/evaluation/embeddings/Results/RF_results-20230830_231531.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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FilenameTest AccuracyTest PrecisionTest RecallTest F1Train AccuracyTrain PrecisionTrain RecallTrain F1
0ImageNet-australia-embeddings.csv0.7433330.7438640.7433330.7431941.0000001.0000001.0000001.000000
1ImageNet-bermuda-embeddings.csv0.5255680.4759270.5255680.4859340.9658850.9652190.9658850.965173
2ImageNet-florida-embeddings.csv0.9158420.9150130.9158420.9150431.0000001.0000001.0000001.000000
3ImageNet-french_polynesia-embeddings.csv0.9688200.9688220.9688200.9688191.0000001.0000001.0000001.000000
4ImageNet-indonesia-embeddings.csv0.9800500.9796750.9800500.9795931.0000001.0000001.0000001.000000
5ImageNet-kenya-embeddings.csv0.7647060.8235290.7647060.7030811.0000001.0000001.0000001.000000
6ReefCLR-australia-embeddings.csv0.6541670.6546930.6541670.6538721.0000001.0000001.0000001.000000
7ReefCLR-bermuda-embeddings.csv0.5852270.5507040.5852270.5651660.9587780.9573390.9587780.957557
8ReefCLR-florida-embeddings.csv0.9188120.9184580.9188120.9173621.0000001.0000001.0000001.000000
9ReefCLR-french_polynesia-embeddings.csv0.9565700.9565800.9565700.9565701.0000001.0000001.0000001.000000
10ReefCLR-indonesia-embeddings.csv0.9775560.9773410.9775560.9774331.0000001.0000001.0000001.000000
11ReefCLR-kenya-embeddings.csv0.8545450.8528210.8545450.8498901.0000001.0000001.0000001.000000
12VGGish-australia-embeddings.csv0.8000000.8003340.8000000.7999441.0000001.0000001.0000001.000000
13VGGish-bermuda-embeddings.csv0.6619320.6411330.6619320.6342800.9538020.9529310.9538020.953206
14VGGish-florida-embeddings.csv0.9584160.9582930.9584160.9583371.0000001.0000001.0000001.000000
15VGGish-french_polynesia-embeddings.csv0.9677060.9677630.9677060.9677051.0000001.0000001.0000001.000000
16VGGish-indonesia-embeddings.csv0.9975060.9975130.9975060.9974931.0000001.0000001.0000001.000000
17VGGish-kenya-embeddings.csv0.8060610.8005350.8060610.7998541.0000001.0000001.0000001.000000
\n", - "
" - ], - "text/plain": [ - " Filename Test Accuracy Test Precision \\\n", - "0 ImageNet-australia-embeddings.csv 0.743333 0.743864 \n", - "1 ImageNet-bermuda-embeddings.csv 0.525568 0.475927 \n", - "2 ImageNet-florida-embeddings.csv 0.915842 0.915013 \n", - "3 ImageNet-french_polynesia-embeddings.csv 0.968820 0.968822 \n", - "4 ImageNet-indonesia-embeddings.csv 0.980050 0.979675 \n", - "5 ImageNet-kenya-embeddings.csv 0.764706 0.823529 \n", - "6 ReefCLR-australia-embeddings.csv 0.654167 0.654693 \n", - "7 ReefCLR-bermuda-embeddings.csv 0.585227 0.550704 \n", - "8 ReefCLR-florida-embeddings.csv 0.918812 0.918458 \n", - "9 ReefCLR-french_polynesia-embeddings.csv 0.956570 0.956580 \n", - "10 ReefCLR-indonesia-embeddings.csv 0.977556 0.977341 \n", - "11 ReefCLR-kenya-embeddings.csv 0.854545 0.852821 \n", - "12 VGGish-australia-embeddings.csv 0.800000 0.800334 \n", - "13 VGGish-bermuda-embeddings.csv 0.661932 0.641133 \n", - "14 VGGish-florida-embeddings.csv 0.958416 0.958293 \n", - "15 VGGish-french_polynesia-embeddings.csv 0.967706 0.967763 \n", - "16 VGGish-indonesia-embeddings.csv 0.997506 0.997513 \n", - "17 VGGish-kenya-embeddings.csv 0.806061 0.800535 \n", - "\n", - " Test Recall Test F1 Train Accuracy Train Precision Train Recall \\\n", - "0 0.743333 0.743194 1.000000 1.000000 1.000000 \n", - "1 0.525568 0.485934 0.965885 0.965219 0.965885 \n", - "2 0.915842 0.915043 1.000000 1.000000 1.000000 \n", - "3 0.968820 0.968819 1.000000 1.000000 1.000000 \n", - "4 0.980050 0.979593 1.000000 1.000000 1.000000 \n", - "5 0.764706 0.703081 1.000000 1.000000 1.000000 \n", - "6 0.654167 0.653872 1.000000 1.000000 1.000000 \n", - "7 0.585227 0.565166 0.958778 0.957339 0.958778 \n", - "8 0.918812 0.917362 1.000000 1.000000 1.000000 \n", - "9 0.956570 0.956570 1.000000 1.000000 1.000000 \n", - "10 0.977556 0.977433 1.000000 1.000000 1.000000 \n", - "11 0.854545 0.849890 1.000000 1.000000 1.000000 \n", - "12 0.800000 0.799944 1.000000 1.000000 1.000000 \n", - "13 0.661932 0.634280 0.953802 0.952931 0.953802 \n", - "14 0.958416 0.958337 1.000000 1.000000 1.000000 \n", - "15 0.967706 0.967705 1.000000 1.000000 1.000000 \n", - "16 0.997506 0.997493 1.000000 1.000000 1.000000 \n", - "17 0.806061 0.799854 1.000000 1.000000 1.000000 \n", - "\n", - " Train F1 \n", - "0 1.000000 \n", - "1 0.965173 \n", - "2 1.000000 \n", - "3 1.000000 \n", - "4 1.000000 \n", - "5 1.000000 \n", - "6 1.000000 \n", - "7 0.957557 \n", - "8 1.000000 \n", - "9 1.000000 \n", - "10 1.000000 \n", - "11 1.000000 \n", - "12 1.000000 \n", - "13 0.953206 \n", - "14 1.000000 \n", - "15 1.000000 \n", - "16 1.000000 \n", - "17 1.000000 " - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results_df" - ] - }, { "cell_type": "code", "execution_count": 41,