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Added Binarizer in Preprocessing and Hyperparameter optimization in pipeline

master
Tuan-Dat Tran 2021-05-18 17:18:26 +00:00
parent 8b647de135
commit b127bc6b84
4 changed files with 943 additions and 356 deletions
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0-pilot-project/MNIST-kNN-pipeline.ipynb
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0-pilot-project/MNIST-kNN-prepoc-vis.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "804dacb6", "id": "f2885b56",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Load MNIST dataset" "### Load MNIST dataset"
@ -11,7 +11,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"id": "7d09885b", "id": "805542e2",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -23,7 +23,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"id": "bf4121a0", "id": "26d38ac4",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -35,7 +35,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"id": "71d91fd8", "id": "749c3ec9",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -46,7 +46,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"id": "1dc68441", "id": "810daa97",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -74,7 +74,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,
"id": "2c7a4966", "id": "48b3d387",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -83,7 +83,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "e2684670", "id": "96ed2a09",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Fix labels" "### Fix labels"
@ -92,7 +92,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 113, "execution_count": 113,
"id": "dbdbc64f", "id": "4c537948",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -105,7 +105,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"id": "4c94aaf6", "id": "4d138b55",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -116,7 +116,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 126, "execution_count": 126,
"id": "f1ba6703", "id": "b5284df4",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -129,7 +129,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "eec5415d", "id": "a572aebf",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Prepare data for machine learning" "### Prepare data for machine learning"
@ -137,7 +137,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "27ed1cdb", "id": "3b5bc85f",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Identify Train Set and Test Set" "### Identify Train Set and Test Set"
@ -146,7 +146,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 9,
"id": "09446324", "id": "3db579b6",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -173,7 +173,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "2c3041ac", "id": "7c035dc8",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Pipeline Declaration" "## Pipeline Declaration"
@ -181,14 +181,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 140,
"id": "99f24362", "id": "4bd42611",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.pipeline import Pipeline\n", "from sklearn.pipeline import Pipeline\n",
"from sklearn.decomposition import PCA\n", "from sklearn.decomposition import PCA\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, Binarizer\n",
"from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import cross_val_predict\n", "from sklearn.model_selection import cross_val_predict\n",
"from sklearn.metrics import classification_report, accuracy_score\n", "from sklearn.metrics import classification_report, accuracy_score\n",
@ -200,17 +200,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 122, "execution_count": 143,
"id": "a6ee7588", "id": "c347de5b",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"(3, 3)" "(4, 4)"
] ]
}, },
"execution_count": 122, "execution_count": 143,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -219,12 +219,14 @@
"names = ['scaler', \n", "names = ['scaler', \n",
" 'minmax', \n", " 'minmax', \n",
" 'maxabs',\n", " 'maxabs',\n",
" 'bin'\n",
" ]\n", " ]\n",
"\n", "\n",
"classifiers = [\n", "classifiers = [\n",
" Pipeline([('scaler', StandardScaler())]),\n", " Pipeline([('scaler', StandardScaler())]),\n",
" Pipeline([('minmax', MinMaxScaler())]),\n", " Pipeline([('minmax', MinMaxScaler())]),\n",
" Pipeline([('maxabs', MaxAbsScaler())]),\n", " Pipeline([('maxabs', MaxAbsScaler())]),\n",
" Pipeline([('bin', Binarizer())]),\n",
"]\n", "]\n",
"\n", "\n",
"len(names), len(classifiers)" "len(names), len(classifiers)"
@ -232,7 +234,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "650c96b4", "id": "bd566c8d",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Crossvalidation" "# Crossvalidation"
@ -241,7 +243,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 123, "execution_count": 123,
"id": "584cb66b", "id": "77f6d632",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -258,7 +260,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 128, "execution_count": 128,
"id": "0b815be6", "id": "bb8eb2e0",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -290,7 +292,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 132, "execution_count": 132,
"id": "8640f2ad", "id": "70f4411f",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -320,7 +322,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 133, "execution_count": 133,
"id": "3ef8cf89", "id": "70f3533d",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -350,7 +352,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 134, "execution_count": 134,
"id": "fe0246a2", "id": "2ec8d300",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -377,10 +379,71 @@
"a = cv(2)" "a = cv(2)"
] ]
}, },
{
"cell_type": "code",
"execution_count": 137,
"id": "b1f285c2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n",
"3\n"
]
},
{
"data": {
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"text/plain": [
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},
"metadata": {
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],
"source": [
"plot_digit(cv(2)-cv(1))"
]
},
{
"cell_type": "code",
"execution_count": 145,
"id": "2c7323b6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3\n"
]
},
{
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"metadata": {
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}
],
"source": [
"a = cv(3)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "87a073e1", "id": "b608bd89",
"metadata": {}, "metadata": {},
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"source": [] "source": []

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0-pilot-project/gs_result.csv Normal file
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@ -0,0 +1,25 @@
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23,27.638134558995564,4.867014100523201,734.1344640254974,31.858155565000086,chebyshev,11,distance,"{'metric': 'chebyshev', 'n_neighbors': 11, 'weights': 'distance'}",0.7919858573954036,0.7913965822038892,0.7912782599378549,0.7915535665123826,0.00030946900255889454,20
18,1.1796804269154866,0.7346351248980267,1684.378450314204,9.234898911008068,chebyshev,5,uniform,"{'metric': 'chebyshev', 'n_neighbors': 5, 'weights': 'uniform'}",0.783575293298334,0.7888251995500081,0.7891353262616522,0.7871786063699981,0.0025510708161656103,21
20,0.49048900604248047,0.05161896574410176,1679.1593386332195,1.1331046631334827,chebyshev,7,uniform,"{'metric': 'chebyshev', 'n_neighbors': 7, 'weights': 'uniform'}",0.7845395617935395,0.7868966625595971,0.7855994856959178,0.7856785700163514,0.0009639058573122337,22
16,0.6336509386698405,0.19319871048710188,1672.988091468811,6.364915306048909,chebyshev,3,uniform,"{'metric': 'chebyshev', 'n_neighbors': 3, 'weights': 'uniform'}",0.7702898162532812,0.7848074141533187,0.7878495660559306,0.7809822654875102,0.007662028670422474,23
22,21.680665890375774,3.469126600885206,794.0563353697459,13.966075572059058,chebyshev,11,uniform,"{'metric': 'chebyshev', 'n_neighbors': 11, 'weights': 'uniform'}",0.78052177639685,0.7794503669577328,0.7796528447444552,0.7798749960330128,0.0004647529399678837,24
1 mean_fit_time std_fit_time mean_score_time std_score_time param_metric param_n_neighbors param_weights params split0_test_score split1_test_score split2_test_score mean_test_score std_test_score rank_test_score
2 1 0.6206035614013672 0.09060096919428257 1070.8031173547108 58.40420733215167 euclidean 3 distance {'metric': 'euclidean', 'n_neighbors': 3, 'weights': 'distance'} 0.9682862806021321 0.970589810896234 0.9706953819779278 0.9698571578254312 0.001111613767256926 1
3 0 0.3251535892486572 0.127680187632828 1156.720083475113 103.6800414474859 euclidean 3 uniform {'metric': 'euclidean', 'n_neighbors': 3, 'weights': 'uniform'} 0.9673755825788826 0.968714844377779 0.9698917818493518 0.9686607362686711 0.0010279463208361468 2
4 3 0.581209659576416 0.10390654521562699 1097.628236611684 49.50409271683481 euclidean 5 distance {'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'distance'} 0.9670541597471474 0.9677505758825735 0.9693024750883961 0.968035736906039 0.0009397581516544441 3
5 5 0.3833950360616048 0.09814461155343633 1019.8115065892538 49.683140897762506 euclidean 7 distance {'metric': 'euclidean', 'n_neighbors': 7, 'weights': 'distance'} 0.9656077570043392 0.9673755825788826 0.9683917282760098 0.9671250226197438 0.0011502780041406022 4
6 2 0.5214482148488363 0.1338813546209848 1234.2422309716542 11.319769031350406 euclidean 5 uniform {'metric': 'euclidean', 'n_neighbors': 5, 'weights': 'uniform'} 0.9659291798360744 0.9661970321958536 0.9679095681988642 0.9666785934102641 0.0008772724469576776 5
7 4 0.38950196901957196 0.10594237544622603 1119.6926170190175 64.25975988670193 euclidean 7 uniform {'metric': 'euclidean', 'n_neighbors': 7, 'weights': 'uniform'} 0.9643756361493545 0.9659827503080303 0.9674809814636237 0.9659464559736696 0.0012680116558989624 6
8 7 0.44870662689208984 0.1539214539086042 1095.8349254131317 95.22915205238016 euclidean 11 distance {'metric': 'euclidean', 'n_neighbors': 11, 'weights': 'distance'} 0.9646434885091337 0.9632506562382814 0.9647487410264652 0.9642142952579601 0.0006827491697964685 7
9 6 0.6263217926025391 0.045471428070326544 1231.947474082311 10.357145120994318 euclidean 11 uniform {'metric': 'euclidean', 'n_neighbors': 11, 'weights': 'uniform'} 0.9637327904858842 0.961911394439385 0.9634094074788385 0.9630178641347026 0.000793452595644972 8
10 9 0.6597681840260824 0.08367258300110442 1603.911631822586 11.793869730582724 manhattan 3 distance {'metric': 'manhattan', 'n_neighbors': 3, 'weights': 'distance'} 0.961161407832003 0.9625006696308994 0.9638915675559842 0.9625178816729623 0.0011146494876286455 9
11 11 0.743593692779541 0.12585558461284738 1623.6791274547577 20.225285349079297 manhattan 5 distance {'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'distance'} 0.9598757165050624 0.9628220924626346 0.9633558341369335 0.9620178810348768 0.001530331488777754 10
12 8 0.5049304962158203 0.03915105846837655 1631.9402144749959 11.72758222926691 manhattan 3 uniform {'metric': 'manhattan', 'n_neighbors': 3, 'weights': 'uniform'} 0.9600899983928859 0.9608935554722237 0.9630343940855031 0.9613393159835374 0.0012426834737134394 11
13 13 0.7563843727111816 0.0361495198811297 1650.8071510791779 2.759115546096175 manhattan 7 distance {'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'distance'} 0.9593935822574597 0.9614828306637382 0.9609986070931105 0.9606250066714361 0.0008929063713852963 12
14 10 0.5714000860850016 0.200821086554179 1670.9476985931396 21.43965257048912 manhattan 5 uniform {'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'uniform'} 0.9581078909305191 0.9610006964161354 0.9616950605378763 0.9602678826281769 0.0015534281409951467 13
15 12 0.7014182408650717 0.07630416360771637 1602.6147842407227 13.998164063202559 manhattan 7 uniform {'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'uniform'} 0.9576793271548723 0.9597150050891948 0.9595521268616737 0.9589821530352469 0.0009236336882771337 14
16 15 0.5463813940684 0.20986627389723325 1621.221689303716 10.57220404161948 manhattan 11 distance {'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'distance'} 0.9573043338511812 0.957036481491402 0.9578913532626165 0.9574107228683998 0.00035701578260487497 15
17 14 0.659561554590861 0.16166694480866448 1647.0242857138317 15.482865038769452 manhattan 11 uniform {'metric': 'manhattan', 'n_neighbors': 11, 'weights': 'uniform'} 0.9554829378046821 0.9555900787485938 0.9565520197149898 0.9558750120894218 0.00048071078572838955 16
18 19 5.247008244196574 3.982239002878492 1610.0853408177693 4.880005667545898 chebyshev 5 distance {'metric': 'chebyshev', 'n_neighbors': 5, 'weights': 'distance'} 0.798253602614239 0.8040392135854717 0.8051001821493625 0.8024643327830244 0.003008776051168824 17
19 21 20.442909558614094 10.268306972202161 1091.167144536972 373.74252764500056 chebyshev 7 distance {'metric': 'chebyshev', 'n_neighbors': 7, 'weights': 'distance'} 0.7973429045909894 0.8016285423474581 0.7997964213007608 0.7995892894130695 0.0017557240593918855 18
20 17 3.0204404989878335 3.242756624385942 1615.3644462426503 47.3978612160567 chebyshev 3 distance {'metric': 'chebyshev', 'n_neighbors': 3, 'weights': 'distance'} 0.7828788771629078 0.80393207264156 0.8089038894246223 0.7985716130763634 0.011280549953147328 19
21 23 27.638134558995564 4.867014100523201 734.1344640254974 31.858155565000086 chebyshev 11 distance {'metric': 'chebyshev', 'n_neighbors': 11, 'weights': 'distance'} 0.7919858573954036 0.7913965822038892 0.7912782599378549 0.7915535665123826 0.00030946900255889454 20
22 18 1.1796804269154866 0.7346351248980267 1684.378450314204 9.234898911008068 chebyshev 5 uniform {'metric': 'chebyshev', 'n_neighbors': 5, 'weights': 'uniform'} 0.783575293298334 0.7888251995500081 0.7891353262616522 0.7871786063699981 0.0025510708161656103 21
23 20 0.49048900604248047 0.05161896574410176 1679.1593386332195 1.1331046631334827 chebyshev 7 uniform {'metric': 'chebyshev', 'n_neighbors': 7, 'weights': 'uniform'} 0.7845395617935395 0.7868966625595971 0.7855994856959178 0.7856785700163514 0.0009639058573122337 22
24 16 0.6336509386698405 0.19319871048710188 1672.988091468811 6.364915306048909 chebyshev 3 uniform {'metric': 'chebyshev', 'n_neighbors': 3, 'weights': 'uniform'} 0.7702898162532812 0.7848074141533187 0.7878495660559306 0.7809822654875102 0.007662028670422474 23
25 22 21.680665890375774 3.469126600885206 794.0563353697459 13.966075572059058 chebyshev 11 uniform {'metric': 'chebyshev', 'n_neighbors': 11, 'weights': 'uniform'} 0.78052177639685 0.7794503669577328 0.7796528447444552 0.7798749960330128 0.0004647529399678837 24

25
0-pilot-project/gs_result_filtered.csv Normal file
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@ -0,0 +1,25 @@
,rank_test_score,mean_fit_time,param_n_neighbors,param_metric,param_weights,mean_test_score
1,1,0.6206035614013672,3,euclidean,distance,0.9698571578254312
0,2,0.3251535892486572,3,euclidean,uniform,0.9686607362686711
3,3,0.581209659576416,5,euclidean,distance,0.968035736906039
5,4,0.3833950360616048,7,euclidean,distance,0.9671250226197438
2,5,0.5214482148488363,5,euclidean,uniform,0.9666785934102641
4,6,0.38950196901957196,7,euclidean,uniform,0.9659464559736696
7,7,0.44870662689208984,11,euclidean,distance,0.9642142952579601
6,8,0.6263217926025391,11,euclidean,uniform,0.9630178641347026
9,9,0.6597681840260824,3,manhattan,distance,0.9625178816729623
11,10,0.743593692779541,5,manhattan,distance,0.9620178810348768
8,11,0.5049304962158203,3,manhattan,uniform,0.9613393159835374
13,12,0.7563843727111816,7,manhattan,distance,0.9606250066714361
10,13,0.5714000860850016,5,manhattan,uniform,0.9602678826281769
12,14,0.7014182408650717,7,manhattan,uniform,0.9589821530352469
15,15,0.5463813940684,11,manhattan,distance,0.9574107228683998
14,16,0.659561554590861,11,manhattan,uniform,0.9558750120894218
19,17,5.247008244196574,5,chebyshev,distance,0.8024643327830244
21,18,20.442909558614094,7,chebyshev,distance,0.7995892894130695
17,19,3.0204404989878335,3,chebyshev,distance,0.7985716130763634
23,20,27.638134558995564,11,chebyshev,distance,0.7915535665123826
18,21,1.1796804269154866,5,chebyshev,uniform,0.7871786063699981
20,22,0.49048900604248047,7,chebyshev,uniform,0.7856785700163514
16,23,0.6336509386698405,3,chebyshev,uniform,0.7809822654875102
22,24,21.680665890375774,11,chebyshev,uniform,0.7798749960330128
1 rank_test_score mean_fit_time param_n_neighbors param_metric param_weights mean_test_score
2 1 1 0.6206035614013672 3 euclidean distance 0.9698571578254312
3 0 2 0.3251535892486572 3 euclidean uniform 0.9686607362686711
4 3 3 0.581209659576416 5 euclidean distance 0.968035736906039
5 5 4 0.3833950360616048 7 euclidean distance 0.9671250226197438
6 2 5 0.5214482148488363 5 euclidean uniform 0.9666785934102641
7 4 6 0.38950196901957196 7 euclidean uniform 0.9659464559736696
8 7 7 0.44870662689208984 11 euclidean distance 0.9642142952579601
9 6 8 0.6263217926025391 11 euclidean uniform 0.9630178641347026
10 9 9 0.6597681840260824 3 manhattan distance 0.9625178816729623
11 11 10 0.743593692779541 5 manhattan distance 0.9620178810348768
12 8 11 0.5049304962158203 3 manhattan uniform 0.9613393159835374
13 13 12 0.7563843727111816 7 manhattan distance 0.9606250066714361
14 10 13 0.5714000860850016 5 manhattan uniform 0.9602678826281769
15 12 14 0.7014182408650717 7 manhattan uniform 0.9589821530352469
16 15 15 0.5463813940684 11 manhattan distance 0.9574107228683998
17 14 16 0.659561554590861 11 manhattan uniform 0.9558750120894218
18 19 17 5.247008244196574 5 chebyshev distance 0.8024643327830244
19 21 18 20.442909558614094 7 chebyshev distance 0.7995892894130695
20 17 19 3.0204404989878335 3 chebyshev distance 0.7985716130763634
21 23 20 27.638134558995564 11 chebyshev distance 0.7915535665123826
22 18 21 1.1796804269154866 5 chebyshev uniform 0.7871786063699981
23 20 22 0.49048900604248047 7 chebyshev uniform 0.7856785700163514
24 16 23 0.6336509386698405 3 chebyshev uniform 0.7809822654875102
25 22 24 21.680665890375774 11 chebyshev uniform 0.7798749960330128