# read training data
df = pd.read_parquet('https://github.com/sky1ove/katlas_raw/raw/refs/heads/main/nbs/raw/combine_t5_kd.parquet').reset_index()
# read data contains info for split
info_df = Data.get_kinase_info().query('pseudo!="1"') # get non-pseudo kinaseTrain ML
A collection of machine learning tools
Setup
Utils
Splitter
get_splits
get_splits (df:pandas.core.frame.DataFrame, stratified:str=None, group:str=None, nfold:int=5, seed:int=123)
Split samples in a dataframe based on Stratified, Group, or StratifiedGroup Kfold method
| Type | Default | Details | |
|---|---|---|---|
| df | DataFrame | df contains info for split | |
| stratified | str | None | colname to make stratified kfold; sampling from different groups |
| group | str | None | colname to make group kfold; test and train are from different groups |
| nfold | int | 5 | |
| seed | int | 123 |
# merge info with training data
info = df[['kinase']].merge(info_df)
info.head()| kinase | ID_coral | uniprot | ID_HGNC | group | family | subfamily_coral | subfamily | in_ST_paper | in_Tyr_paper | in_cddm | pseudo | pspa_category_small | pspa_category_big | cddm_big | cddm_small | length | human_uniprot_sequence | kinasecom_domain | nucleus | cytosol | cytoskeleton | plasma membrane | mitochondrion | Golgi apparatus | endoplasmic reticulum | vesicle | centrosome | aggresome | main_location | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SRC | SRC | P12931 | SRC | TK | Src | None | Src | 0 | 1 | 1 | 0 | SRC | SRC | 1.0 | 2.0 | 536 | MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL | LRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAF | NaN | 2.0 | NaN | 6.0 | NaN | 2.0 | NaN | NaN | NaN | NaN | plasma membrane |
| 1 | EPHA3 | EphA3 | P29320 | EPHA3 | TK | Eph | None | Eph | 0 | 1 | 1 | 0 | Ephrin receptors | Ephrin receptors | 1.0 | 2.0 | 983 | MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTY... | ISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSI | NaN | 1.0 | NaN | 6.0 | NaN | 3.0 | NaN | NaN | NaN | NaN | plasma membrane |
| 2 | FES | FES | P07332 | FES | TK | Fer | None | Fer | 0 | 1 | 1 | 0 | TAM receptors | TAM receptors | 1.0 | 2.0 | 822 | MGFSSELCSPQGHGVLQQMQEAELRLLEGMRKWMAQRVKSDREYAGLLHHMSLQDSGGQSRAISPDSPISQSWAEITSQTEGLSRLLRQHAEDLNSGPLSKLSLLIRERQQLRKTYSEQWQQLQQELTKTHSQDIEKLKSQYRALARDSAQAKRKYQEASKDKDRDKAKDKYVRSLWKLFAHHNRYVLGVRAAQLHHQHHHQLLLPGLLRSLQDLHEEMACILKEILQEYLEISSLVQDEVVAIHREMAAAAARIQPEAEYQGFLRQYGSAPDVPPCVTFDESLLEEGEPLEPGELQLNELTVESVQHTLTSVTDELAVATEMVFRRQEMVTQLQQELRNEEENTHPRERVQLLGKRQVLQEALQGLQVALCSQAKLQAQQELLQTKLEHLGPGEPPPVLLLQDDRHSTSSSEQEREGGRTPTLEILKSHISGIFRPKFSLPPPLQLIPEVQKPLHEQLWYHGAIPRAEVAELLVHSGDFLVRESQGKQEYVLSVLWDGLPRHFIIQSLDNLYRLEGEGFPSIPLLIDHLLSTQQPLTKKSGVVLHRAVPKDKWVLNHEDLVLGEQIGRGNFGEVFSGRLRADNTLVAVKSCRETL... | LVLGEQIGRGNFGEVFSGRLRADNTLVAVKSCRETLPPDLKAKFLQEARILKQYSHPNIVRLIGVCTQKQPIYIVMELVQGGDFLTFLRTEGARLRVKTLLQMVGDAAAGMEYLESKCCIHRDLAARNCLVTEKNVLKISDFGMSREEADGVYAASGGLRQVPVKWTAPEALNYGRYSSESDVWSFGILLWETFSLGASPYPNLSNQQTREFVEKGGRLPCPELCPDAVFRLMEQCWAYEPGQRPSFSTIYQELQS | NaN | 6.0 | NaN | 4.0 | NaN | NaN | NaN | NaN | NaN | NaN | cytosol |
| 3 | NTRK3 | TRKC | Q16288 | NTRK3 | TK | Trk | None | Trk | 0 | 1 | 1 | 0 | Insulin and neurotrophin receptors | Insulin and neurotrophin receptors | 1.0 | 3.0 | 839 | MDVSLCPAKCSFWRIFLLGSVWLDYVGSVLACPANCVCSKTEINCRRPDDGNLFPLLEGQDSGNSNGNASINITDISRNITSIHIENWRSLHTLNAVDMELYTGLQKLTIKNSGLRSIQPRAFAKNPHLRYINLSSNRLTTLSWQLFQTLSLRELQLEQNFFNCSCDIRWMQLWQEQGEAKLNSQNLYCINADGSQLPLFRMNISQCDLPEISVSHVNLTVREGDNAVITCNGSGSPLPDVDWIVTGLQSINTHQTNLNWTNVHAINLTLVNVTSEDNGFTLTCIAENVVGMSNASVALTVYYPPRVVSLEEPELRLEHCIEFVVRGNPPPTLHWLHNGQPLRESKIIHVEYYQEGEISEGCLLFNKPTHYNNGNYTLIAKNPLGTANQTINGHFLKEPFPESTDNFILFDEVSPTPPITVTHKPEEDTFGVSIAVGLAAFACVLLVVLFVMINKYGRRSKFGMKGPVAVISGEEDSASPLHHINHGITTPSSLDAGPDTVVIGMTRIPVIENPQYFRQGHNCHKPDTYVQHIKRRDIVLKRELGEGAFGKVFLAECYNLSPTKDKMLVAVKALKDPTLAARKDFQREAELLTNLQ... | IVLKRELGEGAFGKVFLAECYNLSPTKDKMLVAVKALKDPTLAARKDFQREAELLTNLQHEHIVKFYGVCGDGDPLIMVFEYMKHGDLNKFLRAHGPDAMILVDGQPRQAKGELGLSQMLHIASQIASGMVYLASQHFVHRDLATRNCLVGANLLVKIGDFGMSRDVYSTDYYRVGGHTMLPIRWMPPESIMYRKFTTESDVWSFGVILWEIFTYGKQPWFQLSNTEVIECITQGRVLERPRVCPKEVYDVMLGCWQREPQQRLNIKEIYKI | NaN | 4.0 | NaN | 4.0 | NaN | 2.0 | NaN | NaN | NaN | NaN | cytosol |
| 4 | ALK | ALK | Q9UM73 | ALK | TK | ALK | None | ALK | 0 | 1 | 1 | 0 | PDGF receptors | PDGF receptors | 1.0 | 3.0 | 1620 | MGAIGLLWLLPLLLSTAAVGSGMGTGQRAGSPAAGPPLQPREPLSYSRLQRKSLAVDFVVPSLFRVYARDLLLPPSSSELKAGRPEARGSLALDCAPLLRLLGPAPGVSWTAGSPAPAEARTLSRVLKGGSVRKLRRAKQLVLELGEEAILEGCVGPPGEAAVGLLQFNLSELFSWWIRQGEGRLRIRLMPEKKASEVGREGRLSAAIRASQPRLLFQIFGTGHSSLESPTNMPSPSPDYFTWNLTWIMKDSFPFLSHRSRYGLECSFDFPCELEYSPPLHDLRNQSWSWRRIPSEEASQMDLLDGPGAERSKEMPRGSFLLLNTSADSKHTILSPWMRSSSEHCTLAVSVHRHLQPSGRYIAQLLPHNEAAREILLMPTPGKHGWTVLQGRIGRPDNPFRVALEYISSGNRSLSAVDFFALKNCSEGTSPGSKMALQSSFTCWNGTVLQLGQACDFHQDCAQGEDESQMCRKLPVGFYCNFEDGFCGWTQGTLSPHTPQWQVRTLKDARFQDHQDHALLLSTTDVPASESATVTSATFPAPIKSSPCELRMSWLIRGVLRGNVSLVLVENKTGKEQGRMVWHVAAYEGLSLWQWM... | ITLIRGLGHGAFGEVYEGQVSGMPNDPSPLQVAVKTLPEVCSEQDELDFLMEALIISKFNHQNIVRCIGVSLQSLPRFILLELMAGGDLKSFLRETRPRPSQPSSLAMLDLLHVARDIACGCQYLEENHFIHRDIAARNCLLTCPGPGRVAKIGDFGMARDIYRASYYRKGGCAMLPVKWMPPEAFMEGIFTSKTDTWSFGVLLWEIFSLGYMPYPSKSNQEVLEFVTSGGRMDPPKNCPGPVYRIMTQCWQHQPEDRPNFAIILERIEY | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | None |
# stratify samples based on group
splits = get_splits(info,stratified='group')
len(splits)
split0 = splits[0]StratifiedKFold(n_splits=5, random_state=123, shuffle=True)
# kinase group in train set: 9
# kinase group in test set: 9
---------------------------
# kinase in train set: 312
---------------------------
# kinase in test set: 78
---------------------------
test set: ['EPHA3' 'FES' 'FLT3' 'FYN' 'EPHB1' 'EPHB3' 'FER' 'EPHB4' 'FLT4' 'FGFR1' 'EPHA5' 'TEK' 'DDR2' 'ZAP70' 'LIMK1' 'ULK3' 'JAK1' 'WEE1' 'TESK1' 'MAP2K3' 'AMPKA2' 'ATM' 'CAMK1D' 'CAMK2D' 'CAMK4' 'CAMKK1'
'CK1D' 'CK1E' 'DYRK2' 'DYRK4' 'HGK' 'IKKE' 'JNK2' 'JNK3' 'KHS1' 'MAPKAPK5' 'MEK2' 'MSK2' 'NDR1' 'NEK6' 'NEK9' 'NIM1' 'NLK' 'OSR1' 'P38A' 'P38B' 'P90RSK' 'PAK1' 'PERK' 'PKCH' 'PKCI' 'PKN1' 'ROCK2'
'RSK2' 'SIK' 'STLK3' 'TAK1' 'TSSK1' 'ALPHAK3' 'BMPR2' 'CDK10' 'CDK13' 'CDK14' 'CDKL5' 'GCN2' 'GRK4' 'IRE1' 'KHS2' 'MASTL' 'MLK4' 'MNK1' 'MRCKA' 'PRPK' 'QSK' 'SMMLCK' 'SSTK' 'ULK2' 'VRK1']feat_col = df.columns[df.columns.str.startswith('T5_')]
target_col = df.columns[~df.columns.isin(feat_col)][1:]split_data
split_data (df:pandas.core.frame.DataFrame, feat_col:list, target_col:list, split:tuple)
Given split tuple, split dataframe into X_train, y_train, X_test, y_test
| Type | Details | |
|---|---|---|
| df | DataFrame | dataframe of values |
| feat_col | list | feature columns |
| target_col | list | target columns |
| split | tuple | one of the split in splits |
X_train, y_train, X_test, y_test = split_data(df,feat_col, target_col, split0)X_train.shape,y_train.shape,X_test.shape,y_test.shape((312, 1024), (312, 210), (78, 1024), (78, 210))Scoring
score_each
score_each (target:pandas.core.frame.DataFrame, pred:pandas.core.frame.DataFrame, absolute=True, verbose=True)
Calculate the overall MSE and average Pearson (per row) between two dataframes.
| Type | Default | Details | |
|---|---|---|---|
| target | DataFrame | target dataframe | |
| pred | DataFrame | predicted dataframe | |
| absolute | bool | True | if absolute, take average with absolute values for pearson/spearman |
| verbose | bool | True | whether or not display the error value |
mse,pearson_avg,pearson_all = score_each(y_test, y_test)overall MSE: 0.0000
Average Pearson: 1.0000 pearson_all.head()| Pearson | |
|---|---|
| 3 | 1.0 |
| 8 | 1.0 |
| 10 | 1.0 |
| 19 | 1.0 |
| 24 | 1.0 |
Machine Learning
for regression task
Trainer
train_ml
train_ml (df, feat_col, target_col, split, model, save=None, params={})
Fit and predict using sklearn model format, return target and pred of valid dataset.
| Type | Default | Details | |
|---|---|---|---|
| df | dataframe of values | ||
| feat_col | feature columns | ||
| target_col | target columns | ||
| split | one split in splits | ||
| model | a sklearn models | ||
| save | NoneType | None | file (.joblib) to save, e.g. ‘model.joblib’ |
| params | dict | {} | parameters for model.fit from sklearn |
model = LinearRegression()
## Uncheck to run with saving model
# target,pred = train_ml(df, feat_col, target_col, split0, model,'model.joblib')
# Run without saving model
target,pred = train_ml(df, feat_col, target_col, split0, model)
pred.head()LinearRegression()| -5P | -5G | -5A | -5C | -5S | -5T | -5V | -5I | -5L | -5M | -5F | -5Y | -5W | -5H | -5K | -5R | -5Q | -5N | -5D | -5E | -5s | -5t | -5y | -4P | -4G | -4A | -4C | -4S | -4T | -4V | -4I | -4L | -4M | -4F | -4Y | -4W | -4H | -4K | -4R | -4Q | -4N | -4D | -4E | -4s | -4t | -4y | -3P | -3G | -3A | -3C | -3S | -3T | -3V | -3I | -3L | -3M | -3F | -3Y | -3W | -3H | -3K | -3R | -3Q | -3N | -3D | -3E | -3s | -3t | -3y | -2P | -2G | -2A | -2C | -2S | -2T | -2V | -2I | -2L | -2M | -2F | -2Y | -2W | -2H | -2K | -2R | -2Q | -2N | -2D | -2E | -2s | -2t | -2y | -1P | -1G | -1A | -1C | -1S | -1T | -1V | -1I | -1L | -1M | -1F | -1Y | -1W | -1H | -1K | -1R | -1Q | -1N | -1D | -1E | -1s | -1t | -1y | 1P | 1G | 1A | 1C | 1S | 1T | 1V | 1I | 1L | 1M | 1F | 1Y | 1W | 1H | 1K | 1R | 1Q | 1N | 1D | 1E | 1s | 1t | 1y | 2P | 2G | 2A | 2C | 2S | 2T | 2V | 2I | 2L | 2M | 2F | 2Y | 2W | 2H | 2K | 2R | 2Q | 2N | 2D | 2E | 2s | 2t | 2y | 3P | 3G | 3A | 3C | 3S | 3T | 3V | 3I | 3L | 3M | 3F | 3Y | 3W | 3H | 3K | 3R | 3Q | 3N | 3D | 3E | 3s | 3t | 3y | 4P | 4G | 4A | 4C | 4S | 4T | 4V | 4I | 4L | 4M | 4F | 4Y | 4W | 4H | 4K | 4R | 4Q | 4N | 4D | 4E | 4s | 4t | 4y | 0s | 0t | 0y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | -0.364663 | 0.870487 | 0.991834 | -1.121964 | -0.290657 | -0.833387 | -0.307426 | -0.324773 | 0.471477 | -1.073343 | -0.812458 | -1.434746 | -1.350085 | -1.271085 | 1.800254 | 0.974699 | 0.116555 | 0.744288 | 1.985825 | 2.331658 | -0.413107 | -0.471487 | -0.217772 | -0.247955 | 1.292483 | 0.244955 | -0.891183 | -0.536666 | -0.804704 | 0.428222 | -0.086229 | 1.264924 | -1.158653 | -0.408948 | -1.493813 | -1.168260 | -0.991477 | 0.758629 | 1.455745 | -0.190496 | -0.031264 | 1.957642 | 1.848958 | 0.213255 | -0.796405 | -0.658625 | -0.061372 | 0.335267 | 1.229802 | -1.199341 | -0.880919 | -1.180639 | 0.474184 | -0.231715 | 1.040002 | -1.320606 | -0.500693 | -1.444464 | -1.220817 | -0.538946 | 1.032913 | -0.167847 | 0.402653 | 0.142351 | 1.606669 | 2.048291 | 0.609787 | -0.409185 | 0.234544 | -0.374190 | 0.594941 | 0.711258 | -0.947312 | -0.875054 | -1.197036 | -0.373912 | -0.127618 | 0.149866 | -1.376333 | -0.821700 | -1.700294 | -1.061821 | -1.076945 | 0.975284 | 1.280931 | -0.216788 | 0.233037 | 2.334163 | 2.257660 | 0.741716 | -0.343071 | 1.212989 | -0.722006 | 0.433270 | 0.901180 | -0.993873 | -0.923401 | -1.240499 | 0.247664 | 0.237635 | 1.307997 | -1.448116 | -0.210288 | -1.602547 | -1.224160 | -0.771831 | 0.307597 | -0.361454 | 0.193356 | 0.875600 | 2.765226 | 1.398041 | 0.030973 | -0.140705 | 0.940240 | -2.415863 | 1.794153 | 1.064604 | -0.665626 | -1.398913 | -1.206202 | 2.164858 | 1.292418 | 0.896186 | -1.059124 | -0.072368 | -1.666251 | -1.448471 | -0.671739 | 0.142903 | -0.818297 | 0.341954 | -0.693884 | 1.257289 | 2.420538 | 0.679763 | -0.467378 | 0.529583 | -0.201324 | -0.046453 | 1.260534 | -0.405472 | -1.213894 | -1.201127 | 0.671153 | -0.063335 | 1.122904 | -0.608907 | 0.330743 | -1.727762 | -1.080228 | -0.310596 | 0.212781 | 0.484149 | 0.237102 | 0.537243 | 1.294348 | 1.613651 | 0.065598 | -0.407538 | -0.563436 | -0.303898 | 0.010687 | 0.690049 | -0.584386 | -1.130735 | -1.086324 | 1.374352 | 1.106567 | 3.509903 | -0.697721 | -0.028114 | -1.350998 | -1.099955 | -0.896023 | 0.106905 | 0.872772 | -0.538474 | -0.628891 | 0.592968 | 0.662092 | -0.040950 | -0.359262 | -0.180488 | 0.471792 | 0.807257 | 0.916204 | -1.005127 | -0.735023 | -0.841264 | 0.429592 | -0.462519 | 0.806542 | -0.801919 | -0.263507 | -1.244926 | -0.900742 | -0.736697 | 1.254617 | 0.862551 | -0.011679 | 0.108503 | 0.909324 | 1.577469 | -0.199611 | -0.255610 | -0.685371 | -0.775651 | -0.584817 | 1.360464 |
| 8 | 0.095072 | 0.943352 | 0.534015 | -1.163788 | -0.481405 | -0.721120 | 0.195005 | -0.108232 | 0.560706 | -1.158015 | -0.261681 | -1.330268 | -1.198561 | -0.972852 | 2.034068 | 0.764906 | 0.708835 | 0.480972 | 1.458156 | 1.805948 | -0.680306 | -0.982428 | -0.521676 | -0.020391 | 1.502458 | 0.386169 | -0.937561 | -0.552959 | -0.670121 | 0.616497 | -0.048519 | 1.117032 | -0.955750 | -0.441107 | -1.386421 | -0.926435 | -0.689844 | 1.071099 | 0.465880 | -0.054830 | -0.092276 | 1.455418 | 1.364233 | 0.227066 | -0.579443 | -0.849440 | -0.081970 | 0.733341 | 1.242354 | -0.987211 | -0.754404 | -0.807296 | 0.640984 | 0.088058 | 1.170449 | -0.965116 | -0.627758 | -1.389126 | -1.427940 | -0.674749 | 0.566315 | 0.060729 | 0.067466 | 0.200640 | 1.412241 | 1.654691 | 0.429896 | -0.241506 | -0.310555 | 0.579809 | 0.684449 | 0.772949 | -1.067861 | -0.861802 | -1.025392 | 0.332118 | 0.291076 | 0.351917 | -1.216779 | -0.979973 | -1.922491 | -1.348519 | -1.028992 | 1.264342 | 0.703046 | -0.053192 | 0.238880 | 1.530311 | 2.147685 | 0.480055 | -0.207175 | 0.334262 | -1.038308 | -0.192368 | 0.645983 | -1.011880 | -1.106730 | -1.010666 | 0.659819 | 0.684883 | 2.409362 | -1.372949 | -0.194003 | -1.702078 | -1.257299 | -0.950375 | 0.281790 | -0.061181 | -0.516340 | 0.883897 | 2.911547 | 1.839036 | -0.180963 | 0.028267 | 0.249979 | -0.422785 | 1.517371 | 1.048142 | -0.698999 | -1.233630 | -1.222296 | 1.271998 | 0.667650 | 0.366654 | -1.237536 | -0.441708 | -1.599890 | -1.126246 | -0.805127 | 0.035418 | -0.749247 | 0.394294 | -0.745907 | 1.498242 | 3.478742 | 0.456024 | -0.345219 | -0.105203 | -0.084199 | -0.025061 | 0.499552 | -0.922343 | -1.031462 | -1.100048 | 1.003769 | 0.131931 | 0.733304 | -0.846473 | 0.004175 | -1.608343 | -1.262702 | -0.587479 | 0.846297 | -0.525767 | -0.376103 | -0.449082 | 2.384981 | 2.438125 | 0.198051 | -0.255557 | 0.835180 | -0.018520 | 0.523884 | 0.851703 | -0.650948 | -0.949875 | -0.899370 | 0.841845 | 0.456867 | 2.260202 | -0.808053 | -0.018017 | -1.237804 | -0.988369 | -0.562143 | -0.138822 | 0.506776 | -0.094963 | -0.265247 | 1.148406 | 0.759710 | -0.330317 | -0.420630 | 0.034115 | 0.114871 | 0.928895 | 0.903226 | -0.970254 | -0.439692 | -0.783941 | 0.780267 | -0.195480 | 0.926289 | -0.812014 | -0.128280 | -1.169767 | -0.872822 | -0.868972 | 0.933066 | 0.325221 | -0.203409 | -0.000786 | 1.353711 | 1.829013 | -0.594087 | -0.717604 | -0.338252 | -0.582574 | -0.741412 | 1.323958 |
| 10 | 0.563839 | 1.253888 | 1.572644 | -1.588620 | -0.697072 | -0.664016 | -0.333674 | -1.140501 | -0.300929 | -1.331911 | -1.583480 | -1.799933 | -1.198352 | 0.126503 | 2.050003 | 0.708615 | 0.193843 | 0.541931 | 2.315354 | 2.606576 | -0.373979 | -0.612467 | -0.308787 | 1.072120 | 1.933547 | 0.675078 | -0.953403 | -0.506404 | -0.833606 | 0.014510 | -0.595161 | 0.768042 | -0.874725 | -0.677491 | -1.582819 | 0.236086 | -0.751672 | 0.786535 | 0.314760 | -0.207827 | -0.064260 | 2.431253 | 1.897663 | -0.412307 | -1.699080 | -0.971404 | 1.643257 | 1.129853 | 1.287495 | -1.394400 | -0.554565 | -0.749361 | 0.208812 | -0.510521 | 1.248886 | -0.708512 | 0.044477 | -1.257033 | -1.300735 | -1.083353 | 0.051773 | 0.698143 | 0.280090 | 0.156637 | 1.561516 | 2.185947 | -0.499632 | -1.337549 | -1.100874 | 1.096746 | 2.031115 | 1.024020 | -1.309198 | -0.552031 | -0.950508 | -0.194722 | -0.133484 | 0.804600 | -0.752779 | -1.092868 | -1.784572 | -1.628407 | -0.320507 | 0.668965 | 0.223615 | 0.171455 | 0.411990 | 1.749719 | 2.801573 | -0.002060 | -1.255408 | -1.006296 | 0.650334 | 1.051969 | 1.291957 | -1.152625 | -0.810718 | -0.993471 | -0.093003 | -0.321634 | 1.024997 | -0.824425 | -0.191669 | -1.430274 | -1.442311 | -0.175871 | -0.193080 | -0.423969 | 0.585234 | 1.027001 | 1.929195 | 2.275343 | -1.159789 | -1.326967 | 0.704207 | -0.083205 | 0.854318 | 1.289939 | -0.853477 | -0.820926 | -0.842403 | 2.699846 | 1.306516 | 0.595033 | -0.850548 | 0.032011 | -1.223289 | -1.341007 | -0.424514 | -0.135504 | -1.275982 | 0.901767 | 0.068808 | 0.878630 | 1.679459 | -1.356364 | -2.118935 | 1.019273 | 0.438654 | 0.950586 | 1.400403 | -0.556207 | -0.963658 | -1.033390 | 0.312432 | 0.051808 | 0.726317 | -0.624152 | 0.215556 | -1.329723 | -0.601423 | -0.893652 | -0.260632 | -1.115364 | 0.681550 | 0.779566 | 3.042307 | 2.135977 | -1.304345 | -1.777677 | -0.275492 | 1.312854 | 1.131627 | 0.727194 | -0.734407 | -0.961960 | -1.039300 | 1.119756 | 1.034252 | 3.469051 | -0.263320 | 0.228283 | -1.789867 | -1.532882 | -1.562832 | -1.045330 | -0.542054 | -0.354468 | 0.223724 | 0.778806 | 1.096978 | -0.342373 | -0.798702 | -0.155351 | 1.346906 | 0.894603 | 1.049744 | -1.305460 | -0.674459 | -0.970559 | 0.130576 | -0.306629 | 1.078808 | -0.767426 | -0.022721 | -1.339209 | -1.357529 | -0.905373 | 0.891923 | 1.195551 | -0.349163 | 0.053950 | 1.101158 | 1.518578 | -0.623063 | -0.590557 | -0.049050 | -0.822975 | -0.972125 | 1.795120 |
| 19 | -0.345382 | 0.793358 | 1.339868 | -1.013834 | -0.150577 | -0.959647 | 0.355832 | 0.541212 | 1.640415 | -0.968193 | -0.745335 | -1.560235 | -1.612303 | -0.562593 | 1.528853 | 0.152379 | 0.117539 | -0.150632 | 2.011172 | 1.979279 | -0.541217 | -1.077432 | -0.772270 | -0.117384 | 1.887679 | 0.863820 | -0.919661 | -0.601416 | -1.020145 | 0.254879 | -0.131948 | 1.382426 | -0.552347 | -0.078879 | -1.364123 | -1.020211 | -1.212620 | 0.800576 | 1.010589 | -0.530255 | -0.166166 | 1.640291 | 1.809219 | 0.019190 | -0.891971 | -1.061263 | -0.185716 | 1.275046 | 1.841397 | -1.456876 | -0.913087 | -1.140050 | 0.559102 | -0.154603 | 1.591858 | -1.045307 | -0.098351 | -1.351341 | -0.968525 | -1.044010 | 1.314789 | 0.093477 | 0.163769 | -0.215263 | 0.901547 | 2.001557 | 0.411314 | -0.925916 | -0.654984 | 0.179642 | 1.121340 | 0.831901 | -1.426234 | -0.869804 | -1.135396 | 0.344422 | 0.164559 | 0.146026 | -1.291108 | -0.544701 | -1.222514 | -0.747993 | -1.042427 | 1.757940 | 0.195541 | -0.157202 | -0.090089 | 1.723758 | 2.310650 | 0.441835 | -0.805616 | 0.115001 | -0.369292 | 0.002692 | 1.152909 | -0.793860 | -0.962932 | -0.882195 | 1.532767 | 1.159689 | 2.773156 | -0.995852 | 0.417409 | -1.197087 | -0.784751 | -1.019102 | 0.785648 | -1.036119 | 0.028618 | -0.038557 | 1.170680 | 0.420497 | -0.599014 | -0.156550 | -0.608964 | -1.651130 | 1.384090 | 1.177131 | -1.034129 | -0.847538 | -1.013251 | 1.911686 | 0.737655 | 0.345259 | -1.111957 | 0.292000 | -2.019084 | -1.670378 | -0.601892 | 0.583477 | -0.544753 | 0.610765 | 0.281809 | 1.495013 | 3.113074 | -0.204641 | -1.193882 | -0.039051 | -0.302140 | 0.592020 | 1.178154 | -1.461003 | -1.177476 | -1.262473 | 1.103809 | 0.439654 | 1.534357 | -0.941723 | 0.443396 | -1.412209 | -0.858716 | -1.099370 | -0.343173 | 0.118309 | -0.132197 | 0.061354 | 1.652429 | 2.182911 | -0.581494 | -1.165498 | 1.431351 | 0.049574 | 0.418317 | 0.651266 | -1.012795 | -1.228814 | -1.290874 | 1.634865 | 1.322878 | 3.739813 | -0.531335 | 0.441546 | -1.730245 | -1.325678 | -1.216708 | -0.131361 | 0.613018 | -1.021587 | -0.950824 | 0.839786 | 1.268780 | -0.181957 | -0.366305 | 0.008801 | 0.311004 | 1.188993 | 1.496121 | -1.064321 | -0.815558 | -1.064153 | 1.158995 | -0.260283 | 1.226198 | -0.641580 | -0.407418 | -1.625862 | -1.263432 | -0.796712 | 0.830081 | 0.066254 | -0.052416 | 0.005698 | 1.219596 | 1.779481 | -0.375770 | -0.839007 | -0.076203 | -0.847138 | -0.717033 | 1.564161 |
| 24 | 0.176033 | 0.741688 | 0.991692 | -1.113790 | -0.412446 | -0.791945 | 0.128496 | -0.202053 | -0.113824 | -1.339233 | -0.624250 | -1.806232 | -1.555979 | -1.377404 | 1.704786 | 0.343696 | 0.014072 | -0.062933 | 2.440986 | 2.226345 | 0.418376 | 0.113736 | 0.100893 | -0.527166 | 0.933825 | 0.392002 | -0.941578 | -0.704239 | -0.887847 | 0.382819 | -0.311493 | 0.793274 | -1.238408 | -0.678087 | -1.692941 | -1.849401 | -0.866391 | 0.882327 | 1.105737 | -0.170421 | -0.146371 | 2.465253 | 2.227617 | 0.838525 | -0.005350 | -0.000928 | 0.318767 | 0.262909 | 0.795245 | -1.192453 | -0.700834 | -0.840046 | 0.151631 | -0.585464 | 0.623365 | -1.378155 | -0.448412 | -1.331188 | -1.555552 | -0.634506 | 0.984850 | -1.451043 | 0.482964 | 0.390823 | 2.449124 | 3.598372 | 0.403804 | -0.588716 | 0.244046 | 0.116811 | 0.766701 | 0.311579 | -1.280278 | -0.925376 | -1.180196 | -0.293008 | -0.632813 | -0.248395 | -1.599542 | -0.992108 | -1.182022 | -1.608863 | -0.716496 | 0.951618 | -0.071149 | -1.043482 | 0.817226 | 3.070252 | 3.075492 | 1.161942 | 0.404138 | 1.096686 | -0.776611 | 0.127805 | 0.296811 | -0.936477 | -1.385628 | -1.284595 | 0.885734 | 1.251156 | 1.902369 | -1.655065 | -0.687011 | -2.020199 | -1.894475 | -0.874229 | -0.025291 | -0.306773 | -0.372157 | 0.024541 | 2.315833 | 1.801416 | 1.029931 | 1.136409 | 1.445927 | 0.370569 | 1.385273 | 0.683506 | -0.488000 | -0.706634 | -1.132210 | 0.511413 | -0.283276 | 0.880093 | -0.649611 | 0.151899 | -1.542229 | -1.605936 | -0.906198 | -0.053110 | -0.934906 | 0.001504 | -0.473969 | 1.757605 | 2.094833 | 0.971821 | -0.007963 | -0.023728 | 0.814541 | -0.129355 | 0.693643 | -0.897521 | -0.978683 | -1.263389 | -0.181939 | -0.775093 | 1.018393 | -1.055206 | -0.680719 | -1.965743 | -1.194372 | -0.714936 | 1.176819 | 1.565398 | -0.478578 | 0.213794 | 1.014696 | 1.715941 | 1.303775 | 0.545193 | 0.254092 | 1.093222 | 0.471941 | 0.928536 | -0.844484 | -1.049744 | -1.417446 | 0.739309 | -0.474725 | 2.019905 | -1.585276 | -0.757766 | -1.392234 | -1.344177 | -0.918719 | 2.266661 | 1.790530 | -0.512430 | -0.642730 | -0.236481 | 0.982732 | 0.213872 | -0.225743 | 0.895681 | 0.005032 | 0.478152 | 0.755620 | -1.095983 | -0.385233 | -0.739999 | 0.857009 | -0.331903 | 0.977391 | -0.746958 | -0.228667 | -1.194845 | -1.006765 | -0.892299 | 1.827048 | 0.989671 | 0.028709 | -0.141061 | 0.738915 | 1.347458 | -0.427733 | -0.759636 | -0.054728 | -0.802565 | -0.130813 | 0.933351 |
Cross-Validation
train_ml_cv
train_ml_cv (df, feat_col, target_col, splits, model, save=None, params={})
Cross-validation through the given splits
| Type | Default | Details | |
|---|---|---|---|
| df | dataframe of values | ||
| feat_col | feature columns | ||
| target_col | target columns | ||
| splits | splits | ||
| model | sklearn model | ||
| save | NoneType | None | model name to be saved, e.g., ‘LR’ |
| params | dict | {} | act as kwargs, for model.fit |
oof,metrics = train_ml_cv(df,feat_col,target_col,splits,model)------ fold: 0 --------
LinearRegression()
overall MSE: 0.8763
Average Pearson: 0.7168
------ fold: 1 --------
LinearRegression()
overall MSE: 0.6406
Average Pearson: 0.7313
------ fold: 2 --------
LinearRegression()
overall MSE: 0.7465
Average Pearson: 0.7429
------ fold: 3 --------
LinearRegression()
overall MSE: 0.6453
Average Pearson: 0.7328
------ fold: 4 --------
LinearRegression()
overall MSE: 0.7906
Average Pearson: 0.7263 metrics| fold | mse | pearson_avg | |
|---|---|---|---|
| 0 | 0 | 0.876279 | 0.716824 |
| 1 | 1 | 0.640555 | 0.731331 |
| 2 | 2 | 0.746466 | 0.742891 |
| 3 | 3 | 0.645253 | 0.732798 |
| 4 | 4 | 0.790580 | 0.726299 |
# plot spearman and pearson scores
metrics.iloc[:,1:].plot.box();
# Overall score for oof
_,_,corr_df= score_each(oof,df[target_col])overall MSE: 0.7398
Average Pearson: 0.7300 corr_df.sort_values('Pearson').head() # lowest Pearson| Pearson | |
|---|---|
| 81 | -0.244100 |
| 156 | -0.238898 |
| 313 | -0.197908 |
| 381 | -0.151470 |
| 205 | -0.081771 |
Predictor
predict_ml
predict_ml (df, feat_col, target_col=None, model_pth='model.joblib')
Make predictions based on trained model.
| Type | Default | Details | |
|---|---|---|---|
| df | Dataframe that contains features | ||
| feat_col | feature columns | ||
| target_col | NoneType | None | |
| model_pth | str | model.joblib |
Uncheck below to run if you have model_pth:
# pred2 = predict_ml(X_test,feat_col, target_col, model_pth = 'model.joblib')
# pred2.head()
## or
# predict_ml(df.iloc[split_0[1]],feat_col,'model.joblib')/usr/local/lib/python3.9/dist-packages/sklearn/base.py:376: InconsistentVersionWarning: Trying to unpickle estimator LinearRegression from version 1.4.1.post1 when using version 1.5.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations
warnings.warn(| -5P | -5G | -5A | -5C | -5S | -5T | -5V | -5I | -5L | -5M | -5F | -5Y | -5W | -5H | -5K | -5R | -5Q | -5N | -5D | -5E | -5s | -5t | -5y | -4P | -4G | -4A | -4C | -4S | -4T | -4V | -4I | -4L | -4M | -4F | -4Y | -4W | -4H | -4K | -4R | -4Q | -4N | -4D | -4E | -4s | -4t | -4y | -3P | -3G | -3A | -3C | -3S | -3T | -3V | -3I | -3L | -3M | -3F | -3Y | -3W | -3H | -3K | -3R | -3Q | -3N | -3D | -3E | -3s | -3t | -3y | -2P | -2G | -2A | -2C | -2S | -2T | -2V | -2I | -2L | -2M | -2F | -2Y | -2W | -2H | -2K | -2R | -2Q | -2N | -2D | -2E | -2s | -2t | -2y | -1P | -1G | -1A | -1C | -1S | -1T | -1V | -1I | -1L | -1M | -1F | -1Y | -1W | -1H | -1K | -1R | -1Q | -1N | -1D | -1E | -1s | -1t | -1y | 1P | 1G | 1A | 1C | 1S | 1T | 1V | 1I | 1L | 1M | 1F | 1Y | 1W | 1H | 1K | 1R | 1Q | 1N | 1D | 1E | 1s | 1t | 1y | 2P | 2G | 2A | 2C | 2S | 2T | 2V | 2I | 2L | 2M | 2F | 2Y | 2W | 2H | 2K | 2R | 2Q | 2N | 2D | 2E | 2s | 2t | 2y | 3P | 3G | 3A | 3C | 3S | 3T | 3V | 3I | 3L | 3M | 3F | 3Y | 3W | 3H | 3K | 3R | 3Q | 3N | 3D | 3E | 3s | 3t | 3y | 4P | 4G | 4A | 4C | 4S | 4T | 4V | 4I | 4L | 4M | 4F | 4Y | 4W | 4H | 4K | 4R | 4Q | 4N | 4D | 4E | 4s | 4t | 4y | 0s | 0t | 0y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | -0.364707 | 0.870501 | 0.991939 | -1.122006 | -0.290667 | -0.833366 | -0.307455 | -0.324753 | 0.471472 | -1.073353 | -0.812464 | -1.434765 | -1.350083 | -1.271137 | 1.800298 | 0.974743 | 0.116519 | 0.744288 | 1.985861 | 2.331726 | -0.413103 | -0.471491 | -0.217838 | -0.247941 | 1.292480 | 0.244995 | -0.891201 | -0.536663 | -0.804740 | 0.428205 | -0.086208 | 1.264944 | -1.158690 | -0.408965 | -1.493815 | -1.168097 | -0.991530 | 0.758574 | 1.455711 | -0.190510 | -0.031299 | 1.957698 | 1.849005 | 0.213270 | -0.796449 | -0.658697 | -0.061393 | 0.335216 | 1.229836 | -1.199328 | -0.880915 | -1.180625 | 0.474172 | -0.231705 | 1.040083 | -1.320546 | -0.500661 | -1.444532 | -1.220846 | -0.539033 | 1.032884 | -0.167849 | 0.402668 | 0.142277 | 1.606714 | 2.048386 | 0.609752 | -0.409203 | 0.234526 | -0.374300 | 0.595023 | 0.711315 | -0.947360 | -0.875062 | -1.197055 | -0.373876 | -0.127561 | 0.149932 | -1.376348 | -0.821782 | -1.700367 | -1.061799 | -1.077019 | 0.975288 | 1.281217 | -0.216851 | 0.232975 | 2.334243 | 2.257738 | 0.741782 | -0.343032 | 1.212808 | -0.721960 | 0.433344 | 0.901258 | -0.993851 | -0.923428 | -1.240503 | 0.247750 | 0.237647 | 1.308000 | -1.448115 | -0.210287 | -1.602614 | -1.224206 | -0.771882 | 0.307644 | -0.361465 | 0.193351 | 0.875552 | 2.765148 | 1.398163 | 0.031033 | -0.140698 | 0.940186 | -2.415734 | 1.794049 | 1.064599 | -0.665642 | -1.398878 | -1.206198 | 2.164858 | 1.292426 | 0.896056 | -1.059209 | -0.072301 | -1.666331 | -1.448606 | -0.671760 | 0.142948 | -0.818149 | 0.341993 | -0.693898 | 1.257253 | 2.420482 | 0.679768 | -0.467398 | 0.529559 | -0.201342 | -0.046525 | 1.260589 | -0.405474 | -1.213921 | -1.201158 | 0.671173 | -0.063247 | 1.122971 | -0.608822 | 0.330831 | -1.727682 | -1.080166 | -0.310707 | 0.212629 | 0.484047 | 0.237114 | 0.537202 | 1.294311 | 1.613692 | 0.065542 | -0.407601 | -0.563342 | -0.303835 | 0.010657 | 0.690046 | -0.584377 | -1.130737 | -1.086326 | 1.374406 | 1.106638 | 3.509956 | -0.697685 | -0.028091 | -1.351033 | -1.100004 | -0.896113 | 0.106874 | 0.872651 | -0.538471 | -0.628911 | 0.592987 | 0.662206 | -0.040950 | -0.359280 | -0.180524 | 0.471878 | 0.807249 | 0.916216 | -1.005140 | -0.735050 | -0.841288 | 0.429646 | -0.462429 | 0.806671 | -0.801938 | -0.263505 | -1.245028 | -0.900780 | -0.736740 | 1.254654 | 0.862569 | -0.011711 | 0.108451 | 0.909329 | 1.577520 | -0.199625 | -0.255603 | -0.685361 | -0.775611 | -0.584843 | 1.360464 |
| 8 | 0.094880 | 0.943445 | 0.534657 | -1.164017 | -0.481484 | -0.721019 | 0.194848 | -0.108135 | 0.560754 | -1.158064 | -0.261648 | -1.330354 | -1.198559 | -0.973158 | 2.034347 | 0.765188 | 0.708631 | 0.480957 | 1.458316 | 1.806352 | -0.680310 | -0.982514 | -0.522106 | -0.020348 | 1.502482 | 0.386448 | -0.937715 | -0.552971 | -0.670327 | 0.616476 | -0.048377 | 1.117217 | -0.955937 | -0.441204 | -1.386428 | -0.925580 | -0.690133 | 1.070817 | 0.465711 | -0.054884 | -0.092478 | 1.455645 | 1.364479 | 0.227138 | -0.579721 | -0.849840 | -0.082119 | 0.733043 | 1.242609 | -0.987145 | -0.754413 | -0.807264 | 0.640982 | 0.088161 | 1.170935 | -0.964818 | -0.627630 | -1.389539 | -1.428106 | -0.675190 | 0.566198 | 0.060587 | 0.067555 | 0.200230 | 1.412501 | 1.655164 | 0.429707 | -0.241574 | -0.310633 | 0.579110 | 0.684921 | 0.773356 | -1.068141 | -0.861840 | -1.025491 | 0.332373 | 0.291397 | 0.352253 | -1.216874 | -0.980455 | -1.923013 | -1.348364 | -1.029345 | 1.264398 | 0.704389 | -0.053480 | 0.238599 | 1.530757 | 2.148098 | 0.480477 | -0.206869 | 0.333178 | -1.037978 | -0.191798 | 0.646448 | -1.011805 | -1.106884 | -1.010696 | 0.660367 | 0.684997 | 2.409452 | -1.373009 | -0.193996 | -1.702466 | -1.257548 | -0.950701 | 0.281967 | -0.061367 | -0.516400 | 0.883593 | 2.911111 | 1.839703 | -0.180538 | 0.028359 | 0.249610 | -0.422202 | 1.516919 | 1.048154 | -0.699110 | -1.233507 | -1.222338 | 1.272061 | 0.667691 | 0.365978 | -1.238067 | -0.441393 | -1.600342 | -1.126943 | -0.805258 | 0.035632 | -0.748474 | 0.394493 | -0.745987 | 1.498071 | 3.478439 | 0.456116 | -0.345265 | -0.105351 | -0.084348 | -0.025478 | 0.499899 | -0.922378 | -1.031616 | -1.100205 | 1.003919 | 0.132450 | 0.733760 | -0.845991 | 0.004665 | -1.607834 | -1.262301 | -0.588094 | 0.845450 | -0.526361 | -0.376046 | -0.449336 | 2.384711 | 2.438319 | 0.197771 | -0.255833 | 0.835590 | -0.018200 | 0.523772 | 0.851705 | -0.650950 | -0.949908 | -0.899393 | 0.842180 | 0.457261 | 2.260546 | -0.807861 | -0.017885 | -1.238006 | -0.988591 | -0.562653 | -0.139013 | 0.505947 | -0.094941 | -0.265339 | 1.148561 | 0.760380 | -0.330286 | -0.420700 | 0.033910 | 0.115320 | 0.928882 | 0.903340 | -0.970370 | -0.439861 | -0.784085 | 0.780607 | -0.194983 | 0.927073 | -0.812121 | -0.128218 | -1.170375 | -0.873047 | -0.869217 | 0.933201 | 0.325269 | -0.203590 | -0.001098 | 1.353726 | 1.829347 | -0.594127 | -0.717501 | -0.338266 | -0.582361 | -0.741572 | 1.323994 |
| 10 | 0.563761 | 1.253751 | 1.571941 | -1.588473 | -0.696936 | -0.664023 | -0.333556 | -1.140509 | -0.301241 | -1.331894 | -1.583751 | -1.799943 | -1.198313 | 0.126797 | 2.049657 | 0.708267 | 0.193999 | 0.542017 | 2.315381 | 2.606185 | -0.373873 | -0.612161 | -0.308238 | 1.072220 | 1.933363 | 0.674664 | -0.953069 | -0.506297 | -0.833425 | 0.014233 | -0.595364 | 0.767623 | -0.874673 | -0.677423 | -1.582824 | 0.235657 | -0.751471 | 0.786653 | 0.314812 | -0.207874 | -0.064091 | 2.431409 | 1.897538 | -0.412316 | -1.698755 | -0.971129 | 1.643479 | 1.130139 | 1.287069 | -1.394417 | -0.554439 | -0.749204 | 0.208566 | -0.510789 | 1.248382 | -0.708603 | 0.044584 | -1.256586 | -1.300583 | -1.083200 | 0.051696 | 0.698768 | 0.279998 | 0.156924 | 1.561303 | 2.185819 | -0.499524 | -1.337618 | -1.100894 | 1.097608 | 2.030696 | 1.023373 | -1.308945 | -0.552031 | -0.950460 | -0.195131 | -0.133730 | 0.804491 | -0.752654 | -1.092412 | -1.783758 | -1.628648 | -0.320471 | 0.668794 | 0.223579 | 0.171435 | 0.411966 | 1.749369 | 2.801345 | -0.002586 | -1.255979 | -1.005202 | 0.649796 | 1.050949 | 1.291494 | -1.152493 | -0.810573 | -0.993410 | -0.093676 | -0.321893 | 1.024643 | -0.824132 | -0.191682 | -1.429931 | -1.442146 | -0.175457 | -0.192891 | -0.423366 | 0.585401 | 1.027386 | 1.929529 | 2.274905 | -1.160454 | -1.327223 | 0.704741 | -0.083104 | 0.854169 | 1.289764 | -0.853324 | -0.820734 | -0.842118 | 2.699562 | 1.306493 | 0.595354 | -0.849920 | 0.032008 | -1.222941 | -1.340690 | -0.424373 | -0.135501 | -1.276363 | 0.901684 | 0.068890 | 0.878638 | 1.679665 | -1.356673 | -2.119139 | 1.019436 | 0.438966 | 0.950958 | 1.399998 | -0.556097 | -0.963540 | -1.033353 | 0.312167 | 0.051301 | 0.725654 | -0.624537 | 0.215192 | -1.330343 | -0.601941 | -0.893214 | -0.259996 | -1.114820 | 0.681553 | 0.779848 | 3.042752 | 2.135981 | -1.304259 | -1.777748 | -0.275344 | 1.312742 | 1.131495 | 0.727130 | -0.734200 | -0.961843 | -1.039254 | 1.119374 | 1.033973 | 3.468593 | -0.263437 | 0.228164 | -1.789694 | -1.532912 | -1.562411 | -1.045123 | -0.540854 | -0.354496 | 0.223732 | 0.778495 | 1.096361 | -0.342493 | -0.798788 | -0.155172 | 1.346672 | 0.894495 | 1.049472 | -1.305194 | -0.674273 | -0.970410 | 0.130179 | -0.306987 | 1.077974 | -0.767324 | -0.022978 | -1.338590 | -1.357309 | -0.905156 | 0.892107 | 1.195717 | -0.349027 | 0.054265 | 1.101195 | 1.518123 | -0.623193 | -0.590878 | -0.048774 | -0.823091 | -0.971931 | 1.794952 |
| 19 | -0.345552 | 0.793362 | 1.340005 | -1.013930 | -0.150572 | -0.959579 | 0.355774 | 0.541276 | 1.640309 | -0.968219 | -0.745433 | -1.560300 | -1.612285 | -0.562676 | 1.528894 | 0.152422 | 0.117465 | -0.150604 | 2.011297 | 1.979388 | -0.541172 | -1.077355 | -0.772326 | -0.117310 | 1.887614 | 0.863831 | -0.919621 | -0.601378 | -1.020209 | 0.254741 | -0.131939 | 1.382369 | -0.552455 | -0.078917 | -1.364130 | -1.019803 | -1.212733 | 0.800430 | 1.010493 | -0.530314 | -0.166232 | 1.640520 | 1.809336 | 0.019236 | -0.892022 | -1.061420 | -0.185721 | 1.274965 | 1.841385 | -1.456838 | -0.913036 | -1.139958 | 0.558990 | -0.154651 | 1.591975 | -1.045138 | -0.098213 | -1.351432 | -0.968574 | -1.044251 | 1.314673 | 0.093657 | 0.163790 | -0.215422 | 0.901634 | 2.001833 | 0.411230 | -0.925994 | -0.655048 | 0.179535 | 1.121484 | 0.831897 | -1.426318 | -0.869830 | -1.135444 | 0.344419 | 0.164675 | 0.146213 | -1.291119 | -0.544836 | -1.222516 | -0.747992 | -1.042659 | 1.757903 | 0.196469 | -0.157414 | -0.090297 | 1.723915 | 2.310838 | 0.441897 | -0.805656 | 0.114729 | -0.369300 | 0.002636 | 1.153028 | -0.793749 | -0.962975 | -0.882189 | 1.532851 | 1.159654 | 2.773061 | -0.995763 | 0.417408 | -1.197206 | -0.784852 | -1.019147 | 0.785857 | -1.035980 | 0.028651 | -0.038599 | 1.170523 | 0.420770 | -0.599012 | -0.156600 | -0.608985 | -1.650676 | 1.383706 | 1.177061 | -1.034139 | -0.847366 | -1.013153 | 1.911604 | 0.737674 | 0.344927 | -1.112050 | 0.292221 | -2.019246 | -1.670726 | -0.601921 | 0.583629 | -0.544380 | 0.610869 | 0.281789 | 1.494896 | 3.112953 | -0.204715 | -1.194006 | -0.039082 | -0.302105 | 0.591894 | 1.178216 | -1.460978 | -1.177531 | -1.262566 | 1.103796 | 0.439793 | 1.534381 | -0.941557 | 0.443578 | -1.412128 | -0.858665 | -1.099606 | -0.343484 | 0.118135 | -0.132155 | 0.061301 | 1.652438 | 2.183049 | -0.581653 | -1.165723 | 1.431706 | 0.049749 | 0.418179 | 0.651238 | -1.012705 | -1.228785 | -1.290870 | 1.634930 | 1.323030 | 3.739850 | -0.531252 | 0.441585 | -1.730310 | -1.325847 | -1.216878 | -0.131402 | 0.612972 | -1.021584 | -0.950885 | 0.839756 | 1.268975 | -0.181989 | -0.366393 | 0.008736 | 0.311216 | 1.188935 | 1.496079 | -1.064284 | -0.815594 | -1.064188 | 1.159056 | -0.260094 | 1.226376 | -0.641610 | -0.407489 | -1.626012 | -1.263492 | -0.796787 | 0.830258 | 0.066362 | -0.052483 | 0.005619 | 1.219623 | 1.779513 | -0.375856 | -0.839078 | -0.076090 | -0.847041 | -0.717059 | 1.564111 |
| 24 | 0.176036 | 0.741841 | 0.992534 | -1.113998 | -0.412594 | -0.791905 | 0.128339 | -0.202013 | -0.113531 | -1.339265 | -0.623999 | -1.806253 | -1.556014 | -1.377770 | 1.705188 | 0.344101 | 0.013864 | -0.063015 | 2.441017 | 2.226830 | 0.418280 | 0.113436 | 0.100260 | -0.527241 | 0.933997 | 0.392464 | -0.941927 | -0.704338 | -0.888078 | 0.383058 | -0.311264 | 0.793709 | -1.238518 | -0.678181 | -1.692939 | -1.848728 | -0.866667 | 0.882126 | 1.105633 | -0.170399 | -0.146590 | 2.465192 | 2.227812 | 0.838558 | -0.005733 | -0.001308 | 0.318518 | 0.262553 | 0.795710 | -1.192415 | -0.700949 | -0.840174 | 0.151848 | -0.585192 | 0.623978 | -1.377972 | -0.448464 | -1.331725 | -1.555744 | -0.634792 | 0.984878 | -1.451646 | 0.483077 | 0.390429 | 2.449402 | 3.598647 | 0.403643 | -0.588677 | 0.244037 | 0.115809 | 0.767234 | 0.312291 | -1.280598 | -0.925389 | -1.180273 | -0.292559 | -0.632486 | -0.248184 | -1.599685 | -0.992676 | -1.182922 | -1.608597 | -0.716648 | 0.951788 | -0.070659 | -1.043563 | 0.817152 | 3.070715 | 3.075835 | 1.162553 | 0.404749 | 1.095346 | -0.776021 | 0.128904 | 0.297380 | -0.936568 | -1.385809 | -1.284660 | 0.886518 | 1.251424 | 1.902713 | -1.655345 | -0.686997 | -2.020636 | -1.894705 | -0.874707 | -0.025398 | -0.307371 | -0.372325 | 0.024096 | 2.315388 | 1.802032 | 1.030665 | 1.136667 | 1.445327 | 0.370679 | 1.385250 | 0.683664 | -0.488174 | -0.706762 | -1.132477 | 0.511686 | -0.283241 | 0.879578 | -0.650349 | 0.152010 | -1.542692 | -1.606455 | -0.906367 | -0.053040 | -0.934305 | 0.001645 | -0.474069 | 1.757539 | 2.094547 | 0.972126 | -0.007798 | -0.023923 | 0.814214 | -0.129828 | 0.694122 | -0.897630 | -0.978841 | -1.263475 | -0.181653 | -0.774467 | 1.019136 | -1.054700 | -0.680229 | -1.965019 | -1.193777 | -0.715535 | 1.175966 | 1.564713 | -0.478562 | 0.213456 | 1.014210 | 1.716004 | 1.303603 | 0.545162 | 0.254100 | 1.093431 | 0.472019 | 0.928594 | -0.844668 | -1.049860 | -1.417495 | 0.739762 | -0.474344 | 2.020428 | -1.585106 | -0.757616 | -1.392456 | -1.344226 | -0.919267 | 2.266412 | 1.789183 | -0.512398 | -0.642769 | -0.236152 | 0.983508 | 0.213989 | -0.225691 | 0.895452 | 0.005392 | 0.478243 | 0.755899 | -1.096258 | -0.385456 | -0.740180 | 0.857478 | -0.331416 | 0.978397 | -0.747085 | -0.228418 | -1.195601 | -1.007037 | -0.892575 | 1.826930 | 0.989540 | 0.028526 | -0.141447 | 0.738887 | 1.347976 | -0.427631 | -0.759316 | -0.054978 | -0.802389 | -0.131039 | 0.933512 |