diff --git a/.github/bump_version.py b/.github/bump_version.py
index bb0fd6d..779a82e 100644
--- a/.github/bump_version.py
+++ b/.github/bump_version.py
@@ -19,9 +19,7 @@ def get_current_version(pyproject_path: Path) -> str:
def infer_bump(changelog_dir: Path) -> str:
fragments = [
- f
- for f in changelog_dir.iterdir()
- if f.is_file() and f.name != ".gitkeep"
+ f for f in changelog_dir.iterdir() if f.is_file() and f.name != ".gitkeep"
]
if not fragments:
print("No changelog fragments found", file=sys.stderr)
diff --git a/.github/workflows/pr_code_changes.yaml b/.github/workflows/pr_code_changes.yaml
index 6a23e32..648e1c6 100644
--- a/.github/workflows/pr_code_changes.yaml
+++ b/.github/workflows/pr_code_changes.yaml
@@ -16,7 +16,7 @@ jobs:
uses: astral-sh/setup-uv@v5
- name: Install relevant dependencies
run: |
- uv pip install black isort linecheck --system
+ uv pip install "ruff>=0.9.0" --system
- name: Check code formatting
run: make check-format
diff --git a/Makefile b/Makefile
index 9c0a7f7..ef0d7fe 100644
--- a/Makefile
+++ b/Makefile
@@ -5,14 +5,10 @@ test:
pytest tests/ --cov=microimpute --cov-report=xml --maxfail=0
check-format:
- linecheck .
- isort --check-only --profile black microimpute/
- black . -l 79 --check
+ ruff format --check .
format:
- linecheck . --fix
- isort --profile black microimpute/
- black . -l 79
+ ruff format .
documentation:
cd docs && jupyter book clean . --all
diff --git a/changelog.d/ruff-format.changed b/changelog.d/ruff-format.changed
new file mode 100644
index 0000000..aff84f9
--- /dev/null
+++ b/changelog.d/ruff-format.changed
@@ -0,0 +1 @@
+Switch from black to ruff for code formatting
diff --git a/docs/autoimpute/autoimpute.ipynb b/docs/autoimpute/autoimpute.ipynb
index 59fde5a..dbdab1a 100644
--- a/docs/autoimpute/autoimpute.ipynb
+++ b/docs/autoimpute/autoimpute.ipynb
@@ -73,9 +73,11 @@
"outputs": [],
"source": [
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging\n",
+ "\n",
"logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
"logging.getLogger(\"pytorch_tabular\").setLevel(logging.ERROR)\n",
"logging.getLogger(\"joblib\").setLevel(logging.ERROR)\n",
@@ -348,7 +350,7 @@
" receiver_data=receiver_data_without_targets,\n",
" predictors=predictors,\n",
" imputed_variables=imputed_variables,\n",
- " models=[OLS, QuantReg, QRF, Matching], # MDN model excluded for efficiency\n",
+ " models=[OLS, QuantReg, QRF, Matching], # MDN model excluded for efficiency\n",
" tune_hyperparameters=False,\n",
" k_folds=3,\n",
")\n",
@@ -397,7 +399,9 @@
"print(\"Cross-validation results for different imputation methods:\")\n",
"for model, metric_dict in results.cv_results.items():\n",
" print(f\"\\nModel: {model}\")\n",
- " print(f\"quantile loss results: {metric_dict.get('quantile_loss').get(\"mean_test\"):.4f}\")"
+ " print(\n",
+ " f\"quantile loss results: {metric_dict.get('quantile_loss').get('mean_test'):.4f}\"\n",
+ " )"
]
},
{
@@ -453,7 +457,7 @@
0.0000367764891179606,
0.00005051876144414505,
0.00018829257114425592,
- 0.00037848750942190044,
+ 0.0003784875094219005,
0.0005477958061141973,
0.0006245017815431527,
0.0006789805698947316,
@@ -461,10 +465,10 @@
0.0007295643674175897,
0.0007569494312623643,
0.0008639491258964105,
- 0.0009801026362712217,
+ 0.0009801026362712215,
0.0010518961452707697,
0.0010297648072906266,
- 0.0009590446159744011,
+ 0.0009590446159744012,
0.0008660289152915666,
0.0007846310723713395,
0.00065956869485099,
@@ -508,10 +512,10 @@
],
"xaxis": "x",
"y": [
- 0.0037857585632115076,
+ 0.003785758563211507,
0.0064035381731015984,
0.008682116948116709,
- 0.010637028223496603,
+ 0.010637028223496605,
0.01228354449019281,
0.013704743173963509,
0.014829116089757849,
@@ -595,7 +599,7 @@
0.003648297363886403,
0.006220739206137793,
0.008560805562643258,
- 0.010594467066356217,
+ 0.010594467066356215,
0.012137407181277772,
0.013580247763948514,
0.014709634995905748,
@@ -608,7 +612,7 @@
0.01604088566988471,
0.01484899400756596,
0.01347829835941078,
- 0.011713354867882659,
+ 0.01171335486788266,
0.008831687333185652,
0.005731739757859993
],
@@ -629,7 +633,7 @@
0.000920067680617316,
0.0004569072602015561,
0.0007209820742233905,
- 0.0009019129537077271,
+ 0.0009019129537077272,
0.000654822071277212,
0.0002964891583124209,
0.0008697756394143753,
@@ -678,17 +682,17 @@
"y": [
0.004958981353614306,
0.007709876078177458,
- 0.010610160150823023,
+ 0.010610160150823025,
0.013147576784275966,
0.016230808947783,
0.01765523354548008,
0.020390817639546265,
0.021631607060529953,
- 0.022142205928372478,
+ 0.02214220592837248,
0.022393833653832424,
0.021357150137205804,
0.020907723382855792,
- 0.019930544841328238,
+ 0.01993054484132824,
0.019095495355673266,
0.01816255322567264,
0.016320905888505866,
@@ -702,12 +706,12 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 0.0019368529298846857,
+ 0.0019368529298846855,
0.001756890524910662,
0.0015769649797891588,
0.0013970905356792654,
0.0012172898458174732,
- 0.0010376012524176477,
+ 0.0010376012524176475,
0.0008580951787241413,
0.0006789164152700594,
0.0005004166749212909,
@@ -762,8 +766,8 @@
"y": [
0.02420659950459554,
0.02410222421800216,
- 0.023997848931408782,
- 0.023893473644815394,
+ 0.023997848931408786,
+ 0.02389347364481539,
0.023789098358222013,
0.023684723071628624,
0.023580347785035247,
@@ -772,10 +776,10 @@
0.02326722192525509,
0.02316284663866171,
0.023058471352068326,
- 0.022954096065474938,
+ 0.022954096065474935,
0.02284972077888156,
0.022745345492288172,
- 0.022640970205694794,
+ 0.02264097020569479,
0.02253659491910141,
0.022432219632508025,
0.022327844345914644
@@ -2136,7 +2140,7 @@
132
],
"y": [
- 0.09061988167926385,
+ 0.09061988167926384,
-0.012576582685820214,
-0.004320865536613489,
-0.037343734133440394,
@@ -2156,14 +2160,14 @@
0.005310804470794357,
-0.005696818394814609,
0.03833367306762126,
- -0.011200629827619093,
+ -0.011200629827619091,
-0.12678066991651324,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.05935897986465832,
- -0.051103262715451604,
+ -0.0511032627154516,
0.06310082451524143,
-0.0318399227006359,
- -0.030463969842434782,
+ -0.030463969842434786,
0.005310804470794357,
0.02182223876920781,
0.041085578784023497,
@@ -2186,7 +2190,7 @@
-0.037343734133440394,
0.03833367306762126,
-0.0318399227006359,
- -0.011200629827619093,
+ -0.011200629827619091,
-0.019456346976825818,
0.039709625925822375,
0.0741084473808504,
@@ -2197,7 +2201,7 @@
-0.027712064126032544,
0.024574144485610048,
0.017694380194604446,
- -0.051103262715451604,
+ -0.0511032627154516,
0.04934129593323023,
-0.001568959820211247,
0.04658939021682799,
@@ -2206,7 +2210,7 @@
-0.015328488402222454,
-0.04009563984984263,
-0.0249601584096303,
- -0.00019300696201012598,
+ -0.000193006962010126,
0.15391371315651542,
0.006686757328995478,
-0.05935897986465832,
@@ -2236,12 +2240,12 @@
0.024574144485610048,
-0.029088016984233665,
-0.0029449126784123676,
- -0.011200629827619093,
+ -0.011200629827619091,
0.001182945896190995,
-0.007072771253015731,
0.04246153164222462,
-0.004320865536613489,
- 0.10988322169407955,
+ 0.10988322169407956,
-0.037343734133440394,
-0.016704441260423575,
0.006686757328995478,
@@ -2258,8 +2262,8 @@
0.03145390877661565,
0.05622106022423583,
0.014942474478202204,
- 0.010814615903598841,
- 0.10988322169407955,
+ 0.01081461590359884,
+ 0.10988322169407956,
0.09887559882847057,
0.001182945896190995,
0.07823630595545376,
@@ -2421,7 +2425,7 @@
-0.027782168227463364,
-0.0295299254606757,
-0.005044688697438994,
- -0.027131385829086006,
+ -0.02713138582908601,
-0.014967919098312174,
-0.018483150909256105,
-0.01901799646632892,
@@ -2443,9 +2447,9 @@
0.00926588506800341,
-0.03464258186869676,
-0.008082039196945013,
- 0.011364869658653243,
- 0.0009835337264478081,
- 0.011194546620714103,
+ 0.011364869658653244,
+ 0.000983533726447808,
+ 0.011194546620714105,
-0.03700414327288421,
0.008013545770845095,
-0.014911745323678609,
@@ -2455,7 +2459,7 @@
-0.023694428109623075,
0.0037830236465225455,
0.022283588751957895,
- 0.020259778298767666,
+ 0.020259778298767663,
-0.007782211098720479,
0.001633485541469888,
-0.00336730837946193,
@@ -2463,7 +2467,7 @@
-0.0037485742600287713,
-0.023103696888923084,
-0.02538158359937403,
- 0.010877528444466239,
+ 0.01087752844446624,
0.003396870126202189,
-0.00931576420516173,
-0.02393598933865847,
@@ -2472,13 +2476,13 @@
-0.017131675982750533,
-0.016893899230157834,
0.0016655963268504305,
- 0.014312087552817005,
+ 0.014312087552817003,
0.01972723784114364,
-0.005721194774450792,
0.022818103401221923,
0.008053488228865244,
0.018756167519343916,
- -0.013320754164637287,
+ -0.013320754164637289,
0.034324781399503214,
-0.024136787863848923,
-0.009247336090314272,
@@ -2507,7 +2511,7 @@
0.003937343011525476,
-0.0213359342001851,
0.01935601032943401,
- -0.010330455512844257,
+ -0.010330455512844255,
0.03782723369570142,
0.0146552688808932,
0.013230530856790384,
@@ -2524,18 +2528,18 @@
-0.021721428589336345,
-0.010634357965284093,
0.0055700317473139855,
- -0.0009479228487899657,
+ -0.0009479228487899656,
-0.0020297213245332613,
-0.013546639944096802,
-0.018097376394800455,
0.01808774523221887,
- 0.012340078071508173,
+ 0.012340078071508172,
-0.020561983875713168,
-0.0017113426742070572,
-0.01261277549337654,
-0.03618135352270031,
0.02271639240732281,
- -0.010989417897220211,
+ -0.010989417897220213,
0.03516725603493444,
-0.015797770140132998,
0.00854976213952673,
@@ -3562,7 +3566,7 @@
-0.0763945037500033,
0.07120997975363674,
0.07120997975363674,
- -0.0018542395806650938,
+ -0.0018542395806650936,
0.03430885887772673,
-0.002592261998183278,
-0.047980640675552584,
@@ -3616,7 +3620,7 @@
-0.03949338287409329,
-0.05056371913686628,
0.07194800217115493,
- -0.012555564634678981,
+ -0.01255556463467898,
0.012906208769698923,
0.07120997975363674,
0.10811110062954676,
@@ -3832,7 +3836,7 @@
-0.03973989461680715,
-0.035215263664104976,
0.012493718436498158,
- -0.023285725079843646,
+ -0.02328572507984365,
-0.000379727084753736,
-0.019743775483154393,
-0.009392833506987743,
@@ -3863,16 +3867,16 @@
-0.04074701058422851,
-0.00726694018568288,
0.0049352105579747434,
- 0.010367920997501279,
+ 0.01036792099750128,
0.00007617849551178838,
0.0272796380000965,
0.013649300291033686,
- -0.015212767758098487,
+ -0.015212767758098489,
0.00543914128208987,
-0.03867304908811106,
-0.04146073514129962,
0.037180730183873835,
- -0.021689236815534446,
+ -0.02168923681553445,
0.006731851807441467,
-0.004709125236394509,
0.015675703756764758,
@@ -3889,7 +3893,7 @@
-0.03888936237339751,
0.0046740506114529325,
-0.04102493363171138,
- -0.0036541432594371158,
+ -0.003654143259437116,
-0.031746617901640996,
0.02287841546841343,
-0.017455500382433535,
@@ -3915,7 +3919,7 @@
-0.0011747844791662968,
-0.006854960778212722,
0.03271464639099751,
- -0.020651434806323598,
+ -0.0206514348063236,
0.013655494578349706,
0.0325822587161095,
0.01240140962718438,
@@ -3943,13 +3947,13 @@
-0.028761809138011133,
-0.04800248232671432,
-0.02509379567625947,
- -0.00009153975376653429,
+ -0.00009153975376653428,
0.023345773261553425,
-0.01910895717320799,
0.017869477687563542,
-0.02194514854495601,
- -0.024607711275376646,
- 0.029117388128151366,
+ -0.02460771127537665,
+ 0.02911738812815137,
-0.035875224977058126,
-0.029496018124353707,
-0.03515240882947335,
@@ -5086,7 +5090,7 @@
" receiver_data=receiver_data_without_targets,\n",
" predictors=predictors,\n",
" imputed_variables=imputed_variables,\n",
- " models=[OLS, QuantReg, QRF, Matching], # MDN model excluded for efficiency\n",
+ " models=[OLS, QuantReg, QRF, Matching], # MDN model excluded for efficiency\n",
" tune_hyperparameters=False,\n",
" impute_all=True,\n",
" k_folds=3,\n",
diff --git a/docs/imputation-benchmarking/benchmarking-methods.ipynb b/docs/imputation-benchmarking/benchmarking-methods.ipynb
index 6881559..470aac8 100644
--- a/docs/imputation-benchmarking/benchmarking-methods.ipynb
+++ b/docs/imputation-benchmarking/benchmarking-methods.ipynb
@@ -105,7 +105,7 @@
"xaxis": "x",
"y": [
0.005325246764127821,
- 0.0032574568108043474,
+ 0.003257456810804347,
0.004291351787466084,
0.00827890736591798,
0.006265312781526698,
@@ -122,10 +122,10 @@
0.019167487119075836,
0.017053293276698633,
0.018110390197887235,
- 0.022880240814547738,
+ 0.022880240814547734,
0.022277082286986168,
0.022578661550766953,
- 0.021446312414568482,
+ 0.02144631241456848,
0.020775745672473023,
0.021111029043520753,
0.02154448433197946,
@@ -148,14 +148,14 @@
0.019579226557658855,
0.017968552915103967,
0.018433975664527633,
- 0.018201264289815798,
- 0.015487972734223405,
+ 0.0182012642898158,
+ 0.015487972734223403,
0.015297795009410965,
0.015392883871817184,
0.014002252850255682,
0.015452033402285553,
- 0.014727143126270617,
- 0.011646126832392183,
+ 0.014727143126270615,
+ 0.011646126832392184,
0.011628413888604182,
0.011637270360498182,
0.008636036703271307,
@@ -247,12 +247,12 @@
0.007874317038513602,
0.006400529862683101,
0.0071374234505983515,
- 0.010486056164399217,
+ 0.010486056164399215,
0.008643072078145794,
- 0.009564564121272505,
- 0.012477053907035603,
+ 0.009564564121272504,
+ 0.012477053907035605,
0.010587117407279092,
- 0.011532085657157347,
+ 0.011532085657157349,
0.013976146603677698,
0.012131660539961384,
0.013053903571819542,
@@ -261,12 +261,12 @@
0.014291605945810025,
0.01613677572230468,
0.014296626037316228,
- 0.015216700879810453,
+ 0.015216700879810452,
0.01673064714370026,
0.01483193151214712,
0.015781289327923692,
0.01714337943681527,
- 0.015202903584225455,
+ 0.015202903584225456,
0.016173141510520364,
0.01726262542006888,
0.015569408009243264,
@@ -284,13 +284,13 @@
0.014906351931382766,
0.015238960983911748,
0.014556748021160838,
- 0.013928188770263349,
+ 0.013928188770263347,
0.014242468395712093,
0.013066586876826098,
- 0.012863854530594179,
+ 0.01286385453059418,
0.012965220703710138,
0.011128971605116294,
- 0.011379943160295983,
+ 0.011379943160295985,
0.011254457382706138,
0.008753003147158062,
0.00923575725553136,
@@ -381,24 +381,24 @@
0.004531461146955999,
0.0035102788255859992,
0.004020869986270999,
- 0.0076735298021387045,
+ 0.007673529802138704,
0.006025791893771108,
0.006849660847954906,
0.010433717511492892,
0.008393984293670526,
0.009413850902581709,
- 0.012489837286539055,
- 0.010241987014461877,
- 0.011365912150500465,
- 0.013890261720005525,
+ 0.012489837286539057,
+ 0.010241987014461875,
+ 0.011365912150500463,
+ 0.013890261720005523,
0.01168257270107894,
0.012786417210542233,
0.015149616071954385,
0.013174235947149286,
- 0.014161926009551835,
+ 0.014161926009551837,
0.016156461741229155,
0.014290240245294137,
- 0.015223350993261647,
+ 0.015223350993261648,
0.016968888054397502,
0.014760122634728718,
0.015864505344563108,
@@ -420,14 +420,14 @@
0.0157897063629332,
0.01512288841239022,
0.01545629738766171,
- 0.015038726808881943,
+ 0.015038726808881945,
0.014038874878404531,
0.014538800843643236,
0.01378761758627736,
0.012672652438433218,
0.01323013501235529,
0.011333540802150066,
- 0.011236618106891921,
+ 0.01123661810689192,
0.011285079454520994,
0.008831817336389235,
0.009165763886223251,
@@ -519,14 +519,14 @@
0.02186702376399432,
0.021437134037748187,
0.02120513416077143,
- 0.021920924277633282,
+ 0.02192092427763328,
0.021563029219202355,
0.021403024010040805,
0.021974824791272253,
0.021688924400656527,
0.021600913859310185,
0.022028725304911227,
- 0.021814819582110706,
+ 0.021814819582110703,
0.021798803708579555,
0.02208262581855019,
0.02194071476356487,
@@ -536,11 +536,11 @@
0.0221945834071183,
0.022190426845828132,
0.022192505126473215,
- 0.022392473256387678,
+ 0.02239247325638768,
0.022244327359467103,
0.02231840030792739,
0.02259036310565705,
- 0.022298227873106074,
+ 0.02229822787310607,
0.022444295489381563,
0.022788252954926428,
0.02235212838674504,
@@ -559,19 +559,19 @@
0.023073771396652423,
0.023777702201273305,
0.022621630954939895,
- 0.023199666578106598,
+ 0.0231996665781066,
0.023975592050542675,
0.02267553146857886,
0.023325561759560767,
0.02417348189981205,
0.022729431982217833,
- 0.023451456941014942,
+ 0.023451456941014946,
0.024371371749081425,
0.022783332495856803,
- 0.023577352122469114,
+ 0.02357735212246911,
0.0245692615983508,
0.02283723300949577,
- 0.023703247303923286
+ 0.02370324730392329
],
"yaxis": "y"
}
@@ -1589,9 +1589,7 @@
"# 1. Prepare data\n",
"diabetes = load_diabetes()\n",
"df = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)\n",
- "X_train, X_test = train_test_split(\n",
- " df, test_size=0.2, random_state=RANDOM_STATE\n",
- ")\n",
+ "X_train, X_test = train_test_split(df, test_size=0.2, random_state=RANDOM_STATE)\n",
"\n",
"predictors = [\"age\", \"sex\", \"bmi\", \"bp\"]\n",
"imputed_variables = [\"s1\", \"s4\"] # Numerical variables\n",
@@ -1606,12 +1604,10 @@
"\n",
"# 3. Compare imputation methods using unified metrics function\n",
"# The function automatically detects that these are numerical variables and uses quantile loss\n",
- "loss_comparison_df = compare_metrics(\n",
- " Y_test, method_imputations, imputed_variables\n",
- ")\n",
+ "loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)\n",
"\n",
"# 4. Plot results - filter for quantile loss metrics only\n",
- "quantile_loss_df = loss_comparison_df[loss_comparison_df['Metric'] == 'quantile_loss']\n",
+ "quantile_loss_df = loss_comparison_df[loss_comparison_df[\"Metric\"] == \"quantile_loss\"]\n",
"comparison_viz = method_comparison_results(\n",
" data=quantile_loss_df,\n",
" metric=\"quantile_loss\",\n",
@@ -1624,29 +1620,25 @@
"\n",
"# Display summary statistics\n",
"print(\"Summary of quantile loss by method:\")\n",
- "summary = quantile_loss_df[quantile_loss_df['Imputed Variable'] == 'mean_quantile_loss'].groupby('Method')['Loss'].mean()\n",
+ "summary = (\n",
+ " quantile_loss_df[quantile_loss_df[\"Imputed Variable\"] == \"mean_quantile_loss\"]\n",
+ " .groupby(\"Method\")[\"Loss\"]\n",
+ " .mean()\n",
+ ")\n",
"print(summary.sort_values())\n",
"\n",
"# Evaluate sensitivity to predictors\n",
- "leave_one_out_results = leave_one_out_analysis(\n",
- " df,\n",
- " predictors,\n",
- " imputed_variables,\n",
- " QRF\n",
- ")\n",
+ "leave_one_out_results = leave_one_out_analysis(df, predictors, imputed_variables, QRF)\n",
"\n",
"predictor_inclusion_results = progressive_predictor_inclusion(\n",
- " df,\n",
- " predictors,\n",
- " imputed_variables,\n",
- " QRF\n",
+ " df, predictors, imputed_variables, QRF\n",
")\n",
"\n",
"print(f\"Optimal subset: {predictor_inclusion_results['optimal_subset']}\")\n",
"print(f\"Optimal loss: {predictor_inclusion_results['optimal_loss']}\")\n",
"\n",
"# For step-by-step details:\n",
- "print(predictor_inclusion_results['results_df'])"
+ "print(predictor_inclusion_results[\"results_df\"])"
]
},
{
@@ -1765,8 +1757,8 @@
12516260.560869563,
7105905.247826086,
7105905.247826086,
- 9378343.482608695,
- 9378343.482608695,
+ 9378343.482608696,
+ 9378343.482608696,
9249420.56521739,
9249420.56521739,
7816548.561956522,
@@ -1785,8 +1777,8 @@
4781336.205434782,
2876876.4413043475,
2876876.4413043475,
- 1977240.7739130442,
- 1977240.7739130442
+ 1977240.773913044,
+ 1977240.773913044
],
"yaxis": "y"
},
@@ -1852,8 +1844,8 @@
10030371.987783346,
10428650.95469782,
10428650.95469782,
- 10481079.022997973,
- 10481079.022997973,
+ 10481079.022997972,
+ 10481079.022997972,
10334829.373528926,
10334829.373528926,
10101451.430757131,
@@ -1866,12 +1858,12 @@
8945728.183288647,
8606805.346431458,
8606805.346431458,
- 8299717.3234220585,
- 8299717.3234220585,
+ 8299717.323422059,
+ 8299717.323422059,
7940102.339771939,
7940102.339771939,
- 7571653.4587837765,
- 7571653.4587837765,
+ 7571653.458783776,
+ 7571653.458783776,
7094172.58117888,
7094172.58117888,
6497751.156066222,
@@ -2052,10 +2044,10 @@
11844897.445652174,
11432742.80978261,
11432742.80978261,
- 11020588.173913043,
- 11020588.173913043,
- 10608433.538043479,
- 10608433.538043479,
+ 11020588.173913045,
+ 11020588.173913045,
+ 10608433.53804348,
+ 10608433.53804348,
10196278.902173912,
10196278.902173912,
9784124.266304348,
@@ -2136,8 +2128,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 7417303.5172577705,
- "y1": 7417303.5172577705
+ "y0": 7417303.517257771,
+ "y1": 7417303.517257771
},
{
"line": {
@@ -3063,6 +3055,7 @@
"\n",
"logger = logging.getLogger(__name__)\n",
"\n",
+ "\n",
"# 1. Prepare data\n",
"@validate_call(config=VALIDATE_CONFIG)\n",
"def load_scf(\n",
@@ -3084,13 +3077,12 @@
" RuntimeError: If there's a network error or a problem processing\n",
" the downloaded data\n",
" \"\"\"\n",
+ "\n",
" def scf_url(year: int) -> str:\n",
" \"\"\"Return the URL of the SCF summary microdata zip file for a year.\"\"\"\n",
"\n",
" if year not in VALID_YEARS:\n",
- " logger.error(\n",
- " f\"Invalid SCF year: {year}. Valid years are {VALID_YEARS}\"\n",
- " )\n",
+ " logger.error(f\"Invalid SCF year: {year}. Valid years are {VALID_YEARS}\")\n",
" raise\n",
"\n",
" url = f\"https://www.federalreserve.gov/econres/files/scfp{year}s.zip\"\n",
@@ -3127,29 +3119,19 @@
" # Process zip file\n",
" z = zipfile.ZipFile(io.BytesIO(response.content))\n",
" # Find the .dta file in the zip\n",
- " dta_files: List[str] = [\n",
- " f for f in z.namelist() if f.endswith(\".dta\")\n",
- " ]\n",
+ " dta_files: List[str] = [f for f in z.namelist() if f.endswith(\".dta\")]\n",
" if not dta_files:\n",
- " logger.error(\n",
- " f\"No Stata files found in zip for year {year}\"\n",
- " )\n",
+ " logger.error(f\"No Stata files found in zip for year {year}\")\n",
" raise\n",
"\n",
" # Read the Stata file\n",
" try:\n",
" logger.debug(f\"Reading Stata file: {dta_files[0]}\")\n",
" with z.open(dta_files[0]) as f:\n",
- " df = pd.read_stata(\n",
- " io.BytesIO(f.read()), columns=columns\n",
- " )\n",
- " logger.debug(\n",
- " f\"Read DataFrame with shape {df.shape}\"\n",
- " )\n",
+ " df = pd.read_stata(io.BytesIO(f.read()), columns=columns)\n",
+ " logger.debug(f\"Read DataFrame with shape {df.shape}\")\n",
" except Exception as e:\n",
- " logger.error(\n",
- " f\"Error reading Stata file for year {year}: {str(e)}\"\n",
- " )\n",
+ " logger.error(f\"Error reading Stata file for year {year}: {str(e)}\")\n",
" raise\n",
"\n",
" # Add year column\n",
@@ -3172,38 +3154,41 @@
" )\n",
" return result\n",
" else:\n",
- " logger.info(\n",
- " f\"Returning data for single year, shape: {all_data[0].shape}\"\n",
- " )\n",
+ " logger.info(f\"Returning data for single year, shape: {all_data[0].shape}\")\n",
" return all_data[0]\n",
"\n",
" except Exception as e:\n",
" logger.error(f\"Error in _load: {str(e)}\")\n",
" raise\n",
"\n",
+ "\n",
"scf_data = load_scf(2022)\n",
"PREDICTORS: List[str] = [\n",
- " \"hhsex\", # sex of head of household\n",
- " \"age\", # age of respondent\n",
- " \"married\", # marital status of respondent\n",
- " # \"kids\", # number of children in household\n",
- " \"race\", # race of respondent\n",
- " \"income\", # total annual income of household\n",
- " \"wageinc\", # income from wages and salaries\n",
- " \"bussefarminc\", # income from business, self-employment or farm\n",
- " \"intdivinc\", # income from interest and dividends\n",
- " \"ssretinc\", # income from social security and retirement accounts\n",
- " \"lf\", # labor force status\n",
- " ]\n",
+ " \"hhsex\", # sex of head of household\n",
+ " \"age\", # age of respondent\n",
+ " \"married\", # marital status of respondent\n",
+ " # \"kids\", # number of children in household\n",
+ " \"race\", # race of respondent\n",
+ " \"income\", # total annual income of household\n",
+ " \"wageinc\", # income from wages and salaries\n",
+ " \"bussefarminc\", # income from business, self-employment or farm\n",
+ " \"intdivinc\", # income from interest and dividends\n",
+ " \"ssretinc\", # income from social security and retirement accounts\n",
+ " \"lf\", # labor force status\n",
+ "]\n",
"IMPUTED_VARIABLES: List[str] = [\"networth\"]\n",
"\n",
"# Evaluate predictors\n",
- "predictors_evaluation = compute_predictor_correlations(scf_data, PREDICTORS, IMPUTED_VARIABLES)\n",
+ "predictors_evaluation = compute_predictor_correlations(\n",
+ " scf_data, PREDICTORS, IMPUTED_VARIABLES\n",
+ ")\n",
"print(\"\\nMutual information with networth:\")\n",
"print(predictors_evaluation[\"predictor_target_mi\"])\n",
"\n",
"X_train, X_test = preprocess_data(\n",
- " data=scf_data, full_data=False, normalize=False,\n",
+ " data=scf_data,\n",
+ " full_data=False,\n",
+ " normalize=False,\n",
")\n",
"\n",
"# Shrink down the data by sampling\n",
@@ -3219,12 +3204,10 @@
")\n",
"\n",
"# 3. Compare imputation methods using unified metrics function\n",
- "loss_comparison_df = compare_metrics(\n",
- " Y_test, method_imputations, IMPUTED_VARIABLES\n",
- ")\n",
+ "loss_comparison_df = compare_metrics(Y_test, method_imputations, IMPUTED_VARIABLES)\n",
"\n",
"# 4. Plot results - filter for quantile loss metrics only\n",
- "quantile_loss_df = loss_comparison_df[loss_comparison_df['Metric'] == 'quantile_loss']\n",
+ "quantile_loss_df = loss_comparison_df[loss_comparison_df[\"Metric\"] == \"quantile_loss\"]\n",
"comparison_viz = method_comparison_results(\n",
" data=quantile_loss_df,\n",
" metric=\"quantile_loss\",\n",
@@ -3238,11 +3221,15 @@
"\n",
"# Compare distribution similarity\n",
"for model, imputations in method_imputations.items():\n",
- " print(f\"Model: {model}, distribution similarity: \\n{compare_distributions(\n",
- " donor_data=pd.DataFrame(scf_data[\"networth\"]),\n",
- " receiver_data=pd.DataFrame(imputations[0.5][\"networth\"]),\n",
- " imputed_variables=IMPUTED_VARIABLES,\n",
- ")}\")"
+ " print(\n",
+ " f\"Model: {model}, distribution similarity: \\n{\n",
+ " compare_distributions(\n",
+ " donor_data=pd.DataFrame(scf_data['networth']),\n",
+ " receiver_data=pd.DataFrame(imputations[0.5]['networth']),\n",
+ " imputed_variables=IMPUTED_VARIABLES,\n",
+ " )\n",
+ " }\"\n",
+ " )"
]
}
],
diff --git a/docs/models/matching/matching-imputation.ipynb b/docs/models/matching/matching-imputation.ipynb
index df9d7d0..0a0c94e 100644
--- a/docs/models/matching/matching-imputation.ipynb
+++ b/docs/models/matching/matching-imputation.ipynb
@@ -83,9 +83,11 @@
"source": [
"# Import needed libraries and setup R environment\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging\n",
+ "\n",
"logging.getLogger(\"rpy2\").setLevel(logging.ERROR)\n",
"\n",
"import sys\n",
@@ -845,7 +847,7 @@
0.030077955918414535,
0.03430885887772673,
true,
- 0.10988322169407955,
+ 0.10988322169407956,
0.03430885887772673,
true,
-0.0249601584096303,
@@ -899,13 +901,13 @@
-0.009824676969417972,
-0.03949338287409329,
false,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278,
false,
-0.02220825269322806,
-0.002592261998183278,
true,
- -0.051103262715451604,
+ -0.0511032627154516,
0.03430885887772673,
false,
-0.0249601584096303,
@@ -932,11 +934,11 @@
0.07823630595545376,
-0.002592261998183278,
true,
- -0.011200629827619093,
+ -0.011200629827619091,
-0.002592261998183278,
false,
0.03145390877661565,
- 0.019917421736121838,
+ 0.01991742173612184,
true,
0.024574144485610048,
0.03430885887772673,
@@ -944,10 +946,10 @@
-0.001568959820211247,
-0.03949338287409329,
true,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.03949338287409329,
true,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.05056371913686628,
false,
-0.06623874415566393,
@@ -1013,7 +1015,7 @@
-0.04972730985725048,
-0.03949338287409329,
true,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
true,
-0.005696818394814609,
@@ -1079,7 +1081,7 @@
-0.007072771253015731,
-0.03949338287409329,
false,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.0763945037500033,
true,
-0.08962994274508297,
@@ -1088,7 +1090,7 @@
-0.05935897986465832,
-0.03949338287409329,
false,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278,
false
],
@@ -1139,7 +1141,7 @@
-0.03949338287409329,
false,
0.001182945896190995,
- -0.015507654304751785,
+ -0.015507654304751784,
false,
0.001182945896190995,
0.03430885887772673,
@@ -1184,7 +1186,7 @@
-0.03949338287409329,
true,
-0.004320865536613489,
- -0.0011162171631468765,
+ -0.0011162171631468763,
false,
-0.07587041416307178,
-0.0763945037500033,
@@ -1220,7 +1222,7 @@
0.012906208769698923,
true,
-0.037343734133440394,
- -0.011079519799642579,
+ -0.01107951979964258,
true,
-0.07036660273026729,
-0.002592261998183278,
@@ -1240,13 +1242,13 @@
-0.001568959820211247,
-0.03949338287409329,
false,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
true,
0.024574144485610048,
0.05091436327188625,
false,
- -0.060734932722859444,
+ -0.06073493272285944,
-0.0763945037500033,
false,
-0.007072771253015731,
@@ -1313,7 +1315,7 @@
-0.0763945037500033,
true,
0.001182945896190995,
- -0.015507654304751785,
+ -0.015507654304751784,
false,
-0.0318399227006359,
-0.03949338287409329,
@@ -1336,7 +1338,7 @@
-0.06623874415566393,
-0.03949338287409329,
false,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
true,
0.08374011738825825,
@@ -2244,9 +2246,7 @@
"quantiles = list(imputed_values.keys())\n",
"\n",
"# Convert imputed_values dict to a 3D array: (n_samples, n_quantiles)\n",
- "pred_matrix = np.stack(\n",
- " [imputed_values[q].values.flatten() for q in quantiles], axis=1\n",
- ")\n",
+ "pred_matrix = np.stack([imputed_values[q].values.flatten() for q in quantiles], axis=1)\n",
"\n",
"# Actual values flattened\n",
"actual = actual_values.values.flatten()\n",
@@ -2502,7 +2502,7 @@
"\n",
"# Add quantile predictions\n",
"for q in quantiles_to_show:\n",
- " comparison_df[f\"Q{int(q*100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
+ " comparison_df[f\"Q{int(q * 100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
"\n",
"comparison_df"
]
@@ -2916,7 +2916,7 @@
0.03833367306762126,
0.09887559882847057,
0.030077955918414535,
- 0.10988322169407955,
+ 0.10988322169407956,
-0.0249601584096303,
0.03695772020942014
]
@@ -4137,10 +4137,10 @@
{
"error_y": {
"array": [
- 0.0023955551235670214,
+ 0.002395555123567022,
0.002189517306342527,
0.001998045402184313,
- 0.0018257279461347798,
+ 0.00182572794613478,
0.0016784747085066194,
0.0015633841084603756,
0.0014879382056280817,
@@ -4186,25 +4186,25 @@
"0.95"
],
"y": [
- 0.024143030757925926,
+ 0.02414303075792593,
0.024089235955485365,
0.0240354411530448,
- 0.023981646350604242,
- 0.023927851548163674,
+ 0.02398164635060424,
+ 0.02392785154816367,
0.023874056745723113,
0.02382026194328255,
0.02376646714084199,
0.02371267233840143,
0.02365887753596086,
0.0236050827335203,
- 0.023551287931079738,
+ 0.023551287931079735,
0.023497493128639173,
0.023443698326198616,
0.02338990352375805,
0.02333610872131749,
0.023282313918876925,
0.023228519116436364,
- 0.023174724313995802
+ 0.0231747243139958
]
}
],
diff --git a/docs/models/mdn/mdn-imputation.ipynb b/docs/models/mdn/mdn-imputation.ipynb
index 824d78e..26fd461 100644
--- a/docs/models/mdn/mdn-imputation.ipynb
+++ b/docs/models/mdn/mdn-imputation.ipynb
@@ -118,9 +118,11 @@
"outputs": [],
"source": [
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging\n",
+ "\n",
"logging.getLogger(\"pytorch_lightning\").setLevel(logging.ERROR)\n",
"logging.getLogger(\"pytorch_tabular\").setLevel(logging.ERROR)\n",
"logging.getLogger(\"lightning\").setLevel(logging.ERROR)\n",
@@ -455,7 +457,7 @@
" X_train,\n",
" predictors,\n",
" imputed_variables,\n",
- " tune_hyperparameters=False, # Hyperparameter tuning may be enabled, note it will have high computational cost\n",
+ " tune_hyperparameters=False, # Hyperparameter tuning may be enabled, note it will have high computational cost\n",
")"
]
},
@@ -593,7 +595,7 @@
-0.002592261998183278,
0.030077955918414535,
0.03430885887772673,
- 0.10988322169407955,
+ 0.10988322169407956,
0.03430885887772673,
-0.0249601584096303,
-0.002592261998183278,
@@ -629,11 +631,11 @@
-0.002592261998183278,
-0.009824676969417972,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278,
-0.02220825269322806,
-0.002592261998183278,
- -0.051103262715451604,
+ -0.0511032627154516,
0.03430885887772673,
-0.0249601584096303,
-0.0763945037500033,
@@ -651,17 +653,17 @@
0.03615391492152222,
0.07823630595545376,
-0.002592261998183278,
- -0.011200629827619093,
+ -0.011200629827619091,
-0.002592261998183278,
0.03145390877661565,
- 0.019917421736121838,
+ 0.01991742173612184,
0.024574144485610048,
0.03430885887772673,
-0.001568959820211247,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
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- -0.00019300696201012598,
+ -0.000193006962010126,
-0.05056371913686628,
-0.06623874415566393,
-0.002592261998183278,
@@ -705,7 +707,7 @@
0.0029429061332032365,
-0.04972730985725048,
-0.03949338287409329,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
-0.005696818394814609,
0.03430885887772673,
@@ -749,20 +751,20 @@
-0.03949338287409329,
-0.007072771253015731,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.0763945037500033,
-0.08962994274508297,
-0.0763945037500033,
-0.05935897986465832,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278
],
"xaxis": "x",
"y": [
0.07409832626581192,
-0.0053354548290371895,
- -0.12105628103017807,
+ -0.12105628103017808,
-0.014382392168045044,
0.04452350735664368,
-0.07941135764122009,
@@ -786,11 +788,11 @@
-0.026365965604782104,
-0.12046261131763458,
-0.027624964714050293,
- 0.029128486290574074,
+ 0.02912848629057407,
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+ -0.10975533723831175,
+ 0.02089877054095268,
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0.026763781905174255,
@@ -802,7 +804,7 @@
0.20666047930717468,
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0.024144992232322693,
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+ -0.09012438356876372,
0.022548293694853783,
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-0.11739443987607956,
@@ -810,15 +812,15 @@
-0.02206820249557495,
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+ 0.09131903946399687,
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0.13108958303928375,
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+ -0.01311248540878296,
0.03593648970127106,
-0.09408782422542572,
0.10447344928979874,
@@ -831,7 +833,7 @@
0.13786044716835022,
0.061320580542087555,
0.03548247739672661,
- 0.15061387419700623,
+ 0.15061387419700625,
0.0036609352100640535,
-0.02198277786374092,
0.13655224442481995,
@@ -847,7 +849,7 @@
-0.021243643015623093,
0.024414116516709328,
-0.020500551909208298,
- 0.14334836602210999,
+ 0.14334836602211,
0.09190063178539276,
-0.06325235962867737,
0.034143995493650436,
@@ -856,7 +858,7 @@
-0.021074065938591957,
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-0.025434572249650955,
- 0.018908560276031494,
+ 0.018908560276031498,
0.012480437755584717,
0.02391844429075718,
0.04059349000453949,
@@ -866,16 +868,16 @@
-0.09491998702287674,
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0.11002372950315475,
- -0.045452192425727844,
+ -0.04545219242572784,
0.048709649592638016,
0.16254602372646332,
- -0.23081794381141663,
+ -0.23081794381141665,
0.033685214817523956,
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-0.015713322907686234,
0.026455357670783997,
0.06460753083229065,
- 0.09441250562667847,
+ 0.09441250562667848,
-0.008670782670378685,
-0.08653200417757034,
-0.07236170768737793,
@@ -893,8 +895,8 @@
0.02713024616241455,
0.003619933035224676,
-0.02590913698077202,
- -0.09626477211713791,
- -0.026048922911286354,
+ -0.09626477211713792,
+ -0.02604892291128635,
0.01121927797794342,
-0.0919136255979538,
-0.04032476991415024,
@@ -903,8 +905,8 @@
0.09366445243358612,
0.16090711951255798,
0.12886887788772583,
- 0.024945732206106186,
- 0.10310709476470947,
+ 0.024945732206106182,
+ 0.10310709476470949,
0.09357581287622452,
-0.024438243359327316,
-0.02837105467915535,
@@ -917,11 +919,11 @@
-0.02144046686589718,
0.08984969556331635,
-0.04134964570403099,
- -0.051257528364658356,
+ -0.05125752836465835,
0.045112937688827515,
- 0.047064878046512604,
+ 0.04706487804651261,
0.034162651747465134,
- 0.021895015612244606,
+ 0.02189501561224461,
-0.05455716326832771,
-0.0889664888381958,
0.011050431989133358,
@@ -1816,9 +1818,7 @@
"quantiles = list(imputed_values.keys())\n",
"\n",
"# Convert imputed_values dict to array: (n_samples, n_quantiles)\n",
- "pred_matrix = np.stack(\n",
- " [imputed_values[q].values.flatten() for q in quantiles], axis=1\n",
- ")\n",
+ "pred_matrix = np.stack([imputed_values[q].values.flatten() for q in quantiles], axis=1)\n",
"\n",
"# Actual values flattened\n",
"actual = actual_values.values.flatten()\n",
@@ -1833,10 +1833,12 @@
")\n",
"\n",
"# Create DataFrame for plotting\n",
- "closest_df = pd.DataFrame({\n",
- " \"Actual\": actual,\n",
- " \"ClosestPrediction\": closest_predictions,\n",
- "})\n",
+ "closest_df = pd.DataFrame(\n",
+ " {\n",
+ " \"Actual\": actual,\n",
+ " \"ClosestPrediction\": closest_predictions,\n",
+ " }\n",
+ ")\n",
"\n",
"# Extract median predictions\n",
"median_predictions = imputed_values[0.5]\n",
@@ -2003,7 +2005,7 @@
"\n",
"# Add quantile predictions\n",
"for q in quantiles_to_show:\n",
- " comparison_df[f\"Q{int(q*100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
+ " comparison_df[f\"Q{int(q * 100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
"\n",
"comparison_df"
]
@@ -2063,8 +2065,8 @@
1
],
"y": [
- -1.5948072671890259,
- 2.5628609657287598
+ -1.594807267189026,
+ 2.56286096572876
]
},
{
@@ -2172,7 +2174,7 @@
],
"y": [
-3.3663272857666016,
- 1.5296334028244019
+ 1.529633402824402
]
},
{
@@ -2189,7 +2191,7 @@
8
],
"y": [
- -0.9083917140960693,
+ -0.9083917140960692,
4.910729885101318
]
},
@@ -2225,7 +2227,7 @@
0
],
"y": [
- -0.11874838173389435,
+ -0.11874838173389436,
1.195131540298462
]
},
@@ -2261,7 +2263,7 @@
2
],
"y": [
- -1.4394127130508423,
+ -1.4394127130508425,
0.04452350735664368
]
},
@@ -2316,7 +2318,7 @@
],
"y": [
-0.14701643586158752,
- 1.0303322076797485
+ 1.0303322076797483
]
},
{
@@ -2419,7 +2421,7 @@
0.03833367306762126,
0.09887559882847057,
0.030077955918414535,
- 0.10988322169407955,
+ 0.10988322169407956,
-0.0249601584096303,
0.03695772020942014
]
@@ -2453,7 +2455,7 @@
0.41260623931884766,
2.5850915908813477,
-0.6760748624801636,
- 1.8518712520599365,
+ 1.8518712520599363,
1.7761045694351196
]
},
@@ -3406,7 +3408,8 @@
"# Legend entries for intervals\n",
"fig.add_trace(\n",
" go.Scatter(\n",
- " x=[-1, -1], y=[0, 0],\n",
+ " x=[-1, -1],\n",
+ " y=[0, 0],\n",
" mode=\"lines\",\n",
" line=dict(color=\"rgba(173, 216, 230, 0.3)\", width=10),\n",
" name=\"80% PI (Q10-Q90)\",\n",
@@ -3414,7 +3417,8 @@
")\n",
"fig.add_trace(\n",
" go.Scatter(\n",
- " x=[-1, -1], y=[0, 0],\n",
+ " x=[-1, -1],\n",
+ " y=[0, 0],\n",
" mode=\"lines\",\n",
" line=dict(color=\"rgba(70, 130, 180, 0.5)\", width=10),\n",
" name=\"40% PI (Q30-Q70)\",\n",
@@ -3484,8 +3488,11 @@
"source": [
"# Run cross-validation\n",
"mdn_results = cross_validate_model(\n",
- " MDN, diabetes_df, predictors, imputed_variables,\n",
- " model_hyperparams={\"layers\": \"64-32\", \"num_gaussian\": 3, \"max_epochs\": 30}\n",
+ " MDN,\n",
+ " diabetes_df,\n",
+ " predictors,\n",
+ " imputed_variables,\n",
+ " model_hyperparams={\"layers\": \"64-32\", \"num_gaussian\": 3, \"max_epochs\": 30},\n",
")\n",
"\n",
"if \"quantile_loss\" in mdn_results:\n",
@@ -3516,13 +3523,13 @@
0.08970334204623862,
0.09257452417327834,
0.09425214250614794,
- 0.09727578509414461,
+ 0.0972757850941446,
0.1015340536222146,
0.10484112107099822,
- 0.10665196062518409,
+ 0.10665196062518408,
0.10767398977417002,
- 0.10657423431327813,
- 0.10291839541792067,
+ 0.10657423431327812,
+ 0.10291839541792068,
0.09739380134017506,
0.08998945316371061,
0.079000703908655,
@@ -3561,9 +3568,9 @@
0.04687617706207272,
0.07483489712462413,
0.0943290184639551,
- 0.10805756595645155,
- 0.11728923842580923,
- 0.12286641601968225,
+ 0.10805756595645157,
+ 0.11728923842580924,
+ 0.12286641601968223,
0.12616856929600748,
0.1292466478910964,
0.13271784812663345,
@@ -3574,7 +3581,7 @@
0.13210069705430905,
0.12552150971602297,
0.11633498833082316,
- 0.10223421559735249,
+ 0.10223421559735248,
0.08191549168418728,
0.0529798258038449
]
@@ -3586,20 +3593,20 @@
0.05800221625311354,
0.07291441840284316,
0.08871777392907335,
- 0.10179376511491461,
+ 0.1017937651149146,
0.11098038464484912,
- 0.11340640962306367,
+ 0.11340640962306368,
0.11611839981806513,
- 0.11748612497231077,
+ 0.11748612497231076,
0.1174333555879695,
0.11549620009870612,
0.1127200411691131,
- 0.10807914038546579,
+ 0.1080791403854658,
0.10210520685018526,
0.0948305718757984,
0.08725433626495321,
0.0765462308033389,
- 0.060192339856620924,
+ 0.06019233985662093,
0.03773031440262491
],
"type": "data"
@@ -3634,7 +3641,7 @@
0.04893408004321757,
0.07840558199482109,
0.09924928077798252,
- 0.11724509694752339,
+ 0.1172450969475234,
0.13076715505019737,
0.14024932427368472,
0.14459225917030372,
@@ -3647,7 +3654,7 @@
0.14127787715452564,
0.1335791298846442,
0.1235777656562175,
- 0.10962020934072403,
+ 0.10962020934072404,
0.08833690511355873,
0.05831330288784895
]
@@ -4593,16 +4600,16 @@
"np.random.seed(42)\n",
"\n",
"df_categorical = pd.DataFrame()\n",
- "df_categorical['age'] = df['age']\n",
- "df_categorical['sex'] = df['sex']\n",
- "df_categorical['bmi'] = df['bmi']\n",
- "df_categorical['bp'] = df['bp']\n",
- "df_categorical['risk_level'] = pd.qcut(\n",
- " df['s1'], q=3, labels=['low', 'medium', 'high']\n",
+ "df_categorical[\"age\"] = df[\"age\"]\n",
+ "df_categorical[\"sex\"] = df[\"sex\"]\n",
+ "df_categorical[\"bmi\"] = df[\"bmi\"]\n",
+ "df_categorical[\"bp\"] = df[\"bp\"]\n",
+ "df_categorical[\"risk_level\"] = pd.qcut(\n",
+ " df[\"s1\"], q=3, labels=[\"low\", \"medium\", \"high\"]\n",
").astype(str)\n",
"\n",
"print(\"Categorical variable distribution:\")\n",
- "print(df_categorical['risk_level'].value_counts())\n",
+ "print(df_categorical[\"risk_level\"].value_counts())\n",
"\n",
"# Split the data\n",
"X_train_cat, X_test_cat = preprocess_data(df_categorical)\n",
@@ -4668,16 +4675,16 @@
"# Evaluate categorical imputation accuracy\n",
"from sklearn.metrics import accuracy_score, confusion_matrix\n",
"\n",
- "predicted = imputed_cat_values[0.5]['risk_level'].values\n",
- "actual = actual_cat_values['risk_level'].values\n",
+ "predicted = imputed_cat_values[0.5][\"risk_level\"].values\n",
+ "actual = actual_cat_values[\"risk_level\"].values\n",
"\n",
"accuracy = accuracy_score(actual, predicted)\n",
"print(f\"Categorical imputation accuracy: {accuracy:.2%}\")\n",
"\n",
"conf_matrix = pd.DataFrame(\n",
" confusion_matrix(actual, predicted),\n",
- " index=['Actual: low', 'Actual: medium', 'Actual: high'],\n",
- " columns=['Predicted: low', 'Predicted: medium', 'Predicted: high']\n",
+ " index=[\"Actual: low\", \"Actual: medium\", \"Actual: high\"],\n",
+ " columns=[\"Predicted: low\", \"Predicted: medium\", \"Predicted: high\"],\n",
")\n",
"print(\"\\nConfusion matrix:\")\n",
"print(conf_matrix)"
@@ -4712,8 +4719,11 @@
"source": [
"# Run cross-validation for categorical variables\n",
"mdn_categorical_results = cross_validate_model(\n",
- " MDN, df_categorical, predictors_cat, imputed_variables_cat,\n",
- " model_hyperparams={\"layers\": \"64-32\", \"max_epochs\": 30}\n",
+ " MDN,\n",
+ " df_categorical,\n",
+ " predictors_cat,\n",
+ " imputed_variables_cat,\n",
+ " model_hyperparams={\"layers\": \"64-32\", \"max_epochs\": 30},\n",
")\n",
"\n",
"print(\"Categorical imputation cross-validation results (log loss):\")\n",
diff --git a/docs/models/ols/ols-imputation.ipynb b/docs/models/ols/ols-imputation.ipynb
index f09b1d9..c1c1c01 100644
--- a/docs/models/ols/ols-imputation.ipynb
+++ b/docs/models/ols/ols-imputation.ipynb
@@ -77,9 +77,11 @@
"source": [
"# Import necessary libraries\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging\n",
+ "\n",
"logging.getLogger(\"joblib\").setLevel(logging.ERROR)\n",
"\n",
"import pandas as pd\n",
@@ -691,7 +693,7 @@
-0.002592261998183278,
0.030077955918414535,
0.03430885887772673,
- 0.10988322169407955,
+ 0.10988322169407956,
0.03430885887772673,
-0.0249601584096303,
-0.002592261998183278,
@@ -727,11 +729,11 @@
-0.002592261998183278,
-0.009824676969417972,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
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-0.02220825269322806,
-0.002592261998183278,
- -0.051103262715451604,
+ -0.0511032627154516,
0.03430885887772673,
-0.0249601584096303,
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@@ -749,17 +751,17 @@
0.03615391492152222,
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+ -0.011200629827619091,
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0.03145390877661565,
- 0.019917421736121838,
+ 0.01991742173612184,
0.024574144485610048,
0.03430885887772673,
-0.001568959820211247,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.05056371913686628,
-0.06623874415566393,
-0.002592261998183278,
@@ -803,7 +805,7 @@
0.0029429061332032365,
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-0.03949338287409329,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
-0.005696818394814609,
0.03430885887772673,
@@ -847,13 +849,13 @@
-0.03949338287409329,
-0.007072771253015731,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.0763945037500033,
-0.08962994274508297,
-0.0763945037500033,
-0.05935897986465832,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278
],
"xaxis": "x",
@@ -877,7 +879,7 @@
-0.02296523633098297,
-0.0002549272258217522,
0.041955854264005145,
- -0.0031897494688274686,
+ -0.003189749468827469,
0.0402027220618115,
-0.024866536095489496,
-0.06912468804874679,
@@ -886,7 +888,7 @@
-0.07892515737008987,
0.015970447196464337,
0.03619541301703941,
- -0.028228730382243362,
+ -0.02822873038224336,
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-0.06775072190344172,
-0.03692692819649154,
@@ -914,14 +916,14 @@
-0.0016407795683481136,
-0.04889259692561741,
0.035264517478444876,
- -0.025364952890037026,
+ -0.025364952890037023,
-0.07502262099071229,
0.03285461135563695,
-0.0033241450781181566,
0.0069311803556940735,
0.02335877551972937,
0.006020682765349943,
- -0.0012186791520593099,
+ -0.00121867915205931,
0.0023458292473084886,
-0.0048534866374601165,
-0.019113418465764444,
@@ -936,11 +938,11 @@
0.022431302956834456,
0.02299766316282579,
0.0330832941728074,
- -0.0009029334348420945,
+ -0.0009029334348420944,
-0.04100725461887905,
-0.0030711943185808884,
-0.03824514844023506,
- 0.00020705616180374461,
+ 0.0002070561618037446,
-0.0474042240377852,
-0.06922782316125986,
-0.003176387805614361,
@@ -964,11 +966,11 @@
-0.037421796660582166,
-0.009089348860182794,
0.066913269713818,
- -0.010230297692207537,
+ -0.010230297692207535,
-0.03991377587845481,
0.06411056273760844,
0.06890387711398434,
- -0.023580709022924654,
+ -0.023580709022924657,
-0.04016111681959053,
0.029592803999122893,
0.00015354203815951678,
@@ -984,7 +986,7 @@
0.001664775986958149,
-0.04582909556245362,
-0.03233799582480009,
- 0.009719393670681685,
+ 0.009719393670681684,
-0.03913374776075587,
-0.007878790086778449,
0.023687275583639304,
@@ -996,7 +998,7 @@
-0.004113029099613391,
-0.06646155059455752,
-0.03841985292294773,
- -0.013154171189878341,
+ -0.01315417118987834,
0.03379751252954509,
-0.021956074452147704,
0.03667469879958614,
@@ -1012,11 +1014,11 @@
0.06479495776571835,
0.08113883362817842,
0.03253171497370689,
- 0.015467576859767131,
+ 0.015467576859767133,
0.0273038158718173,
0.019897980143643197,
-0.002518314433471329,
- 0.014960425406933221,
+ 0.01496042540693322,
0.04561355396433109,
-0.0023713124076254963,
-0.041974664159081174,
@@ -1030,12 +1032,12 @@
-0.041481556112161366,
-0.02937218462509378,
-0.07521240249368287,
- -0.09638511808503773,
+ -0.09638511808503772,
-0.07548521368019834,
-0.06230767899290826,
-0.03828709682806566,
-0.03236850494971294,
- -0.0020334292172825962
+ -0.002033429217282596
],
"yaxis": "y"
},
@@ -1920,9 +1922,7 @@
"quantiles = list(imputed_values.keys())\n",
"\n",
"# Convert imputed_values dict to a 3D array: (n_samples, n_quantiles)\n",
- "pred_matrix = np.stack(\n",
- " [imputed_values[q].values.flatten() for q in quantiles], axis=1\n",
- ")\n",
+ "pred_matrix = np.stack([imputed_values[q].values.flatten() for q in quantiles], axis=1)\n",
"\n",
"# Actual values flattened\n",
"actual = actual_values.values.flatten()\n",
@@ -2143,7 +2143,7 @@
"\n",
"# Add quantile predictions\n",
"for q in quantiles_to_show:\n",
- " comparison_df[f\"Q{int(q*100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
+ " comparison_df[f\"Q{int(q * 100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
"\n",
"comparison_df"
]
@@ -2238,7 +2238,7 @@
],
"y": [
-0.017954922014908045,
- 0.09686043563749905
+ 0.09686043563749903
]
},
{
@@ -2436,7 +2436,7 @@
],
"y": [
-0.028182984855784268,
- 0.018798528833127034
+ 0.01879852883312703
]
},
{
@@ -2557,7 +2557,7 @@
0.03833367306762126,
0.09887559882847057,
0.030077955918414535,
- 0.10988322169407955,
+ 0.10988322169407956,
-0.0249601584096303,
0.03695772020942014
]
@@ -3648,9 +3648,7 @@
"imputed_variables = [\"s1\", \"s4\"]\n",
"\n",
"# Run cross-validation on the same data set\n",
- "ols_results = cross_validate_model(\n",
- " OLS, diabetes_df, predictors, imputed_variables\n",
- ")\n",
+ "ols_results = cross_validate_model(OLS, diabetes_df, predictors, imputed_variables)\n",
"\n",
"# Check if we have quantile loss results (for numerical variables)\n",
"if \"quantile_loss\" in ols_results:\n",
@@ -3675,9 +3673,9 @@
"array": [
0.00007176011467255402,
0.0000913355164601245,
- 0.00011083575969884443,
- 0.00011210242100357653,
- 0.00011563162775412741,
+ 0.00011083575969884445,
+ 0.00011210242100357652,
+ 0.0001156316277541274,
0.00013000531898907708,
0.000143271836739722,
0.0001506544338309766,
@@ -3728,17 +3726,17 @@
0.010719210779992303,
0.012360163352286103,
0.013728123157267269,
- 0.014797115937605753,
+ 0.014797115937605751,
0.015594882419791484,
0.01617398236197238,
0.016566202754267158,
0.016699542778334436,
0.016548416575840677,
0.016129193671341906,
- 0.015426513127436165,
+ 0.015426513127436163,
0.0143934893281901,
- 0.012979751302578679,
- 0.011086126527490197,
+ 0.01297975130257868,
+ 0.011086126527490196,
0.008604890852799892,
0.005268542436316983
]
@@ -3795,12 +3793,12 @@
"0.95"
],
"y": [
- 0.0038771971010891706,
+ 0.00387719710108917,
0.006548161659672087,
0.008871648569474996,
0.010895901016821911,
- 0.012535169678463895,
- 0.013912511232847457,
+ 0.012535169678463897,
+ 0.013912511232847456,
0.01498764146777921,
0.01577647194645207,
0.016355539840059392,
@@ -4742,19 +4740,20 @@
"\n",
"# Create synthetic categorical variables based on diabetes features\n",
"df_categorical = pd.DataFrame()\n",
- "df_categorical['age'] = df['age']\n",
- "df_categorical['sex'] = df['sex'] \n",
- "df_categorical['bmi'] = df['bmi']\n",
- "df_categorical['bp'] = df['bp']\n",
- "df_categorical['risk_level'] = pd.qcut(df['s1'], \n",
- " q=3, \n",
- " labels=['low', 'medium', 'high'],\n",
- " ).astype(str)\n",
+ "df_categorical[\"age\"] = df[\"age\"]\n",
+ "df_categorical[\"sex\"] = df[\"sex\"]\n",
+ "df_categorical[\"bmi\"] = df[\"bmi\"]\n",
+ "df_categorical[\"bp\"] = df[\"bp\"]\n",
+ "df_categorical[\"risk_level\"] = pd.qcut(\n",
+ " df[\"s1\"],\n",
+ " q=3,\n",
+ " labels=[\"low\", \"medium\", \"high\"],\n",
+ ").astype(str)\n",
"\n",
"print(\"Categorical variable distribution:\")\n",
- "print(pd.Series(df_categorical['risk_level']).value_counts())\n",
+ "print(pd.Series(df_categorical[\"risk_level\"]).value_counts())\n",
"print(f\"\\nPercentage distribution:\")\n",
- "print(pd.Series(df_categorical['risk_level']).value_counts(normalize=True))\n",
+ "print(pd.Series(df_categorical[\"risk_level\"]).value_counts(normalize=True))\n",
"print(f\"\\nData types: {df_categorical.dtypes.to_dict()}\")\n",
"\n",
"# Split the categorical data for training and testing\n",
@@ -4831,8 +4830,8 @@
"from sklearn.metrics import accuracy_score, confusion_matrix\n",
"\n",
"# Get predictions and actual values\n",
- "predicted = imputed_cat_values[0.5]['risk_level'].values\n",
- "actual = actual_cat_values['risk_level'].values\n",
+ "predicted = imputed_cat_values[0.5][\"risk_level\"].values\n",
+ "actual = actual_cat_values[\"risk_level\"].values\n",
"\n",
"# Calculate accuracy\n",
"accuracy = accuracy_score(actual, predicted)\n",
@@ -4841,8 +4840,8 @@
"# Create confusion matrix\n",
"conf_matrix = pd.DataFrame(\n",
" confusion_matrix(actual, predicted),\n",
- " index=['Actual: low', 'Actual: medium', 'Actual: high'],\n",
- " columns=['Predicted: low', 'Predicted: medium', 'Predicted: high']\n",
+ " index=[\"Actual: low\", \"Actual: medium\", \"Actual: high\"],\n",
+ " columns=[\"Predicted: low\", \"Predicted: medium\", \"Predicted: high\"],\n",
")\n",
"print(\"\\nConfusion matrix:\")\n",
"print(conf_matrix)"
@@ -4925,7 +4924,7 @@
{
"error_y": {
"array": [
- 0.011486633730960285
+ 0.011486633730960283
],
"type": "data"
},
@@ -4940,7 +4939,7 @@
],
"xaxis": "x",
"y": [
- 1.0794183318669401
+ 1.07941833186694
],
"yaxis": "y"
}
diff --git a/docs/models/qrf/qrf-imputation.ipynb b/docs/models/qrf/qrf-imputation.ipynb
index ffba33f..6f66353 100644
--- a/docs/models/qrf/qrf-imputation.ipynb
+++ b/docs/models/qrf/qrf-imputation.ipynb
@@ -103,9 +103,11 @@
"source": [
"# Import necessary libraries\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging\n",
+ "\n",
"logging.getLogger(\"joblib\").setLevel(logging.ERROR)\n",
"\n",
"import pandas as pd\n",
@@ -724,7 +726,7 @@
-0.002592261998183278,
0.030077955918414535,
0.03430885887772673,
- 0.10988322169407955,
+ 0.10988322169407956,
0.03430885887772673,
-0.0249601584096303,
-0.002592261998183278,
@@ -760,11 +762,11 @@
-0.002592261998183278,
-0.009824676969417972,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278,
-0.02220825269322806,
-0.002592261998183278,
- -0.051103262715451604,
+ -0.0511032627154516,
0.03430885887772673,
-0.0249601584096303,
-0.0763945037500033,
@@ -782,17 +784,17 @@
0.03615391492152222,
0.07823630595545376,
-0.002592261998183278,
- -0.011200629827619093,
+ -0.011200629827619091,
-0.002592261998183278,
0.03145390877661565,
- 0.019917421736121838,
+ 0.01991742173612184,
0.024574144485610048,
0.03430885887772673,
-0.001568959820211247,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.05056371913686628,
-0.06623874415566393,
-0.002592261998183278,
@@ -836,7 +838,7 @@
0.0029429061332032365,
-0.04972730985725048,
-0.03949338287409329,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
-0.005696818394814609,
0.03430885887772673,
@@ -880,13 +882,13 @@
-0.03949338287409329,
-0.007072771253015731,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.0763945037500033,
-0.08962994274508297,
-0.0763945037500033,
-0.05935897986465832,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278
],
"xaxis": "x",
@@ -953,11 +955,11 @@
-0.002592261998183278,
0.02319819162740893,
0.05091436327188625,
- -0.00019300696201012598,
+ -0.000193006962010126,
0.03430885887772673,
0.0011829458961909658,
-0.002592261998183278,
- -0.009824676969417983,
+ -0.009824676969417984,
0.03430885887772673,
0.04658939021682799,
0.03430885887772673,
@@ -989,7 +991,7 @@
-0.0763945037500033,
-0.038719686991641536,
-0.03949338287409329,
- -0.009824676969417983,
+ -0.009824676969417984,
-0.002592261998183278,
-0.04835135699904936,
-0.03949338287409329,
@@ -1045,7 +1047,7 @@
0.07120997975363674,
0.08786797596286161,
0.05091436327188625,
- 0.012190568761799941,
+ 0.01219056876179994,
-0.033958214742706834,
0.020446285911006685,
-0.002592261998183278,
@@ -1953,9 +1955,7 @@
"quantiles = list(imputed_values.keys())\n",
"\n",
"# Convert imputed_values dict to a 3D array: (n_samples, n_quantiles)\n",
- "pred_matrix = np.stack(\n",
- " [imputed_values[q].values.flatten() for q in quantiles], axis=1\n",
- ")\n",
+ "pred_matrix = np.stack([imputed_values[q].values.flatten() for q in quantiles], axis=1)\n",
"\n",
"# Actual values flattened\n",
"actual = actual_values.values.flatten()\n",
@@ -2176,7 +2176,7 @@
"\n",
"# Add quantile predictions\n",
"for q in quantiles_to_show:\n",
- " comparison_df[f\"Q{int(q*100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
+ " comparison_df[f\"Q{int(q * 100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
"\n",
"comparison_df"
]
@@ -2396,8 +2396,8 @@
0
],
"y": [
- 0.020446285911006674,
- 0.020446285911006674
+ 0.02044628591100667,
+ 0.02044628591100667
]
},
{
@@ -2590,7 +2590,7 @@
0.03833367306762126,
0.09887559882847057,
0.030077955918414535,
- 0.10988322169407955,
+ 0.10988322169407956,
-0.0249601584096303,
0.03695772020942014
]
@@ -2616,7 +2616,7 @@
9
],
"y": [
- -0.015328488402222479,
+ -0.01532848840222248,
0.039709625925822375,
0.06998058880624704,
0.04658939021682799,
@@ -3681,9 +3681,7 @@
"imputed_variables = [\"s1\", \"s4\"]\n",
"\n",
"# Run cross-validation on the same data set\n",
- "qrf_results = cross_validate_model(\n",
- " QRF, diabetes_df, predictors, imputed_variables\n",
- ")\n",
+ "qrf_results = cross_validate_model(QRF, diabetes_df, predictors, imputed_variables)\n",
"\n",
"# Check if we have quantile loss results (for numerical variables)\n",
"if \"quantile_loss\" in qrf_results:\n",
@@ -3709,7 +3707,7 @@
0.00008551920671991658,
0.0004107245170541804,
0.00018326432680445731,
- 0.00044296698746322717,
+ 0.0004429669874632272,
0.00024075509310405528,
0.00029083292370858524,
0.00043090603620434025,
@@ -3723,7 +3721,7 @@
0.0002952487775964039,
0.0002930002413745392,
0.00022755896739762955,
- 0.00017022925074651195,
+ 0.00017022925074651197,
0.00005512096011768218
],
"type": "data"
@@ -3780,7 +3778,7 @@
"error_y": {
"array": [
0.000623604695409784,
- 0.0009828712930208695,
+ 0.0009828712930208697,
0.0012293155627994283,
0.001167454357338009,
0.000969638311140881,
@@ -3790,13 +3788,13 @@
0.0010923805491178616,
0.001634028502692155,
0.0006800631282736149,
- 0.0009467176016052857,
+ 0.0009467176016052856,
0.0010995284899625922,
0.0008688007594503854,
0.0010158843160004165,
- 0.0011140556744687275,
+ 0.0011140556744687277,
0.0006082527278363497,
- 0.00045185788868329796,
+ 0.0004518578886832979,
0.0004799569375129921
],
"type": "data"
@@ -3833,19 +3831,19 @@
0.01113193972961984,
0.013860904750387554,
0.016689810586868782,
- 0.019171101595596866,
- 0.020931660127358354,
+ 0.019171101595596863,
+ 0.02093166012735835,
0.021842069561388972,
0.023747004741023488,
0.02316096952448388,
0.02282294983336111,
0.022316332853161047,
0.02138806547874039,
- 0.020244041100969286,
+ 0.02024404110096929,
0.01872178310065714,
0.016499239789796494,
- 0.013710297740183203,
- 0.010793916043219703,
+ 0.013710297740183204,
+ 0.010793916043219704,
0.006778420409803051
]
}
@@ -4829,19 +4827,20 @@
"\n",
"# Create synthetic categorical variables based on diabetes features\n",
"df_categorical = pd.DataFrame()\n",
- "df_categorical['age'] = df['age']\n",
- "df_categorical['sex'] = df['sex'] \n",
- "df_categorical['bmi'] = df['bmi']\n",
- "df_categorical['bp'] = df['bp']\n",
- "df_categorical['risk_level'] = pd.qcut(df['s1'], \n",
- " q=3, \n",
- " labels=['low', 'medium', 'high'],\n",
- " ).astype(str)\n",
+ "df_categorical[\"age\"] = df[\"age\"]\n",
+ "df_categorical[\"sex\"] = df[\"sex\"]\n",
+ "df_categorical[\"bmi\"] = df[\"bmi\"]\n",
+ "df_categorical[\"bp\"] = df[\"bp\"]\n",
+ "df_categorical[\"risk_level\"] = pd.qcut(\n",
+ " df[\"s1\"],\n",
+ " q=3,\n",
+ " labels=[\"low\", \"medium\", \"high\"],\n",
+ ").astype(str)\n",
"\n",
"print(\"Categorical variable distribution:\")\n",
- "print(pd.Series(df_categorical['risk_level']).value_counts())\n",
+ "print(pd.Series(df_categorical[\"risk_level\"]).value_counts())\n",
"print(f\"\\nPercentage distribution:\")\n",
- "print(pd.Series(df_categorical['risk_level']).value_counts(normalize=True))\n",
+ "print(pd.Series(df_categorical[\"risk_level\"]).value_counts(normalize=True))\n",
"print(f\"\\nData types: {df_categorical.dtypes.to_dict()}\")\n",
"\n",
"# Split the categorical data for training and testing\n",
@@ -4918,8 +4917,8 @@
"from sklearn.metrics import accuracy_score, confusion_matrix\n",
"\n",
"# Get predictions and actual values\n",
- "predicted = imputed_cat_values[0.5]['risk_level'].values\n",
- "actual = actual_cat_values['risk_level'].values\n",
+ "predicted = imputed_cat_values[0.5][\"risk_level\"].values\n",
+ "actual = actual_cat_values[\"risk_level\"].values\n",
"\n",
"# Calculate accuracy\n",
"accuracy = accuracy_score(actual, predicted)\n",
@@ -4928,8 +4927,8 @@
"# Create confusion matrix\n",
"conf_matrix = pd.DataFrame(\n",
" confusion_matrix(actual, predicted),\n",
- " index=['Actual: low', 'Actual: medium', 'Actual: high'],\n",
- " columns=['Predicted: low', 'Predicted: medium', 'Predicted: high']\n",
+ " index=[\"Actual: low\", \"Actual: medium\", \"Actual: high\"],\n",
+ " columns=[\"Predicted: low\", \"Predicted: medium\", \"Predicted: high\"],\n",
")\n",
"print(\"\\nConfusion matrix:\")\n",
"print(conf_matrix)"
@@ -5012,7 +5011,7 @@
{
"error_y": {
"array": [
- 0.21683973320460714
+ 0.21683973320460712
],
"type": "data"
},
diff --git a/docs/models/quantreg/quantreg-imputation.ipynb b/docs/models/quantreg/quantreg-imputation.ipynb
index 2a93247..9a52966 100644
--- a/docs/models/quantreg/quantreg-imputation.ipynb
+++ b/docs/models/quantreg/quantreg-imputation.ipynb
@@ -81,9 +81,11 @@
"# Import necessary libraries\n",
"\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import logging\n",
+ "\n",
"logging.getLogger(\"joblib\").setLevel(logging.ERROR)\n",
"\n",
"import pandas as pd\n",
@@ -699,7 +701,7 @@
-0.002592261998183278,
0.030077955918414535,
0.03430885887772673,
- 0.10988322169407955,
+ 0.10988322169407956,
0.03430885887772673,
-0.0249601584096303,
-0.002592261998183278,
@@ -735,11 +737,11 @@
-0.002592261998183278,
-0.009824676969417972,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278,
-0.02220825269322806,
-0.002592261998183278,
- -0.051103262715451604,
+ -0.0511032627154516,
0.03430885887772673,
-0.0249601584096303,
-0.0763945037500033,
@@ -757,17 +759,17 @@
0.03615391492152222,
0.07823630595545376,
-0.002592261998183278,
- -0.011200629827619093,
+ -0.011200629827619091,
-0.002592261998183278,
0.03145390877661565,
- 0.019917421736121838,
+ 0.01991742173612184,
0.024574144485610048,
0.03430885887772673,
-0.001568959820211247,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.03949338287409329,
- -0.00019300696201012598,
+ -0.000193006962010126,
-0.05056371913686628,
-0.06623874415566393,
-0.002592261998183278,
@@ -811,7 +813,7 @@
0.0029429061332032365,
-0.04972730985725048,
-0.03949338287409329,
- 0.010814615903598841,
+ 0.01081461590359884,
-0.03949338287409329,
-0.005696818394814609,
0.03430885887772673,
@@ -855,20 +857,20 @@
-0.03949338287409329,
-0.007072771253015731,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.0763945037500033,
-0.08962994274508297,
-0.0763945037500033,
-0.05935897986465832,
-0.03949338287409329,
- -0.030463969842434782,
+ -0.030463969842434786,
-0.002592261998183278
],
"xaxis": "x",
"y": [
0.08014287312679191,
0.03714649322909741,
- -0.026031660118498466,
+ -0.026031660118498463,
-0.044193882521366384,
0.09101448103178654,
-0.0037397391866476936,
@@ -880,7 +882,7 @@
-0.005766866352520972,
0.030816567306961713,
0.03544781498501736,
- 0.10668826264912233,
+ 0.10668826264912232,
0.036734882953587095,
-0.02997727997822642,
-0.0011722847540775105,
@@ -892,9 +894,9 @@
-0.0006709918804689477,
-0.05578626296392443,
-0.07796135775452627,
- 0.015576453203715973,
+ 0.015576453203715972,
0.035171221074529704,
- -0.024743406630440574,
+ -0.024743406630440577,
-0.0380541517369264,
-0.07259407040873256,
-0.03840622837918541,
@@ -906,7 +908,7 @@
-0.029470847920470195,
0.015880100228139078,
0.03218833931116769,
- -0.010779699976034085,
+ -0.010779699976034084,
-0.0032634057101994887,
0.04513439333830303,
-0.003923939642995818,
@@ -914,7 +916,7 @@
-0.03983331463353444,
0.014840596881445116,
0.00037655395396533464,
- -0.013392987786083919,
+ -0.01339298778608392,
-0.035679435087367845,
-0.029262629871969204,
-0.004785461066430634,
@@ -928,7 +930,7 @@
-0.0033741099283343475,
0.011170405292467548,
0.019500166699586877,
- 0.0010965937113121965,
+ 0.0010965937113121963,
0.003276477624201848,
-0.0007842682110082554,
-0.003915230365606908,
@@ -968,7 +970,7 @@
-0.00283143384401933,
-0.05050664993682316,
-0.043885893993368015,
- -0.022702186982428846,
+ -0.02270218698242885,
-0.04079343726026782,
-0.007065282197659795,
0.07004192563517492,
@@ -988,7 +990,7 @@
-0.07660374125676828,
0.0886233448123382,
0.08405677601332026,
- -0.029998060971523518,
+ -0.02999806097152352,
0.005085240428089589,
-0.05233055980342409,
-0.02157230805144349,
@@ -1010,17 +1012,17 @@
0.03406004375439392,
0.04130900734749181,
0.07832176156023327,
- 0.042507579027702996,
+ 0.04250757902770299,
0.061768959513458344,
-0.050022611469575186,
0.012001464809311808,
-0.027678360943266977,
- -0.041651740851295964,
+ -0.04165174085129597,
0.039068858659739504,
0.06545052953410004,
0.07368543011926391,
0.03310918611125326,
- 0.020064642573002854,
+ 0.020064642573002858,
0.02755038598329164,
0.01953177249091752,
-0.0024850912479511085,
@@ -1032,7 +1034,7 @@
-0.03643251181359,
-0.006470190751127564,
-0.04911173816890387,
- 0.029230850771744758,
+ 0.02923085077174476,
-0.03752490612322712,
-0.005686980412318444,
-0.03928403363551349,
@@ -1928,9 +1930,7 @@
"quantiles = list(imputed_values.keys())\n",
"\n",
"# Convert imputed_values dict to a 3D array: (n_samples, n_quantiles)\n",
- "pred_matrix = np.stack(\n",
- " [imputed_values[q].values.flatten() for q in quantiles], axis=1\n",
- ")\n",
+ "pred_matrix = np.stack([imputed_values[q].values.flatten() for q in quantiles], axis=1)\n",
"\n",
"# Actual values flattened\n",
"actual = actual_values.values.flatten()\n",
@@ -2151,7 +2151,7 @@
"\n",
"# Add quantile predictions\n",
"for q in quantiles_to_show:\n",
- " comparison_df[f\"Q{int(q*100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
+ " comparison_df[f\"Q{int(q * 100)}\"] = imputed_values[q].iloc[:5, 0].values\n",
"\n",
"comparison_df"
]
@@ -2209,7 +2209,7 @@
1
],
"y": [
- -0.026031660118498466,
+ -0.026031660118498463,
0.10471629771224444
]
},
@@ -2246,7 +2246,7 @@
],
"y": [
-0.013884103418189783,
- 0.11355448959635461
+ 0.1135544895963546
]
},
{
@@ -2426,7 +2426,7 @@
],
"y": [
0.017276693092682247,
- 0.061518324551356716
+ 0.06151832455135672
]
},
{
@@ -2565,7 +2565,7 @@
0.03833367306762126,
0.09887559882847057,
0.030077955918414535,
- 0.10988322169407955,
+ 0.10988322169407956,
-0.0249601584096303,
0.03695772020942014
]
@@ -3694,11 +3694,11 @@
0.00013964325829394816,
0.0001352059984264296,
0.0001266211747154425,
- 0.00010806937892337481,
+ 0.0001080693789233748,
0.0000955126673952158,
- 0.00009572945677819657,
- 0.00010825711304818197,
- 0.00009924455051088341,
+ 0.00009572945677819656,
+ 0.00010825711304818196,
+ 0.0000992445505108834,
0.00008058254845100971
],
"type": "data"
@@ -3736,7 +3736,7 @@
0.01061192764658154,
0.012260635572631122,
0.013608913676947831,
- 0.014687816023102573,
+ 0.014687816023102571,
0.0154803423898212,
0.016035153061010853,
0.01636737614305893,
@@ -3746,7 +3746,7 @@
0.015215424354852342,
0.014217213287838575,
0.012835893477184366,
- 0.010950667994380053,
+ 0.010950667994380051,
0.008441313864545722,
0.005034747517506451
]
@@ -3803,7 +3803,7 @@
"0.95"
],
"y": [
- 0.0037022360177334317,
+ 0.003702236017733432,
0.006427987072919606,
0.008919835958629399,
0.010950435483523608,
diff --git a/examples/demo.ipynb b/examples/demo.ipynb
index c749976..e69abc1 100644
--- a/examples/demo.ipynb
+++ b/examples/demo.ipynb
@@ -70,9 +70,7 @@
")\n",
"\n",
"donor_data = diabetes_data.iloc[donor_indices].reset_index(drop=True)\n",
- "receiver_data = diabetes_data.iloc[receiver_indices].reset_index(\n",
- " drop=True\n",
- ")"
+ "receiver_data = diabetes_data.iloc[receiver_indices].reset_index(drop=True)"
]
},
{
@@ -124,9 +122,7 @@
"imputed_variables = [\"s1\", \"s4\", \"risk_factor\"]\n",
"\n",
"# Remove imputed variables from receiver data\n",
- "receiver_data = receiver_data.drop(\n",
- " columns=imputed_variables\n",
- ")\n",
+ "receiver_data = receiver_data.drop(columns=imputed_variables)\n",
"\n",
"print(f\"Donor data shape: {donor_data.shape}\")\n",
"print(f\"Receiver data shape: {receiver_data.shape}\")\n",
@@ -242,7 +238,11 @@
")\n",
"\n",
"all_attributes = dir(autoimpute_results)\n",
- "non_dunder_attributes = [attr for attr in all_attributes if (not (attr.startswith('__') and attr.endswith('__')))]\n",
+ "non_dunder_attributes = [\n",
+ " attr\n",
+ " for attr in all_attributes\n",
+ " if (not (attr.startswith(\"__\") and attr.endswith(\"__\")))\n",
+ "]\n",
"non_dunder_attributes"
]
},
@@ -260,9 +260,7 @@
}
],
"source": [
- "best_method_name = autoimpute_results.fitted_models[\n",
- " \"best_method\"\n",
- "].__class__.__name__\n",
+ "best_method_name = autoimpute_results.fitted_models[\"best_method\"].__class__.__name__\n",
"print(f\"Best performing method: {best_method_name}\")"
]
},
@@ -355,8 +353,8 @@
0.01804729212868635,
0.020713966247658862,
0.020713966247658862,
- 0.022742931417508282,
- 0.022742931417508282,
+ 0.02274293141750828,
+ 0.02274293141750828,
0.02258721277917371,
0.02258721277917371,
0.023351581275292608,
@@ -375,10 +373,10 @@
0.01912773338390523,
0.01739101077609373,
0.01739101077609373,
- 0.013723503511700213,
- 0.013723503511700213,
- 0.010967503180152355,
- 0.010967503180152355,
+ 0.013723503511700211,
+ 0.013723503511700211,
+ 0.010967503180152357,
+ 0.010967503180152357,
0.0071899999204732045,
0.0071899999204732045
],
@@ -450,12 +448,12 @@
0.008964045456920146,
0.010925268697485854,
0.010925268697485854,
- 0.012586171018988979,
- 0.012586171018988979,
+ 0.01258617101898898,
+ 0.01258617101898898,
0.013971078532560128,
0.013971078532560128,
- 0.015078371048174203,
- 0.015078371048174203,
+ 0.015078371048174204,
+ 0.015078371048174204,
0.015960673093506517,
0.015960673093506517,
0.016611314804344936,
@@ -472,8 +470,8 @@
0.01584349745574873,
0.014830479499612956,
0.014830479499612956,
- 0.013414418494530157,
- 0.013414418494530157,
+ 0.013414418494530156,
+ 0.013414418494530156,
0.011604173888267462,
0.011604173888267462,
0.009116693282835488,
@@ -541,8 +539,8 @@
],
"xaxis": "x",
"y": [
- 0.022039382576829306,
- 0.022039382576829306,
+ 0.022039382576829303,
+ 0.022039382576829303,
0.022147598558993443,
0.022147598558993443,
0.022255814541157576,
@@ -567,10 +565,10 @@
0.023229758380634808,
0.023337974362798945,
0.023337974362798945,
- 0.023446190344963078,
- 0.023446190344963078,
- 0.023554406327127214,
- 0.023554406327127214,
+ 0.02344619034496308,
+ 0.02344619034496308,
+ 0.023554406327127218,
+ 0.023554406327127218,
0.02366262230929135,
0.02366262230929135,
0.023770838291455487,
@@ -618,8 +616,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 0.012779822693513777,
- "y1": 0.012779822693513777
+ "y0": 0.012779822693513776,
+ "y1": 0.012779822693513776
},
{
"line": {
@@ -1577,18 +1575,10 @@
")\n",
"\n",
"print(\"Correlation analysis completed:\")\n",
- "print(\n",
- " f\" - Pearson correlation matrix: {predictor_correlations['pearson'].shape}\"\n",
- ")\n",
- "print(\n",
- " f\" - Spearman correlation matrix: {predictor_correlations['spearman'].shape}\"\n",
- ")\n",
- "print(\n",
- " f\" - Mutual information matrix: {predictor_correlations['mutual_info'].shape}\"\n",
- ")\n",
- "print(\n",
- " f\" - Predictor-target MI: {predictor_correlations['predictor_target_mi'].shape}\"\n",
- ")"
+ "print(f\" - Pearson correlation matrix: {predictor_correlations['pearson'].shape}\")\n",
+ "print(f\" - Spearman correlation matrix: {predictor_correlations['spearman'].shape}\")\n",
+ "print(f\" - Mutual information matrix: {predictor_correlations['mutual_info'].shape}\")\n",
+ "print(f\" - Predictor-target MI: {predictor_correlations['predictor_target_mi'].shape}\")"
]
},
{
diff --git a/examples/pipeline.py b/examples/pipeline.py
index 5907a86..9cb45e5 100644
--- a/examples/pipeline.py
+++ b/examples/pipeline.py
@@ -93,9 +93,7 @@ def categorize_risk(s4_value):
imputed_variables = ["s1", "s4", "risk_factor"]
# Remove imputed variables from receiver data
- receiver_data_without_targets = receiver_data.drop(
- columns=imputed_variables
- )
+ receiver_data_without_targets = receiver_data.drop(columns=imputed_variables)
print(f"Donor data shape: {donor_data.shape}")
print(f"Receiver data shape: {receiver_data_without_targets.shape}")
@@ -160,9 +158,7 @@ def categorize_risk(s4_value):
)
print("Correlation analysis completed:")
- print(
- f" - Pearson correlation matrix: {predictor_correlations['pearson'].shape}"
- )
+ print(f" - Pearson correlation matrix: {predictor_correlations['pearson'].shape}")
print(
f" - Spearman correlation matrix: {predictor_correlations['spearman'].shape}"
)
@@ -271,9 +267,7 @@ def categorize_risk(s4_value):
count = len(formatted_df[formatted_df["type"] == result_type])
print(f" - {result_type}: {count} rows")
print()
- print(
- f"The output file '{output_path}' is ready for dashboard visualization."
- )
+ print(f"The output file '{output_path}' is ready for dashboard visualization.")
print()
return formatted_df
diff --git a/microimpute/comparisons/autoimpute.py b/microimpute/comparisons/autoimpute.py
index 41b370a..69b8317 100644
--- a/microimpute/comparisons/autoimpute.py
+++ b/microimpute/comparisons/autoimpute.py
@@ -85,15 +85,11 @@ def _reverse_transformations(
# Reverse log transform if any
if params.get("log_transform"):
- result = unlog_transform_predictions(
- result, params["log_transform"]
- )
+ result = unlog_transform_predictions(result, params["log_transform"])
# Reverse asinh transform if any
if params.get("asinh_transform"):
- result = un_asinh_transform_predictions(
- result, params["asinh_transform"]
- )
+ result = un_asinh_transform_predictions(result, params["asinh_transform"])
return result
@@ -162,9 +158,7 @@ def _can_model_handle_variables(
continue
# Use VariableTypeDetector to categorize the variable
- var_type, _ = detector.categorize_variable(
- training_data[var], var, log
- )
+ var_type, _ = detector.categorize_variable(training_data[var], var, log)
# Check if model supports this variable type
if var_type in ["categorical", "numeric_categorical"]:
@@ -317,28 +311,22 @@ def _generate_imputations_for_all_models(
continue # Skip the best method as it's already done
# Check if model can handle the variable types using original data
- if not _can_model_handle_variables(
- model_name, donor_data, imputed_variables
- ):
- log.info(
- f"Skipping {model_name} due to incompatible variable types."
- )
+ if not _can_model_handle_variables(model_name, donor_data, imputed_variables):
+ log.info(f"Skipping {model_name} due to incompatible variable types.")
continue
log.info(f"Generating imputations with {model_name}.")
# Preprocess data fresh for this model
- training_data, imputing_data, transform_params = (
- prepare_data_for_imputation(
- donor_data,
- receiver_data,
- predictors,
- imputed_variables,
- weight_col,
- train_size,
- 1 - train_size,
- preprocessing=preprocessing,
- )
+ training_data, imputing_data, transform_params = prepare_data_for_imputation(
+ donor_data,
+ receiver_data,
+ predictors,
+ imputed_variables,
+ weight_col,
+ train_size,
+ 1 - train_size,
+ preprocessing=preprocessing,
)
# Get model-specific hyperparameters if available
@@ -360,9 +348,7 @@ def _generate_imputations_for_all_models(
)
# Reverse transformations if needed
- final_imputations = _reverse_transformations(
- imputations, transform_params
- )
+ final_imputations = _reverse_transformations(imputations, transform_params)
final_imputations_dict[model_name] = final_imputations[imputation_q]
fitted_models_dict[model_name] = fitted_model
@@ -486,17 +472,15 @@ def autoimpute(
# Keep track of original imputed variable names
original_imputed_variables = imputed_variables.copy()
- training_data, imputing_data, transform_params = (
- prepare_data_for_imputation(
- donor_data,
- receiver_data,
- predictors,
- imputed_variables,
- weight_col,
- train_size,
- 1 - train_size,
- preprocessing=preprocessing,
- )
+ training_data, imputing_data, transform_params = prepare_data_for_imputation(
+ donor_data,
+ receiver_data,
+ predictors,
+ imputed_variables,
+ weight_col,
+ train_size,
+ 1 - train_size,
+ preprocessing=preprocessing,
)
# Step 2: Model evaluation
@@ -524,14 +508,10 @@ def autoimpute(
# Log hyperparameter usage
if hyperparameters:
- model_names = [
- model_class.__name__ for model_class in model_classes
- ]
+ model_names = [model_class.__name__ for model_class in model_classes]
for model_name, model_params in hyperparameters.items():
if model_name in model_names:
- log.info(
- f"Using hyperparameters for {model_name}: {model_params}"
- )
+ log.info(f"Using hyperparameters for {model_name}: {model_params}")
else:
log.info(
f"Hyperparameters provided for {model_name} but model not in list: {model_names}"
@@ -563,9 +543,7 @@ def autoimpute(
log.info(
f"Comparing across {model_classes} methods using metric_priority='{metric_priority}'."
)
- best_method, _ = select_best_model_dual_metrics(
- method_results, metric_priority
- )
+ best_method, _ = select_best_model_dual_metrics(method_results, metric_priority)
# Step 4: Generate imputations with best method
if numeric_log_level <= logging.INFO:
@@ -624,9 +602,7 @@ def autoimpute(
)
# Reverse transformations if needed
- final_imputations = _reverse_transformations(
- imputations, transform_params
- )
+ final_imputations = _reverse_transformations(imputations, transform_params)
log.info(
f"Imputation generation completed for {len(receiver_data)} samples "
@@ -639,9 +615,7 @@ def autoimpute(
if var in median_imputations.columns:
receiver_data[var] = median_imputations[var]
else:
- log.warning(
- f"Imputed variable {var} not found in the imputations."
- )
+ log.warning(f"Imputed variable {var} not found in the imputations.")
# Initialize results
final_imputations_dict = {
@@ -668,21 +642,19 @@ def autoimpute(
else:
merged_hyperparams[model_name] = params
- other_imputations, other_models = (
- _generate_imputations_for_all_models(
- model_classes,
- best_method,
- donor_data,
- receiver_data,
- predictors,
- original_imputed_variables,
- weight_col,
- imputation_q,
- train_size,
- merged_hyperparams if merged_hyperparams else None,
- log_level,
- preprocessing=preprocessing,
- )
+ other_imputations, other_models = _generate_imputations_for_all_models(
+ model_classes,
+ best_method,
+ donor_data,
+ receiver_data,
+ predictors,
+ original_imputed_variables,
+ weight_col,
+ imputation_q,
+ train_size,
+ merged_hyperparams if merged_hyperparams else None,
+ log_level,
+ preprocessing=preprocessing,
)
final_imputations_dict.update(other_imputations)
fitted_models_dict.update(other_models)
diff --git a/microimpute/comparisons/autoimpute_helpers.py b/microimpute/comparisons/autoimpute_helpers.py
index fe4fe6a..8cc0f32 100644
--- a/microimpute/comparisons/autoimpute_helpers.py
+++ b/microimpute/comparisons/autoimpute_helpers.py
@@ -108,9 +108,7 @@ def prepare_data_for_imputation(
imputed variables.
"""
# Remove imputed variables from receiver if present
- receiver_data = receiver_data.drop(
- columns=imputed_variables, errors="ignore"
- )
+ receiver_data = receiver_data.drop(columns=imputed_variables, errors="ignore")
training_data = donor_data.copy()
imputing_data = receiver_data.copy()
@@ -141,9 +139,7 @@ def prepare_data_for_imputation(
raise ValueError(error_msg)
# Group columns by transformation type
- normalize_cols = [
- col for col, t in preprocessing.items() if t == "normalize"
- ]
+ normalize_cols = [col for col, t in preprocessing.items() if t == "normalize"]
log_cols = [col for col, t in preprocessing.items() if t == "log"]
asinh_cols = [col for col, t in preprocessing.items() if t == "asinh"]
@@ -169,9 +165,7 @@ def prepare_data_for_imputation(
full_data=True,
train_size=train_size,
test_size=test_size,
- normalize=(
- predictor_normalize if predictor_normalize else False
- ),
+ normalize=(predictor_normalize if predictor_normalize else False),
log_transform=predictor_log if predictor_log else False,
asinh_transform=predictor_asinh if predictor_asinh else False,
)
@@ -288,11 +282,7 @@ def evaluate_model(
model_hyperparams=hyperparameters,
)
- if (
- tune_hyperparams
- and isinstance(cv_result, tuple)
- and len(cv_result) == 2
- ):
+ if tune_hyperparams and isinstance(cv_result, tuple) and len(cv_result) == 2:
final_results, best_params = cv_result
return model_name, final_results, best_params
else:
@@ -342,8 +332,7 @@ def fit_and_predict_model(
categorical_vars = [
var
for var in imputed_variables
- if get_metric_for_variable_type(training_data[var], var)
- == "log_loss"
+ if get_metric_for_variable_type(training_data[var], var) == "log_loss"
]
error_msg = (
f"QuantReg does not support categorical variables: {categorical_vars}. "
@@ -412,16 +401,12 @@ def select_best_model_dual_metrics(
model_metrics = {}
for model_name, results in method_results.items():
model_metrics[model_name] = {
- "quantile_loss": results.get("quantile_loss", {}).get(
- "mean_test", np.inf
- ),
+ "quantile_loss": results.get("quantile_loss", {}).get("mean_test", np.inf),
"log_loss": results.get("log_loss", {}).get("mean_test", np.inf),
"n_quantile_vars": len(
results.get("quantile_loss", {}).get("variables", [])
),
- "n_log_vars": len(
- results.get("log_loss", {}).get("variables", [])
- ),
+ "n_log_vars": len(results.get("log_loss", {}).get("variables", [])),
}
# Select based on priority
@@ -453,9 +438,7 @@ def select_best_model_dual_metrics(
elif metric_priority == "categorical":
# Check if any model has categorical variables
- has_categorical = any(
- model_metrics[m]["n_log_vars"] > 0 for m in model_metrics
- )
+ has_categorical = any(model_metrics[m]["n_log_vars"] > 0 for m in model_metrics)
if not has_categorical:
error_msg = (
"No categorical variables found for evaluation with 'categorical' metric priority. "
@@ -483,8 +466,7 @@ def select_best_model_dual_metrics(
# Check if there are any variables to evaluate
total_vars_across_models = sum(
- model_metrics[m]["n_quantile_vars"]
- + model_metrics[m]["n_log_vars"]
+ model_metrics[m]["n_quantile_vars"] + model_metrics[m]["n_log_vars"]
for m in models
)
if total_vars_across_models == 0:
@@ -527,8 +509,7 @@ def select_best_model_dual_metrics(
else: # combined or other
# Check if there are any variables to evaluate
total_vars = sum(
- model_metrics[m]["n_quantile_vars"]
- + model_metrics[m]["n_log_vars"]
+ model_metrics[m]["n_quantile_vars"] + model_metrics[m]["n_log_vars"]
for m in model_metrics
)
if total_vars == 0:
@@ -549,9 +530,7 @@ def select_best_model_dual_metrics(
if not np.isnan(metrics["quantile_loss"])
else 0
)
- l_loss = (
- metrics["log_loss"] if not np.isnan(metrics["log_loss"]) else 0
- )
+ l_loss = metrics["log_loss"] if not np.isnan(metrics["log_loss"]) else 0
n_q = metrics["n_quantile_vars"]
n_l = metrics["n_log_vars"]
@@ -562,12 +541,12 @@ def select_best_model_dual_metrics(
best_method = model_name
if best_method is None:
- error_msg = "Failed to select a model - all models have infinite combined scores."
+ error_msg = (
+ "Failed to select a model - all models have infinite combined scores."
+ )
log.error(error_msg)
raise RuntimeError(error_msg)
- log.info(
- f"Selected {best_method} based on combined metric: {best_score:.6f}"
- )
+ log.info(f"Selected {best_method} based on combined metric: {best_score:.6f}")
return best_method, model_metrics[best_method]
diff --git a/microimpute/comparisons/imputations.py b/microimpute/comparisons/imputations.py
index 0316880..f24ecd3 100644
--- a/microimpute/comparisons/imputations.py
+++ b/microimpute/comparisons/imputations.py
@@ -63,9 +63,7 @@ def get_imputations(
if quantiles:
validate_quantiles(quantiles)
- log.info(
- f"Generating imputations for {len(model_classes)} model classes"
- )
+ log.info(f"Generating imputations for {len(model_classes)} model classes")
log.info(
f"Training data shape: {X_train.shape}, Test data shape: {X_test.shape}"
)
@@ -97,9 +95,7 @@ def get_imputations(
)
else:
log.info(f"Fitting {model_name}")
- fitted_model = model.fit(
- X_train, predictors, imputed_variables
- )
+ fitted_model = model.fit(X_train, predictors, imputed_variables)
# Get predictions
log.info(f"Generating predictions with {model_name}")
@@ -107,9 +103,7 @@ def get_imputations(
method_imputations[model_name] = imputations
except (TypeError, AttributeError, ValueError) as model_error:
- log.error(
- f"Error processing model {model_name}: {str(model_error)}"
- )
+ log.error(f"Error processing model {model_name}: {str(model_error)}")
raise RuntimeError(
f"Failed to process model {model_name}: {str(model_error)}"
) from model_error
diff --git a/microimpute/comparisons/metrics.py b/microimpute/comparisons/metrics.py
index 29336ec..adac8d0 100644
--- a/microimpute/comparisons/metrics.py
+++ b/microimpute/comparisons/metrics.py
@@ -30,9 +30,7 @@
MetricType = Literal["quantile_loss", "log_loss"]
-def get_metric_for_variable_type(
- series: pd.Series, col_name: str = "variable"
-) -> str:
+def get_metric_for_variable_type(series: pd.Series, col_name: str = "variable") -> str:
"""Detect the metric to use depending on whether a variable is categorical or numerical.
Uses the VariableTypeDetector from the imputer module for consistency.
@@ -97,9 +95,7 @@ def log_loss(
"""
try:
# Handle case where predictions are class labels instead of probabilities
- if len(y_pred.shape) == 1 or (
- len(y_pred.shape) == 2 and y_pred.shape[1] == 1
- ):
+ if len(y_pred.shape) == 1 or (len(y_pred.shape) == 2 and y_pred.shape[1] == 1):
# Binary case or class predictions
if labels is None:
labels = np.unique(y_true)
@@ -139,9 +135,7 @@ def log_loss(
f"for {n_classes}-class classification."
)
- return sklearn_log_loss(
- y_true, y_pred, normalize=normalize, labels=labels
- )
+ return sklearn_log_loss(y_true, y_pred, normalize=normalize, labels=labels)
except Exception as e:
log.error(f"Error computing log loss: {str(e)}")
raise RuntimeError(f"Failed to compute log loss: {str(e)}") from e
@@ -225,9 +219,7 @@ def compute_loss(
# Validate quantile value
validate_quantiles([q])
- log.debug(
- f"Computing quantile loss for q={q} with {len(test_y)} samples"
- )
+ log.debug(f"Computing quantile loss for q={q} with {len(test_y)} samples")
losses = quantile_loss(q, test_y, imputations)
mean_loss = np.mean(losses)
log.debug(f"Quantile loss at q={q}: mean={mean_loss:.6f}")
@@ -236,9 +228,7 @@ def compute_loss(
elif metric == "log_loss":
log.debug(f"Computing log loss with {len(test_y)} samples")
# Log loss returns a single value by default
- loss_value = log_loss(
- test_y, imputations, normalize=True, labels=labels
- )
+ loss_value = log_loss(test_y, imputations, normalize=True, labels=labels)
log.debug(f"Log loss: {loss_value:.6f}")
# Return array of same value for consistency
losses = np.full(len(test_y), loss_value)
@@ -291,7 +281,9 @@ def _compute_method_losses(
# Validate that the quantile exists in the imputation results
if quantile not in imputation:
- error_msg = f"Quantile {quantile} not found in imputations for method {method}"
+ error_msg = (
+ f"Quantile {quantile} not found in imputations for method {method}"
+ )
log.error(error_msg)
raise ValueError(error_msg)
@@ -419,8 +411,7 @@ def _compute_method_losses(
all_categorical_losses = [
r["Loss"]
for r in results
- if r["Imputed Variable"] == "mean_log_loss"
- and r["Percentile"] != "mean_loss"
+ if r["Imputed Variable"] == "mean_log_loss" and r["Percentile"] != "mean_loss"
]
if all_categorical_losses:
avg_cat_loss = np.mean(all_categorical_losses)
@@ -547,21 +538,15 @@ def kl_divergence(
- Uses scipy.special.rel_entr for numerical stability
"""
if len(donor_values) == 0 or len(receiver_values) == 0:
- raise ValueError(
- "Both donor and receiver values must be non-empty arrays"
- )
+ raise ValueError("Both donor and receiver values must be non-empty arrays")
# Get all unique categories from both distributions
- all_categories = np.union1d(
- np.unique(donor_values), np.unique(receiver_values)
- )
+ all_categories = np.union1d(np.unique(donor_values), np.unique(receiver_values))
# Calculate probability distributions (weighted if weights provided)
if donor_weights is not None:
# Compute weighted probabilities
- donor_df = pd.DataFrame(
- {"value": donor_values, "weight": donor_weights}
- )
+ donor_df = pd.DataFrame({"value": donor_values, "weight": donor_weights})
donor_grouped = donor_df.groupby("value")["weight"].sum()
donor_total = donor_grouped.sum()
donor_counts = donor_grouped / donor_total
@@ -577,15 +562,11 @@ def kl_divergence(
receiver_total = receiver_grouped.sum()
receiver_counts = receiver_grouped / receiver_total
else:
- receiver_counts = pd.Series(receiver_values).value_counts(
- normalize=True
- )
+ receiver_counts = pd.Series(receiver_values).value_counts(normalize=True)
# Create probability arrays for all categories
p_donor = np.array([donor_counts.get(cat, 0.0) for cat in all_categories])
- q_receiver = np.array(
- [receiver_counts.get(cat, 0.0) for cat in all_categories]
- )
+ q_receiver = np.array([receiver_counts.get(cat, 0.0) for cat in all_categories])
# Add small epsilon to avoid log(0) and division by zero
epsilon = 1e-10
@@ -643,17 +624,13 @@ def compare_distributions(
1 region kl_divergence 0.166667
"""
try:
- log.info(
- f"Comparing distributions for {len(imputed_variables)} variables"
- )
+ log.info(f"Comparing distributions for {len(imputed_variables)} variables")
log.info(f"Donor data shape: {donor_data.shape}")
log.info(f"Receiver data shape: {receiver_data.shape}")
# Validate inputs
validate_columns_exist(donor_data, imputed_variables, "donor_data")
- validate_columns_exist(
- receiver_data, imputed_variables, "receiver_data"
- )
+ validate_columns_exist(receiver_data, imputed_variables, "receiver_data")
# Convert weights to numpy arrays if provided
donor_weights_arr = None
@@ -703,9 +680,7 @@ def compare_distributions(
continue
# Detect variable type using donor data
- var_type, _ = detector.categorize_variable(
- donor_data[var], var, log
- )
+ var_type, _ = detector.categorize_variable(donor_data[var], var, log)
# Choose appropriate metric
if var_type in ["bool", "categorical", "numeric_categorical"]:
diff --git a/microimpute/comparisons/validation.py b/microimpute/comparisons/validation.py
index 47d487d..06e2f6b 100644
--- a/microimpute/comparisons/validation.py
+++ b/microimpute/comparisons/validation.py
@@ -27,9 +27,7 @@ def validate_quantiles(quantiles: List[float]) -> None:
"""
invalid_quantiles = [q for q in quantiles if not 0 <= q <= 1]
if invalid_quantiles:
- error_msg = (
- f"Invalid quantiles (must be between 0 and 1): {invalid_quantiles}"
- )
+ error_msg = f"Invalid quantiles (must be between 0 and 1): {invalid_quantiles}"
log.error(error_msg)
raise ValueError(error_msg)
diff --git a/microimpute/evaluations/cross_validation.py b/microimpute/evaluations/cross_validation.py
index 05f7a19..ffe2682 100644
--- a/microimpute/evaluations/cross_validation.py
+++ b/microimpute/evaluations/cross_validation.py
@@ -53,7 +53,7 @@ def _process_single_fold(
]:
"""Process a single CV fold and return results organized by variable."""
fold_idx, (train_idx, test_idx) = fold_idx_pair
- log.info(f"Processing fold {fold_idx+1}")
+ log.info(f"Processing fold {fold_idx + 1}")
# Split data for this fold
train_data = data.iloc[train_idx]
@@ -142,10 +142,7 @@ def _fit_model_for_fold(
)
for var in imputed_variables:
- if (
- get_metric_for_variable_type(train_data[var], var)
- == "log_loss"
- ):
+ if get_metric_for_variable_type(train_data[var], var) == "log_loss":
log.warning(
f"QuantReg does not support categorical variable '{var}'. "
f"Skipping QuantReg for this fold."
@@ -155,9 +152,7 @@ def _fit_model_for_fold(
# Handle model-specific hyperparameters
if model_hyperparams:
try:
- log.info(
- f"Fitting {model_name} with hyperparameters: {model_hyperparams}"
- )
+ log.info(f"Fitting {model_name} with hyperparameters: {model_hyperparams}")
fitted_model = model.fit(
X_train=train_data,
predictors=predictors,
@@ -168,9 +163,7 @@ def _fit_model_for_fold(
except ValueError as e:
# Check if it's due to categorical incompatibility
if "QuantReg does not support categorical" in str(e):
- log.warning(
- f"{model_name} incompatible with variable types: {str(e)}"
- )
+ log.warning(f"{model_name} incompatible with variable types: {str(e)}")
return None, None
raise e
except TypeError as e:
@@ -183,9 +176,7 @@ def _fit_model_for_fold(
imputed_variables=imputed_variables,
weight_col=weight_col,
)
- raise ValueError(
- f"Invalid hyperparameters for {model_name}"
- ) from e
+ raise ValueError(f"Invalid hyperparameters for {model_name}") from e
# Handle QuantReg which needs explicit quantiles
elif model_class == QuantReg:
@@ -200,9 +191,7 @@ def _fit_model_for_fold(
)
except ValueError as e:
if "QuantReg does not support categorical" in str(e):
- log.warning(
- f"QuantReg incompatible with variable types: {str(e)}"
- )
+ log.warning(f"QuantReg incompatible with variable types: {str(e)}")
return None, None
raise e
@@ -232,9 +221,7 @@ def _fit_model_for_fold(
"QuantReg does not support categorical" in str(e)
and model_name == "QuantReg"
):
- log.warning(
- f"QuantReg incompatible with variable types: {str(e)}"
- )
+ log.warning(f"QuantReg incompatible with variable types: {str(e)}")
return None, None
raise e
@@ -292,10 +279,7 @@ def _compute_fold_loss_by_metric(
else: # log_loss
# Use probabilities if available, otherwise use class predictions
- if (
- test_probabilities
- and test_probabilities[var][fold_idx] is not None
- ):
+ if test_probabilities and test_probabilities[var][fold_idx] is not None:
# Get probabilities and classes for this variable
test_prob_info = test_probabilities[var][fold_idx]
train_prob_info = train_probabilities[var][fold_idx]
@@ -316,14 +300,10 @@ def _compute_fold_loss_by_metric(
# Order probabilities alphabetically
test_probs_ordered, alphabetical_labels = (
- order_probabilities_alphabetically(
- test_probs, model_classes
- )
+ order_probabilities_alphabetically(test_probs, model_classes)
)
- train_probs_ordered, _ = (
- order_probabilities_alphabetically(
- train_probs, model_classes
- )
+ train_probs_ordered, _ = order_probabilities_alphabetically(
+ train_probs, model_classes
)
# Compute log loss with properly ordered probabilities
@@ -344,9 +324,7 @@ def _compute_fold_loss_by_metric(
log.warning(
f"Probabilities not in expected format for variable {var}, using class predictions"
)
- labels = np.unique(
- np.concatenate([test_y_var, train_y_var])
- )
+ labels = np.unique(np.concatenate([test_y_var, train_y_var]))
labels = np.sort(labels) # Ensure alphabetical order
_, test_loss = compute_loss(
test_y_var, test_pred_var, "log_loss", labels=labels
@@ -377,9 +355,7 @@ def _compute_fold_loss_by_metric(
for metric_type in ["quantile_loss", "log_loss"]:
if result[metric_type]["test"] is not None:
result[metric_type]["test"] = np.mean(result[metric_type]["test"])
- result[metric_type]["train"] = np.mean(
- result[metric_type]["train"]
- )
+ result[metric_type]["train"] = np.mean(result[metric_type]["train"])
else:
# No variables of this type
result[metric_type]["test"] = np.nan
@@ -461,18 +437,12 @@ def _compute_losses_parallel(
for metric_type in ["quantile_loss", "log_loss"]:
if not np.isnan(result[metric_type]["test"]):
- results[metric_type]["test"][q].append(
- result[metric_type]["test"]
- )
- results[metric_type]["train"][q].append(
- result[metric_type]["train"]
- )
+ results[metric_type]["test"][q].append(result[metric_type]["test"])
+ results[metric_type]["train"][q].append(result[metric_type]["train"])
# Store variable list (only once)
if fold_idx == 0 and q == quantiles[0]:
- results[metric_type]["variables"] = result[metric_type][
- "variables"
- ]
+ results[metric_type]["variables"] = result[metric_type]["variables"]
return results
@@ -538,13 +508,9 @@ def cross_validate_model(
for var in imputed_variables:
metric_type = get_metric_for_variable_type(data[var], var)
variable_metrics[var] = (
- "quantile_loss"
- if metric_type == "quantile_loss"
- else "log_loss"
- )
- log.info(
- f"Variable '{var}' will use metric: {variable_metrics[var]}"
+ "quantile_loss" if metric_type == "quantile_loss" else "log_loss"
)
+ log.info(f"Variable '{var}' will use metric: {variable_metrics[var]}")
# Set up k-fold cross-validation
kf = KFold(n_splits=n_splits, shuffle=True, random_state=random_state)
@@ -676,9 +642,7 @@ def cross_validate_model(
},
{
q: np.mean(values)
- for q, values in metric_results[metric_type][
- "test"
- ].items()
+ for q, values in metric_results[metric_type]["test"].items()
},
],
index=["train", "test"],
@@ -695,9 +659,7 @@ def cross_validate_model(
},
{
q: np.std(values) if len(values) > 1 else 0.0
- for q, values in metric_results[metric_type][
- "test"
- ].items()
+ for q, values in metric_results[metric_type]["test"].items()
},
],
index=["train", "test"],
@@ -751,9 +713,7 @@ def cross_validate_model(
if 0.5 in quantiles:
for fold_idx in range(n_splits):
- fold_loss = metric_results[primary_metric]["test"][0.5][
- fold_idx
- ]
+ fold_loss = metric_results[primary_metric]["test"][0.5][fold_idx]
if fold_loss < best_loss:
best_loss = fold_loss
best_fold = fold_idx
diff --git a/microimpute/evaluations/predictor_analysis.py b/microimpute/evaluations/predictor_analysis.py
index febf6ec..938c08b 100644
--- a/microimpute/evaluations/predictor_analysis.py
+++ b/microimpute/evaluations/predictor_analysis.py
@@ -100,9 +100,7 @@ def compute_predictor_correlations(
if imputed_variables:
missing_targets = set(imputed_variables) - set(data.columns)
if missing_targets:
- raise ValueError(
- f"Target variables not found in data: {missing_targets}"
- )
+ raise ValueError(f"Target variables not found in data: {missing_targets}")
valid_methods = ["all", "pearson", "spearman", "mutual_info"]
if method not in valid_methods:
@@ -193,9 +191,7 @@ def compute_predictor_correlations(
target_is_categorical = {}
for target in imputed_variables:
- var_type, _ = detector.categorize_variable(
- data[target], target, log
- )
+ var_type, _ = detector.categorize_variable(data[target], target, log)
target_is_categorical[target] = var_type in [
"categorical",
"numeric_categorical",
@@ -205,9 +201,7 @@ def compute_predictor_correlations(
if target_is_categorical[target]:
# Encode categorical targets
le = LabelEncoder()
- targets_encoded[target] = le.fit_transform(
- data[target].astype(str)
- )
+ targets_encoded[target] = le.fit_transform(data[target].astype(str))
else:
targets_encoded[target] = data[target].values
@@ -340,16 +334,12 @@ def process_predictor(pred):
"avg_log_loss": losses.get("log_loss", 0),
"loss_increase": loss_increase,
"relative_impact": (
- (loss_increase / baseline_total * 100)
- if baseline_total > 0
- else 0
+ (loss_increase / baseline_total * 100) if baseline_total > 0 else 0
),
}
except Exception as e:
- log.warning(
- f"Failed to evaluate model without predictor {pred}: {e}"
- )
+ log.warning(f"Failed to evaluate model without predictor {pred}: {e}")
return {
"predictor_removed": pred,
"avg_quantile_loss": np.nan,
@@ -447,7 +437,6 @@ def progressive_predictor_inclusion(
range(min(max_predictors, len(predictors))),
desc="Progressive inclusion",
):
-
best_predictor = None
best_step_loss = float("inf")
best_losses_detail = {}
@@ -468,9 +457,7 @@ def progressive_predictor_inclusion(
random_state=random_state,
)
- total_loss = losses["quantile_loss"] + losses.get(
- "log_loss", 0
- )
+ total_loss = losses["quantile_loss"] + losses.get("log_loss", 0)
if total_loss < best_step_loss:
best_step_loss = total_loss
@@ -494,9 +481,7 @@ def progressive_predictor_inclusion(
# Calculate improvements
marginal_improvement = (
- previous_loss - best_step_loss
- if previous_loss != float("inf")
- else 0.0
+ previous_loss - best_step_loss if previous_loss != float("inf") else 0.0
)
cumulative_improvement = first_loss - best_step_loss
@@ -558,13 +543,9 @@ def _compute_mutual_information(
# Use appropriate MI function based on target type
if y_is_categorical:
- mi = mutual_info_classif(x_clean, y_clean, random_state=RANDOM_STATE)[
- 0
- ]
+ mi = mutual_info_classif(x_clean, y_clean, random_state=RANDOM_STATE)[0]
else:
- mi = mutual_info_regression(
- x_clean, y_clean, random_state=RANDOM_STATE
- )[0]
+ mi = mutual_info_regression(x_clean, y_clean, random_state=RANDOM_STATE)[0]
return mi
diff --git a/microimpute/models/imputer.py b/microimpute/models/imputer.py
index 193c57d..14016fd 100644
--- a/microimpute/models/imputer.py
+++ b/microimpute/models/imputer.py
@@ -32,9 +32,7 @@ def __init__(self, constant_value, variable_name: str):
def predict(self, X: pd.DataFrame, **kwargs) -> pd.Series:
"""Return the constant value for all rows."""
- return pd.Series(
- self.constant_value, index=X.index, name=self.variable_name
- )
+ return pd.Series(self.constant_value, index=X.index, name=self.variable_name)
class Imputer(ABC):
@@ -55,16 +53,16 @@ def __init__(
self.imputed_variables: Optional[List[str]] = None
self.imputed_vars_dummy_info: Optional[Dict[str, Any]] = None
self.original_predictors: Optional[List[str]] = None
- self.categorical_targets: Dict[str, Dict] = (
- {}
- ) # {var_name: {"type": "categorical", "categories": [...]}}
- self.boolean_targets: Dict[str, Dict] = (
- {}
- ) # {var_name: {"type": "boolean", "dtype": ...}}
+ self.categorical_targets: Dict[
+ str, Dict
+ ] = {} # {var_name: {"type": "categorical", "categories": [...]}}
+ self.boolean_targets: Dict[
+ str, Dict
+ ] = {} # {var_name: {"type": "boolean", "dtype": ...}}
self.numeric_targets: List[str] = [] # [var_name, ...]
- self.constant_targets: Dict[str, Dict] = (
- {}
- ) # {var_name: {"value": constant, "dtype": ...}}
+ self.constant_targets: Dict[
+ str, Dict
+ ] = {} # {var_name: {"value": constant, "dtype": ...}}
self.seed = seed
self.logger = logging.getLogger(__name__)
@@ -99,9 +97,7 @@ def _validate_data(self, data: pd.DataFrame, columns: List[str]) -> None:
missing_count = data.isna().sum().sum()
if missing_count > 0:
- self.logger.warning(
- f"Data contains {missing_count} missing values"
- )
+ self.logger.warning(f"Data contains {missing_count} missing values")
def identify_target_types(
self,
@@ -194,13 +190,11 @@ def preprocess_data_types(
"""
try:
processor = DummyVariableProcessor(self.logger)
- processed_data, updated_predictors = (
- processor.preprocess_predictors(
- data,
- predictors,
- imputed_variables,
- not_numeric_categorical,
- )
+ processed_data, updated_predictors = processor.preprocess_predictors(
+ data,
+ predictors,
+ imputed_variables,
+ not_numeric_categorical,
)
# Store the processor for later use in test data
@@ -282,9 +276,7 @@ def fit(
raise ValueError("Weights must be positive")
# Identify target types BEFORE preprocessing
- self.identify_target_types(
- X_train, imputed_variables, not_numeric_categorical
- )
+ self.identify_target_types(X_train, imputed_variables, not_numeric_categorical)
X_train, predictors, imputed_variables, imputed_vars_dummy_info = (
self.preprocess_data_types(
@@ -361,9 +353,7 @@ def _handle_missing_variables(
"""
# Identify available and missing variables
available_vars = [v for v in imputed_variables if v in X_train.columns]
- missing_vars = [
- v for v in imputed_variables if v not in X_train.columns
- ]
+ missing_vars = [v for v in imputed_variables if v not in X_train.columns]
if missing_vars:
self.logger.warning(
@@ -429,12 +419,8 @@ def _validate_quantiles(
"""
if quantiles is not None:
if not isinstance(quantiles, list):
- self.logger.error(
- f"quantiles must be a list, got {type(quantiles)}"
- )
- raise ValueError(
- f"quantiles must be a list, got {type(quantiles)}"
- )
+ self.logger.error(f"quantiles must be a list, got {type(quantiles)}")
+ raise ValueError(f"quantiles must be a list, got {type(quantiles)}")
invalid_quantiles = [q for q in quantiles if not 0 <= q <= 1]
if invalid_quantiles:
@@ -468,18 +454,14 @@ def preprocess_data_types(
try:
if dummy_processor and hasattr(dummy_processor, "dummy_mapping"):
# Use existing processor with training mappings
- return dummy_processor.apply_dummy_encoding_to_test(
- data, predictors
- )
+ return dummy_processor.apply_dummy_encoding_to_test(data, predictors)
else:
# Fallback: create new processor (shouldn't happen normally)
processor = DummyVariableProcessor(self.logger)
# This will only encode predictors in test data
return processor.preprocess_predictors(data, predictors, [])
except Exception as e:
- self.logger.error(
- f"Error during test data preprocessing: {str(e)}"
- )
+ self.logger.error(f"Error during test data preprocessing: {str(e)}")
raise RuntimeError("Failed to preprocess data types") from e
# Note: postprocess_imputations removed - categorical targets now handled directly by classification
@@ -554,6 +536,4 @@ def _predict(
RuntimeError: If imputation fails.
NotImplementedError: If method is not implemented by subclass.
"""
- raise NotImplementedError(
- "Subclasses must implement the predict method"
- )
+ raise NotImplementedError("Subclasses must implement the predict method")
diff --git a/microimpute/models/matching.py b/microimpute/models/matching.py
index e1c8747..86b2d6e 100644
--- a/microimpute/models/matching.py
+++ b/microimpute/models/matching.py
@@ -99,9 +99,7 @@ def _predict(
RuntimeError: If matching or prediction fails.
"""
try:
- self.logger.info(
- f"Performing matching for {len(X_test)} recipient records"
- )
+ self.logger.info(f"Performing matching for {len(X_test)} recipient records")
# Create a copy to avoid modifying the input
try:
@@ -109,9 +107,7 @@ def _predict(
X_test_copy = X_test.copy()
# Drop imputed variables if they exist in test data
- if any(
- col in X_test.columns for col in self.imputed_variables
- ):
+ if any(col in X_test.columns for col in self.imputed_variables):
self.logger.debug(
f"Dropping imputed variables from test data: {self.imputed_variables}"
)
@@ -122,9 +118,7 @@ def _predict(
errors="ignore",
)
except Exception as copy_error:
- self.logger.error(
- f"Error preparing test data: {str(copy_error)}"
- )
+ self.logger.error(f"Error preparing test data: {str(copy_error)}")
raise RuntimeError(
"Failed to prepare test data for matching"
) from copy_error
@@ -146,9 +140,7 @@ def _predict(
X_test_copy, quantiles, chunk_size, return_probs
)
else:
- return self._predict_single(
- X_test_copy, quantiles, return_probs
- )
+ return self._predict_single(X_test_copy, quantiles, return_probs)
except ValueError as e:
raise e
@@ -181,9 +173,7 @@ def _predict_single(
z_variables=self.imputed_variables,
)
except Exception as matching_error:
- self.logger.error(
- f"Error in hot deck matching: {str(matching_error)}"
- )
+ self.logger.error(f"Error in hot deck matching: {str(matching_error)}")
raise RuntimeError("Hot deck matching failed") from matching_error
return self._process_matching_results(
@@ -206,8 +196,8 @@ def _predict_chunked(
chunk_data = X_test_copy.iloc[i:chunk_end]
self.logger.debug(
- f"Processing chunk {i//chunk_size + 1}: "
- f"rows {i} to {chunk_end-1} ({len(chunk_data)} records)"
+ f"Processing chunk {i // chunk_size + 1}: "
+ f"rows {i} to {chunk_end - 1} ({len(chunk_data)} records)"
)
try:
@@ -237,7 +227,7 @@ def _predict_chunked(
except Exception as chunk_error:
self.logger.warning(
- f"Chunk {i//chunk_size + 1} failed: {chunk_error}. "
+ f"Chunk {i // chunk_size + 1} failed: {chunk_error}. "
"Filling with NaN values."
)
# Create NaN-filled results for failed chunk
@@ -277,10 +267,7 @@ def _generate_one_hot_probabilities(
Returns:
Dict with 'probabilities' and 'classes' keys
"""
- if (
- variable not in categorical_targets
- and variable not in boolean_targets
- ):
+ if variable not in categorical_targets and variable not in boolean_targets:
return None
# Determine categories
@@ -319,9 +306,7 @@ def _process_matching_results(
try:
# Verify imputed variables exist in the result
missing_imputed = [
- var
- for var in self.imputed_variables
- if var not in fused0.columns
+ var for var in self.imputed_variables if var not in fused0.columns
]
if missing_imputed:
self.logger.error(
@@ -338,9 +323,7 @@ def _process_matching_results(
self.logger.error(
f"Error converting matching results: {str(convert_error)}"
)
- raise RuntimeError(
- "Failed to process matching results"
- ) from convert_error
+ raise RuntimeError("Failed to process matching results") from convert_error
# Create output dictionary with results
imputations: Dict[float, pd.DataFrame] = {}
@@ -352,9 +335,7 @@ def _process_matching_results(
try:
if quantiles:
- self.logger.info(
- f"Creating imputations for {len(quantiles)} quantiles"
- )
+ self.logger.info(f"Creating imputations for {len(quantiles)} quantiles")
# For each quantile, return a DataFrame with all imputed variables
for q in quantiles:
imputed_df = pd.DataFrame(index=X_test_copy.index)
@@ -417,12 +398,8 @@ def _process_matching_results(
# Return just the DataFrame for the single quantile
return imputations[q_default]
except Exception as output_error:
- self.logger.error(
- f"Error creating output imputations: {str(output_error)}"
- )
- raise RuntimeError(
- "Failed to create output imputations"
- ) from output_error
+ self.logger.error(f"Error creating output imputations: {str(output_error)}")
+ raise RuntimeError("Failed to create output imputations") from output_error
class Matching(Imputer):
@@ -493,9 +470,7 @@ def _fit(
self.donor_data = X_train.copy()
if tune_hyperparameters:
- self.logger.info(
- "Tuning hyperparameters for the matching model"
- )
+ self.logger.info("Tuning hyperparameters for the matching model")
best_params = self._tune_hyperparameters(
data=X_train,
predictors=predictors,
@@ -527,9 +502,7 @@ def _fit(
f"optional parameters: {matching_kwargs}"
)
self.logger.info(f"Using predictors: {predictors}")
- self.logger.info(
- f"Targeting imputed variables: {imputed_variables}"
- )
+ self.logger.info(f"Targeting imputed variables: {imputed_variables}")
return MatchingResults(
matching_hotdeck=self.matching_hotdeck,
@@ -547,9 +520,7 @@ def _fit(
)
except Exception as e:
self.logger.error(f"Error setting up matching model: {str(e)}")
- raise ValueError(
- f"Failed to set up matching model: {str(e)}"
- ) from e
+ raise ValueError(f"Failed to set up matching model: {str(e)}") from e
@validate_call(config=VALIDATE_CONFIG)
def _tune_hyperparameters(
@@ -610,9 +581,7 @@ def objective(trial: optuna.Trial) -> float:
variable_metrics = {}
for var in imputed_variables:
- variable_metrics[var] = get_metric_for_variable_type(
- data[var], var
- )
+ variable_metrics[var] = get_metric_for_variable_type(data[var], var)
# Track errors across CV folds
fold_errors = []
@@ -634,8 +603,7 @@ def objective(trial: optuna.Trial) -> float:
total_size = len(X_train_fold) * len(X_val_var)
use_chunking = (
len(X_val_var) > chunk_size
- or total_size
- > 25_000_000 # Lower threshold for tuning
+ or total_size > 25_000_000 # Lower threshold for tuning
)
if use_chunking:
@@ -661,9 +629,7 @@ def objective(trial: optuna.Trial) -> float:
except Exception:
# If chunk fails, use mean of training data as prediction
mean_val = X_train_fold[var].mean()
- y_pred_chunks.append(
- np.full(len(chunk_data), mean_val)
- )
+ y_pred_chunks.append(np.full(len(chunk_data), mean_val))
y_val_chunks.append(chunk_y_val.values)
# Combine chunk results
@@ -699,9 +665,7 @@ def objective(trial: optuna.Trial) -> float:
)
# Normalize by variable's standard deviation
std = np.std(y_val_combined.flatten())
- normalized_loss = (
- loss_value / std if std > 0 else loss_value
- )
+ normalized_loss = loss_value / std if std > 0 else loss_value
else: # log_loss for categorical/boolean
_, loss_value = compute_loss(
y_val_combined.flatten(),
@@ -738,8 +702,6 @@ def objective(trial: optuna.Trial) -> float:
)
best_params = study.best_params
- self.logger.info(
- f"Matching - Best hyperparameters found: {best_params}"
- )
+ self.logger.info(f"Matching - Best hyperparameters found: {best_params}")
return best_params
diff --git a/microimpute/models/mdn.py b/microimpute/models/mdn.py
index 1649ecb..f42ffaf 100644
--- a/microimpute/models/mdn.py
+++ b/microimpute/models/mdn.py
@@ -180,9 +180,7 @@ def _get_package_versions_hash() -> str:
return hashlib.md5("_".join(versions).encode()).hexdigest()[:6]
-def _generate_cache_key(
- predictors: List[str], target: str, data_hash: str
-) -> str:
+def _generate_cache_key(predictors: List[str], target: str, data_hash: str) -> str:
"""Generate a cache key for model storage.
Args:
@@ -547,9 +545,7 @@ def predict(
preds_df = self.model.predict(X, ret_logits=False)
# Extract probability columns (named like target_0_probability, etc.)
- prob_cols = sorted(
- [c for c in preds_df.columns if c.endswith("_probability")]
- )
+ prob_cols = sorted([c for c in preds_df.columns if c.endswith("_probability")])
probs = preds_df[prob_cols].values
if return_probs:
@@ -570,9 +566,7 @@ def predict(
)
if self.var_type == "boolean":
- predictions = pd.Series(
- sampled_indices.astype(bool), index=X.index
- )
+ predictions = pd.Series(sampled_indices.astype(bool), index=X.index)
else:
predictions = pd.Series(
[self.categories[i] for i in sampled_indices], index=X.index
@@ -749,9 +743,7 @@ def _predict(
imputed_df[variable] = quantile_values
else:
- raise ValueError(
- f"Unknown model type for variable {variable}"
- )
+ raise ValueError(f"Unknown model type for variable {variable}")
imputations[q] = imputed_df
@@ -767,9 +759,7 @@ def _predict(
except Exception as e:
self.logger.error(f"Error during MDN prediction: {str(e)}")
- raise RuntimeError(
- f"Failed to predict with MDN model: {str(e)}"
- ) from e
+ raise RuntimeError(f"Failed to predict with MDN model: {str(e)}") from e
class MDN(Imputer):
@@ -960,9 +950,7 @@ def _fit(
# Only categorical - apply to learning rate
classifier_params = best_params["classifier"]
if "learning_rate" in classifier_params:
- self.learning_rate = classifier_params[
- "learning_rate"
- ]
+ self.learning_rate = classifier_params["learning_rate"]
else:
# Only numeric - flat dict
if "num_gaussian" in best_params:
@@ -970,22 +958,16 @@ def _fit(
if "learning_rate" in best_params:
self.learning_rate = best_params["learning_rate"]
- self.logger.info(
- f"Applied tuned hyperparameters: {best_params}"
- )
+ self.logger.info(f"Applied tuned hyperparameters: {best_params}")
- self.logger.info(
- f"Fitting MDN model with {len(predictors)} predictors"
- )
+ self.logger.info(f"Fitting MDN model with {len(predictors)} predictors")
self.models = {}
# If force_retrain is True, delete existing cached models
if self.force_retrain and Path(self.model_dir).exists():
shutil.rmtree(self.model_dir)
- self.logger.info(
- f"Deleted cached models directory: {self.model_dir}"
- )
+ self.logger.info(f"Deleted cached models directory: {self.model_dir}")
# Ensure model directory exists
Path(self.model_dir).mkdir(parents=True, exist_ok=True)
@@ -1010,9 +992,7 @@ def _fit(
# Generate data hash for caching
Y = X_train[variable]
data_hash = _generate_data_hash(X_train[predictors], Y)
- cache_path = self._get_cache_path(
- predictors, variable, data_hash
- )
+ cache_path = self._get_cache_path(predictors, variable, data_hash)
# Check cache
if not self.force_retrain and self._model_exists(cache_path):
@@ -1020,16 +1000,14 @@ def _fit(
f"Loading cached model for '{variable}' from {cache_path}"
)
try:
- if variable in (
- categorical_targets or {}
- ) or variable in (boolean_targets or {}):
+ if variable in (categorical_targets or {}) or variable in (
+ boolean_targets or {}
+ ):
model = _NeuralClassifierModel.load(
cache_path, self.seed, self.logger
)
else:
- model = _MDNModel.load(
- cache_path, self.seed, self.logger
- )
+ model = _MDNModel.load(cache_path, self.seed, self.logger)
self.models[variable] = model
continue
except Exception as e:
@@ -1061,14 +1039,11 @@ def _fit(
X_train[predictors],
Y,
var_type=categorical_targets[variable]["type"],
- categories=categorical_targets[variable].get(
- "categories"
- ),
+ categories=categorical_targets[variable].get("categories"),
categorical_cols=categorical_cols,
)
self.logger.info(
- f"Neural classifier fitted for categorical variable "
- f"{variable}"
+ f"Neural classifier fitted for categorical variable {variable}"
)
elif variable in (boolean_targets or {}):
@@ -1096,15 +1071,12 @@ def _fit(
categorical_cols=categorical_cols,
)
self.logger.info(
- f"Neural classifier fitted for boolean variable "
- f"{variable}"
+ f"Neural classifier fitted for boolean variable {variable}"
)
else:
# Numeric target - use MDN
- self.logger.info(
- f"Training MDN for numeric variable {variable}"
- )
+ self.logger.info(f"Training MDN for numeric variable {variable}")
model = _MDNModel(
seed=self.seed,
@@ -1125,9 +1097,7 @@ def _fit(
Y,
categorical_cols=categorical_cols,
)
- self.logger.info(
- f"MDN fitted for numeric variable {variable}"
- )
+ self.logger.info(f"MDN fitted for numeric variable {variable}")
self.models[variable] = model
@@ -1198,9 +1168,7 @@ def _tune_mdn_hyperparameters(
def objective(trial: optuna.Trial) -> float:
# Only tune num_gaussian and learning_rate
num_gaussian = trial.suggest_int("num_gaussian", 2, 10)
- learning_rate = trial.suggest_float(
- "learning_rate", 1e-4, 1e-2, log=True
- )
+ learning_rate = trial.suggest_float("learning_rate", 1e-4, 1e-2, log=True)
# Track errors across CV folds
fold_errors = []
@@ -1249,9 +1217,7 @@ def objective(trial: optuna.Trial) -> float:
# Normalize by variable's standard deviation
std = np.std(y_val.values.flatten())
normalized_loss = (
- quantile_loss_value / std
- if std > 0
- else quantile_loss_value
+ quantile_loss_value / std if std > 0 else quantile_loss_value
)
var_errors.append(normalized_loss)
@@ -1327,9 +1293,7 @@ def _tune_classifier_hyperparameters(
def objective(trial: optuna.Trial) -> float:
# Only tune learning_rate for classifier
- learning_rate = trial.suggest_float(
- "learning_rate", 1e-4, 1e-2, log=True
- )
+ learning_rate = trial.suggest_float("learning_rate", 1e-4, 1e-2, log=True)
# Track errors across CV folds
fold_errors = []
@@ -1351,9 +1315,7 @@ def objective(trial: optuna.Trial) -> float:
var_type = "boolean"
categories = None
else:
- var_type = categorical_targets[var].get(
- "type", "categorical"
- )
+ var_type = categorical_targets[var].get("type", "categorical")
categories = categorical_targets[var].get("categories")
# Create and fit classifier with trial parameters
@@ -1378,16 +1340,12 @@ def objective(trial: optuna.Trial) -> float:
)
# Get probabilities for log loss calculation
- prob_info = model.predict(
- X_val_fold[predictors], return_probs=True
- )
+ prob_info = model.predict(X_val_fold[predictors], return_probs=True)
probs = prob_info["probabilities"]
classes = prob_info["classes"]
# Order probabilities alphabetically for consistent evaluation
- probs, classes = order_probabilities_alphabetically(
- probs, classes
- )
+ probs, classes = order_probabilities_alphabetically(probs, classes)
# Compute log loss
_, log_loss_value = compute_loss(
@@ -1416,14 +1374,11 @@ def objective(trial: optuna.Trial) -> float:
best_value = study.best_value
self.logger.info(
- f"Classifier - Lowest average log loss "
- f"({n_cv_folds}-fold CV): {best_value}"
+ f"Classifier - Lowest average log loss ({n_cv_folds}-fold CV): {best_value}"
)
best_params = study.best_params
- self.logger.info(
- f"Classifier - Best hyperparameters found: {best_params}"
- )
+ self.logger.info(f"Classifier - Best hyperparameters found: {best_params}")
return best_params
@@ -1465,9 +1420,7 @@ def _tune_hyperparameters(
for var in imputed_variables
if var in categorical_targets or var in boolean_targets
]
- numeric_vars = [
- var for var in imputed_variables if var not in categorical_vars
- ]
+ numeric_vars = [var for var in imputed_variables if var not in categorical_vars]
self.logger.info(
f"MDN hyperparameter tuning with {n_cv_folds}-fold CV and "
@@ -1478,17 +1431,14 @@ def _tune_hyperparameters(
# Tune appropriate models based on variable types
if not categorical_vars:
# Only numeric variables
- self.logger.info(
- "Tuning MDN hyperparameters (numeric variables only)"
- )
+ self.logger.info("Tuning MDN hyperparameters (numeric variables only)")
return self._tune_mdn_hyperparameters(
data, predictors, numeric_vars, n_cv_folds, n_trials
)
elif not numeric_vars:
# Only categorical variables
self.logger.info(
- "Tuning classifier hyperparameters "
- "(categorical/boolean variables only)"
+ "Tuning classifier hyperparameters (categorical/boolean variables only)"
)
return self._tune_classifier_hyperparameters(
data,
@@ -1502,8 +1452,7 @@ def _tune_hyperparameters(
else:
# Mixed: tune both separately
self.logger.info(
- "Tuning both MDN and classifier hyperparameters "
- "(mixed variable types)"
+ "Tuning both MDN and classifier hyperparameters (mixed variable types)"
)
mdn_params = self._tune_mdn_hyperparameters(
data, predictors, numeric_vars, n_cv_folds, n_trials
diff --git a/microimpute/models/ols.py b/microimpute/models/ols.py
index f2fb6e8..8595450 100644
--- a/microimpute/models/ols.py
+++ b/microimpute/models/ols.py
@@ -189,9 +189,7 @@ def _predict_variable(
# Classification for categorical/boolean targets
if return_probs and prob_results is not None:
# Get probabilities and classes
- prob_info = model.predict(
- X_test[self.predictors], return_probs=True
- )
+ prob_info = model.predict(X_test[self.predictors], return_probs=True)
prob_results[variable] = prob_info
# Get class predictions
@@ -339,9 +337,7 @@ def _predict(
except Exception as e:
self.logger.error(f"Error during prediction: {str(e)}")
- raise RuntimeError(
- f"Failed to predict with OLS model: {str(e)}"
- ) from e
+ raise RuntimeError(f"Failed to predict with OLS model: {str(e)}") from e
@validate_call(config=VALIDATE_CONFIG)
def _predict_quantile(
@@ -394,9 +390,7 @@ def _predict_quantile(
# Adjust each mean prediction by corresponding sampled quantile times standard error
return mean_preds + selected_quantiles * se
else:
- self.logger.info(
- f"Predicting at specified quantile {mean_quantile}"
- )
+ self.logger.info(f"Predicting at specified quantile {mean_quantile}")
specified_quantile = norm.ppf(mean_quantile)
return mean_preds + specified_quantile * se
@@ -453,9 +447,7 @@ def _fit(
RuntimeError: If model fitting fails.
"""
try:
- self.logger.info(
- f"Fitting OLS model with {len(predictors)} predictors"
- )
+ self.logger.info(f"Fitting OLS model with {len(predictors)} predictors")
self.models = {}
@@ -478,16 +470,12 @@ def _fit(
# Choose appropriate model based on variable type
if variable in (categorical_targets or {}):
# Use logistic regression for categorical targets
- model = _LogisticRegressionModel(
- seed=self.seed, logger=self.logger
- )
+ model = _LogisticRegressionModel(seed=self.seed, logger=self.logger)
model.fit(
X_train[predictors],
Y,
var_type=categorical_targets[variable]["type"],
- categories=categorical_targets[variable].get(
- "categories"
- ),
+ categories=categorical_targets[variable].get("categories"),
**kwargs,
)
self.logger.info(
@@ -495,12 +483,8 @@ def _fit(
)
elif variable in (boolean_targets or {}):
# Use logistic regression for boolean targets
- model = _LogisticRegressionModel(
- seed=self.seed, logger=self.logger
- )
- model.fit(
- X_train[predictors], Y, var_type="boolean", **kwargs
- )
+ model = _LogisticRegressionModel(seed=self.seed, logger=self.logger)
+ model.fit(X_train[predictors], Y, var_type="boolean", **kwargs)
self.logger.info(
f"Logistic regression fitted for boolean variable {variable}"
)
diff --git a/microimpute/models/qrf.py b/microimpute/models/qrf.py
index 2154f51..b1bbecf 100644
--- a/microimpute/models/qrf.py
+++ b/microimpute/models/qrf.py
@@ -97,9 +97,7 @@ def fit(
self.classifier = RandomForestClassifier(**classifier_params)
self.classifier.fit(X, y_encoded)
- def predict(
- self, X: pd.DataFrame, return_probs: bool = False
- ) -> pd.Series:
+ def predict(self, X: pd.DataFrame, return_probs: bool = False) -> pd.Series:
"""Predict classes or probabilities."""
if return_probs:
probs = self.classifier.predict_proba(X)
@@ -188,12 +186,8 @@ def predict(
# Sample from beta distribution
random_generator = np.random.default_rng(self.seed)
a = mean_quantile / (1 - mean_quantile)
- input_quantiles = (
- random_generator.beta(a, 1, size=len(X)) * count_samples
- )
- input_quantiles = np.clip(
- input_quantiles.astype(int), 0, count_samples - 1
- )
+ input_quantiles = random_generator.beta(a, 1, size=len(X)) * count_samples
+ input_quantiles = np.clip(input_quantiles.astype(int), 0, count_samples - 1)
# Extract predictions
if len(pred.shape) == 2:
@@ -254,9 +248,7 @@ def __init__(
self.constant_targets = constant_targets or {}
self.dummy_processor = dummy_processor
- def _get_encoded_predictors(
- self, current_predictors: List[str]
- ) -> List[str]:
+ def _get_encoded_predictors(self, current_predictors: List[str]) -> List[str]:
"""Get properly encoded predictor columns for sequential imputation.
Args:
@@ -355,7 +347,7 @@ def _predict(
if not quantiles:
self.logger.info(
- f"[{i+1}/{len(self.imputed_variables)}] Predicting for '{variable}'"
+ f"[{i + 1}/{len(self.imputed_variables)}] Predicting for '{variable}'"
)
model = self.models[variable]
@@ -366,9 +358,7 @@ def _predict(
)
# Get properly encoded predictor columns
- encoded_predictors = self._get_encoded_predictors(
- var_predictors
- )
+ encoded_predictors = self._get_encoded_predictors(var_predictors)
self.logger.debug(
f"var_predictors for {variable}: {var_predictors}"
@@ -442,14 +432,11 @@ def _predict(
# Track the dummy columns that were added
if (
self.dummy_processor
- and variable
- in self.dummy_processor.imputed_var_dummy_mapping
+ and variable in self.dummy_processor.imputed_var_dummy_mapping
):
- var_info = (
- self.dummy_processor.imputed_var_dummy_mapping[
- variable
- ]
- )
+ var_info = self.dummy_processor.imputed_var_dummy_mapping[
+ variable
+ ]
if var_info["dummy_cols"]:
imputed_dummy_cols.update(var_info["dummy_cols"])
@@ -488,9 +475,7 @@ def _predict(
except Exception as e:
self.logger.error(f"Error during QRF prediction: {str(e)}")
- raise RuntimeError(
- f"Failed to predict with QRF model: {str(e)}"
- ) from e
+ raise RuntimeError(f"Failed to predict with QRF model: {str(e)}") from e
class QRF(Imputer):
@@ -555,9 +540,7 @@ def _get_encoded_predictors(
dummy_processor = getattr(self, "dummy_processor", None)
if dummy_processor:
- return dummy_processor.get_sequential_predictor_columns(
- current_predictors
- )
+ return dummy_processor.get_sequential_predictor_columns(current_predictors)
else:
return current_predictors
@@ -583,13 +566,8 @@ def _encode_imputed_variable(
if dummy_processor is None:
dummy_processor = getattr(self, "dummy_processor", None)
- if (
- dummy_processor
- and variable in dummy_processor.imputed_var_dummy_mapping
- ):
- data = dummy_processor.sequential_imputed_predictor_encoding(
- data, variable
- )
+ if dummy_processor and variable in dummy_processor.imputed_var_dummy_mapping:
+ data = dummy_processor.sequential_imputed_predictor_encoding(data, variable)
self.logger.debug(
f" Encoded '{variable}' for use in sequential imputation"
)
@@ -603,14 +581,10 @@ def _create_model_for_variable(self, variable: str, **kwargs) -> Any:
if variable in categorical_targets:
# Use classifier for categorical targets
- return _RandomForestClassifierModel(
- seed=self.seed, logger=self.logger
- )
+ return _RandomForestClassifierModel(seed=self.seed, logger=self.logger)
elif variable in boolean_targets:
# Use classifier for boolean targets
- return _RandomForestClassifierModel(
- seed=self.seed, logger=self.logger
- )
+ return _RandomForestClassifierModel(seed=self.seed, logger=self.logger)
else:
# Use QRF for numeric targets
return _QRFModel(seed=self.seed, logger=self.logger)
@@ -722,22 +696,15 @@ def _fit(
)
# Handle batch processing if enabled
- if (
- self.batch_size
- and len(imputed_variables) > self.batch_size
- ):
+ if self.batch_size and len(imputed_variables) > self.batch_size:
self.logger.info(
f"Processing {len(imputed_variables)} variables in batches of {self.batch_size}"
)
variable_batches = [
imputed_variables[i : i + self.batch_size]
- for i in range(
- 0, len(imputed_variables), self.batch_size
- )
+ for i in range(0, len(imputed_variables), self.batch_size)
]
- for batch_idx, batch_variables in enumerate(
- variable_batches
- ):
+ for batch_idx, batch_variables in enumerate(variable_batches):
self.logger.info(
f"Processing batch {batch_idx + 1}/{len(variable_batches)} "
f"({len(batch_variables)} variables)"
@@ -769,9 +736,7 @@ def _fit(
# Handle constant targets
if variable in (constant_targets or {}):
- constant_val = constant_targets[variable][
- "value"
- ]
+ constant_val = constant_targets[variable]["value"]
self.models[variable] = _ConstantValueModel(
constant_val, variable
)
@@ -786,16 +751,14 @@ def _fit(
)
# Get properly encoded predictor columns
- dummy_processor = getattr(
- self, "dummy_processor", None
- )
+ dummy_processor = getattr(self, "dummy_processor", None)
encoded_predictors = self._get_encoded_predictors(
current_predictors, dummy_processor
)
# Log detailed pre-imputation information
self.logger.info(
- f"[{i+1}/{len(imputed_variables)}] Starting imputation for '{variable}'"
+ f"[{i + 1}/{len(imputed_variables)}] Starting imputation for '{variable}'"
)
self.logger.info(
f" Features: {len(encoded_predictors)} predictors"
@@ -815,7 +778,6 @@ def _fit(
)
try:
-
# Log post-imputation information
var_time = time.time() - var_start_time
self.logger.info(
@@ -844,9 +806,7 @@ def _fit(
)
except Exception as e:
- self.logger.error(
- f" ✗ Failed: {variable} - {str(e)}"
- )
+ self.logger.error(f" ✗ Failed: {variable} - {str(e)}")
raise
# Memory cleanup if enabled
@@ -869,18 +829,14 @@ def _fit(
categorical_targets=categorical_targets,
boolean_targets=boolean_targets,
constant_targets=constant_targets,
- dummy_processor=getattr(
- self, "dummy_processor", None
- ),
+ dummy_processor=getattr(self, "dummy_processor", None),
seed=self.seed,
),
qrf_kwargs,
)
except Exception as e:
- self.logger.error(
- f"Error tuning hyperparameters: {str(e)}"
- )
+ self.logger.error(f"Error tuning hyperparameters: {str(e)}")
raise RuntimeError(
f"Failed to tune hyperparameters: {str(e)}"
) from e
@@ -895,22 +851,15 @@ def _fit(
)
# Handle batch processing if enabled
- if (
- self.batch_size
- and len(imputed_variables) > self.batch_size
- ):
+ if self.batch_size and len(imputed_variables) > self.batch_size:
self.logger.info(
f"Processing {len(imputed_variables)} variables in batches of {self.batch_size}"
)
variable_batches = [
imputed_variables[i : i + self.batch_size]
- for i in range(
- 0, len(imputed_variables), self.batch_size
- )
+ for i in range(0, len(imputed_variables), self.batch_size)
]
- for batch_idx, batch_variables in enumerate(
- variable_batches
- ):
+ for batch_idx, batch_variables in enumerate(variable_batches):
self.logger.info(
f"Processing batch {batch_idx + 1}/{len(variable_batches)} "
f"({len(batch_variables)} variables)"
@@ -956,16 +905,14 @@ def _fit(
)
# Get properly encoded predictor columns
- dummy_processor = getattr(
- self, "dummy_processor", None
- )
+ dummy_processor = getattr(self, "dummy_processor", None)
encoded_predictors = self._get_encoded_predictors(
current_predictors, dummy_processor
)
# Log detailed pre-imputation information
self.logger.info(
- f"[{i+1}/{len(imputed_variables)}] Starting imputation for '{variable}'"
+ f"[{i + 1}/{len(imputed_variables)}] Starting imputation for '{variable}'"
)
self.logger.info(
f" Features: {len(encoded_predictors)} predictors"
@@ -1014,9 +961,7 @@ def _fit(
)
except Exception as e:
- self.logger.error(
- f" ✗ Failed: {variable} - {str(e)}"
- )
+ self.logger.error(f" ✗ Failed: {variable} - {str(e)}")
raise
# Memory cleanup if enabled
@@ -1082,9 +1027,7 @@ def _fit_variable_batch(
# Handle constant targets
if variable in (constant_targets or {}):
constant_val = constant_targets[variable]["value"]
- self.models[variable] = _ConstantValueModel(
- constant_val, variable
- )
+ self.models[variable] = _ConstantValueModel(constant_val, variable)
self.logger.info(
f"Using constant value {constant_val} for variable {variable}"
)
@@ -1097,14 +1040,10 @@ def _fit_variable_batch(
# Log detailed pre-imputation information
self.logger.info(
- f"[{i+1}/{len(imputed_variables)}] Starting imputation for '{variable}'"
- )
- self.logger.info(
- f" Features: {len(current_predictors)} predictors"
- )
- self.logger.info(
- f" Memory usage: {self._get_memory_usage_info()}"
+ f"[{i + 1}/{len(imputed_variables)}] Starting imputation for '{variable}'"
)
+ self.logger.info(f" Features: {len(current_predictors)} predictors")
+ self.logger.info(f" Memory usage: {self._get_memory_usage_info()}")
# Create and fit model
# Note: X_train is already preprocessed by base class
@@ -1121,9 +1060,7 @@ def _fit_variable_batch(
# Log post-imputation information
var_time = time.time() - var_start_time
- self.logger.info(
- f" ✓ Success: {variable} fitted in {var_time:.2f}s"
- )
+ self.logger.info(f" ✓ Success: {variable} fitted in {var_time:.2f}s")
# Get model complexity metrics if available
if hasattr(model.qrf, "n_estimators"):
@@ -1181,16 +1118,10 @@ def _tune_qrf_hyperparameters(
def objective(trial: optuna.Trial) -> float:
params = {
"n_estimators": trial.suggest_int("n_estimators", 50, 300),
- "min_samples_split": trial.suggest_int(
- "min_samples_split", 2, 20
- ),
- "min_samples_leaf": trial.suggest_int(
- "min_samples_leaf", 1, 10
- ),
+ "min_samples_split": trial.suggest_int("min_samples_split", 2, 20),
+ "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10),
"max_features": trial.suggest_float("max_features", 0.1, 1.0),
- "bootstrap": trial.suggest_categorical(
- "bootstrap", [True, False]
- ),
+ "bootstrap": trial.suggest_categorical("bootstrap", [True, False]),
}
# Track errors across CV folds
@@ -1235,9 +1166,7 @@ def objective(trial: optuna.Trial) -> float:
)
# Predict
- y_pred = model.predict(
- X_val_augmented[encoded_predictors]
- )
+ y_pred = model.predict(X_val_augmented[encoded_predictors])
# Add predictions to augmented datasets for next variable
X_train_augmented[var] = model.predict(
@@ -1344,18 +1273,12 @@ def _tune_rfc_hyperparameters(
def objective(trial: optuna.Trial) -> float:
params = {
"n_estimators": trial.suggest_int("n_estimators", 50, 300),
- "min_samples_split": trial.suggest_int(
- "min_samples_split", 2, 20
- ),
- "min_samples_leaf": trial.suggest_int(
- "min_samples_leaf", 1, 10
- ),
+ "min_samples_split": trial.suggest_int("min_samples_split", 2, 20),
+ "min_samples_leaf": trial.suggest_int("min_samples_leaf", 1, 10),
"max_features": trial.suggest_categorical(
"max_features", ["sqrt", "log2", 0.5, 0.8, 1.0]
),
- "bootstrap": trial.suggest_categorical(
- "bootstrap", [True, False]
- ),
+ "bootstrap": trial.suggest_categorical("bootstrap", [True, False]),
}
# Track errors across CV folds
@@ -1402,9 +1325,7 @@ def objective(trial: optuna.Trial) -> float:
X_train_augmented[encoded_predictors],
X_train_fold[var],
var_type=categorical_targets[var]["type"],
- categories=categorical_targets[var].get(
- "categories"
- ),
+ categories=categorical_targets[var].get("categories"),
**params,
)
elif var in boolean_targets:
@@ -1528,9 +1449,7 @@ def _tune_hyperparameters(
for var in imputed_variables
if var in categorical_targets or var in boolean_targets
]
- numeric_vars = [
- var for var in imputed_variables if var not in categorical_vars
- ]
+ numeric_vars = [var for var in imputed_variables if var not in categorical_vars]
# Default: 3-fold CV with 10 trials (same computational cost as old 30 trials)
n_cv_folds = 3
@@ -1545,9 +1464,7 @@ def _tune_hyperparameters(
# Tune appropriate models based on variable types
if not categorical_vars:
# Backward compatible: only numeric variables
- self.logger.info(
- "Tuning QRF hyperparameters (numeric variables only)"
- )
+ self.logger.info("Tuning QRF hyperparameters (numeric variables only)")
return self._tune_qrf_hyperparameters(
data, predictors, numeric_vars, n_cv_folds, n_trials
)
diff --git a/microimpute/models/quantreg.py b/microimpute/models/quantreg.py
index 2f2c686..a808303 100644
--- a/microimpute/models/quantreg.py
+++ b/microimpute/models/quantreg.py
@@ -149,9 +149,7 @@ def _predict(
else:
quantiles = list(self.models[self.imputed_variables[0]].keys())
if random_quantile_sample:
- self.logger.info(
- "Sampling random quantiles for each prediction"
- )
+ self.logger.info("Sampling random quantiles for each prediction")
mean_quantile = np.mean(quantiles)
# Get predictions for all quantiles first
@@ -167,9 +165,7 @@ def _predict(
# Check if this is a constant target
if variable in self.constant_targets:
# Get the constant model from any quantile
- available_q = list(
- self.models[variable].keys()
- )[0]
+ available_q = list(self.models[variable].keys())[0]
model = self.models[variable][available_q]
predictions = model.predict(X_test)
else:
@@ -178,9 +174,7 @@ def _predict(
# Constant model - just return the constant value
predictions = model.predict(X_test)
else:
- predictions = model.predict(
- X_test_with_const
- )
+ predictions = model.predict(X_test_with_const)
# Convert to boolean if this was a boolean target
if variable in self.boolean_targets:
predictions = predictions > 0.5
@@ -200,11 +194,9 @@ def _predict(
# For all variables, use the sampled quantile for this row
for variable in self.imputed_variables:
- result_df.loc[idx, variable] = (
- random_q_imputations[sampled_q].loc[
- idx, variable
- ]
- )
+ result_df.loc[idx, variable] = random_q_imputations[
+ sampled_q
+ ].loc[idx, variable]
# Add to imputations dictionary using the mean quantile as key
imputations[mean_quantile] = result_df
@@ -225,9 +217,7 @@ def _predict(
# Check if this is a constant target
if variable in self.constant_targets:
# Get the constant model from any quantile
- available_q = list(
- self.models[variable].keys()
- )[0]
+ available_q = list(self.models[variable].keys())[0]
model = self.models[variable][available_q]
predictions = model.predict(X_test)
else:
@@ -236,18 +226,14 @@ def _predict(
# Constant model - just return the constant value
predictions = model.predict(X_test)
else:
- predictions = model.predict(
- X_test_with_const
- )
+ predictions = model.predict(X_test_with_const)
# Convert to boolean if this was a boolean target
if variable in self.boolean_targets:
predictions = predictions > 0.5
imputed_df[variable] = predictions
imputations[q] = imputed_df
- self.logger.info(
- f"Completed predictions for {len(imputations)} quantiles"
- )
+ self.logger.info(f"Completed predictions for {len(imputations)} quantiles")
# Return behavior based on how the model was fitted:
# - If quantiles were explicitly specified during fit OR predict, return dict
@@ -339,7 +325,9 @@ def _fit(
if quantiles:
invalid_quantiles = [q for q in quantiles if not 0 <= q <= 1]
if invalid_quantiles:
- error_msg = f"Quantiles must be between 0 and 1, got: {invalid_quantiles}"
+ error_msg = (
+ f"Quantiles must be between 0 and 1, got: {invalid_quantiles}"
+ )
self.logger.error(error_msg)
raise ValueError(error_msg)
self.logger.info(
@@ -373,9 +361,7 @@ def _fit(
# Convert boolean to numeric for regression
if variable in (boolean_targets or {}):
Y = Y.astype(float)
- self.models[variable][q] = sm.QuantReg(
- Y, X_with_const
- ).fit(q=q)
+ self.models[variable][q] = sm.QuantReg(Y, X_with_const).fit(q=q)
self.logger.info(f"Model for q={q} fitted successfully")
else:
random_generator = np.random.default_rng(self.seed)
@@ -400,9 +386,7 @@ def _fit(
# Convert boolean to numeric for regression
if variable in (boolean_targets or {}):
Y = Y.astype(float)
- self.models[variable][q] = sm.QuantReg(
- Y, X_with_const
- ).fit(q=q)
+ self.models[variable][q] = sm.QuantReg(Y, X_with_const).fit(q=q)
self.logger.info(f"Model for q={q:.4f} fitted successfully")
self.logger.info(f"QuantReg has {len(self.models)} fitted models")
@@ -421,6 +405,4 @@ def _fit(
)
except Exception as e:
self.logger.error(f"Error fitting QuantReg model: {str(e)}")
- raise RuntimeError(
- f"Failed to fit QuantReg model: {str(e)}"
- ) from e
+ raise RuntimeError(f"Failed to fit QuantReg model: {str(e)}") from e
diff --git a/microimpute/utils/dashboard_formatter.py b/microimpute/utils/dashboard_formatter.py
index 5d56309..d39291f 100644
--- a/microimpute/utils/dashboard_formatter.py
+++ b/microimpute/utils/dashboard_formatter.py
@@ -43,9 +43,7 @@ def _compute_histogram_data(
# Compute histogram heights (normalized as densities)
donor_heights, _ = np.histogram(donor_clean, bins=bin_edges, density=True)
- receiver_heights, _ = np.histogram(
- receiver_clean, bins=bin_edges, density=True
- )
+ receiver_heights, _ = np.histogram(receiver_clean, bins=bin_edges, density=True)
# Convert to percentages for easier interpretation
# Multiply by bin width to get probability mass per bin
@@ -99,14 +97,10 @@ def _compute_categorical_distribution(
if len(all_categories) > max_categories:
# Get top categories by combined frequency
combined_counts = donor_counts.add(receiver_counts, fill_value=0)
- top_categories = combined_counts.nlargest(
- max_categories - 1
- ).index.tolist()
+ top_categories = combined_counts.nlargest(max_categories - 1).index.tolist()
# Calculate "Other" category
- donor_other = donor_counts[
- ~donor_counts.index.isin(top_categories)
- ].sum()
+ donor_other = donor_counts[~donor_counts.index.isin(top_categories)].sum()
receiver_other = receiver_counts[
~receiver_counts.index.isin(top_categories)
].sum()
@@ -116,13 +110,9 @@ def _compute_categorical_distribution(
# Get proportions
donor_props = [donor_counts.get(cat, 0) for cat in top_categories]
donor_props.append(donor_other)
- donor_props = (
- pd.Series(donor_props) / donor_values.count() * 100
- ).tolist()
+ donor_props = (pd.Series(donor_props) / donor_values.count() * 100).tolist()
- receiver_props = [
- receiver_counts.get(cat, 0) for cat in top_categories
- ]
+ receiver_props = [receiver_counts.get(cat, 0) for cat in top_categories]
receiver_props.append(receiver_other)
receiver_props = (
pd.Series(receiver_props) / receiver_values.count() * 100
@@ -190,9 +180,7 @@ def _format_histogram_rows(
"receiver_proportion": float(
histogram_data["receiver_proportions"][i]
),
- "n_samples_donor": int(
- histogram_data["n_samples_donor"]
- ),
+ "n_samples_donor": int(histogram_data["n_samples_donor"]),
"n_samples_receiver": int(
histogram_data["n_samples_receiver"]
),
@@ -218,18 +206,12 @@ def _format_histogram_rows(
{
"bin_index": int(i),
"bin_start": float(histogram_data["bin_edges"][i]),
- "bin_end": float(
- histogram_data["bin_edges"][i + 1]
- ),
- "donor_height": float(
- histogram_data["donor_heights"][i]
- ),
+ "bin_end": float(histogram_data["bin_edges"][i + 1]),
+ "donor_height": float(histogram_data["donor_heights"][i]),
"receiver_height": float(
histogram_data["receiver_heights"][i]
),
- "n_samples_donor": int(
- histogram_data["n_samples_donor"]
- ),
+ "n_samples_donor": int(histogram_data["n_samples_donor"]),
"n_samples_receiver": int(
histogram_data["n_samples_receiver"]
),
@@ -368,9 +350,7 @@ def format_csv(
for method, cv_result in autoimpute_result.items():
# Append "_best_method" if this is the best method
method_label = (
- f"{method}_best_method"
- if method == best_method_name
- else method
+ f"{method}_best_method" if method == best_method_name else method
)
for metric_type in ["quantile_loss", "log_loss"]:
@@ -392,9 +372,7 @@ def format_csv(
and split in results_std_df.index
):
std_value = float(
- results_std_df.loc[
- split, quantile
- ]
+ results_std_df.loc[split, quantile]
)
rows.append(
@@ -410,11 +388,7 @@ def format_csv(
"metric_std": std_value,
"split": split,
"additional_info": json.dumps(
- {
- "n_variables": len(
- variables
- )
- }
+ {"n_variables": len(variables)}
),
}
)
@@ -444,9 +418,7 @@ def format_csv(
if "mean_test" in data:
std_test = (
- float(data["std_test"])
- if "std_test" in data
- else None
+ float(data["std_test"]) if "std_test" in data else None
)
rows.append(
{
@@ -469,9 +441,7 @@ def format_csv(
for _, row in comparison_metrics_df.iterrows():
method = row["Method"]
method_label = (
- f"{method}_best_method"
- if method == best_method_name
- else method
+ f"{method}_best_method" if method == best_method_name else method
)
# Handle variable naming - check if it's an aggregate
@@ -510,10 +480,7 @@ def format_csv(
)
# 3. Process distribution comparison metrics
- if (
- distribution_comparison_df is not None
- and not distribution_comparison_df.empty
- ):
+ if distribution_comparison_df is not None and not distribution_comparison_df.empty:
for _, row in distribution_comparison_df.iterrows():
rows.append(
{
@@ -552,9 +519,7 @@ def format_csv(
"metric_value": corr_matrix.iloc[i, j],
"metric_std": None,
"split": "full",
- "additional_info": json.dumps(
- {"predictor2": pred2}
- ),
+ "additional_info": json.dumps({"predictor2": pred2}),
}
)
@@ -578,10 +543,7 @@ def format_csv(
)
# 5. Process predictor importance (leave-one-out)
- if (
- predictor_importance_df is not None
- and not predictor_importance_df.empty
- ):
+ if predictor_importance_df is not None and not predictor_importance_df.empty:
for _, row in predictor_importance_df.iterrows():
predictor = row["predictor_removed"]
@@ -596,9 +558,7 @@ def format_csv(
"metric_value": row["relative_impact"],
"metric_std": None,
"split": "test",
- "additional_info": json.dumps(
- {"removed_predictor": predictor}
- ),
+ "additional_info": json.dumps({"removed_predictor": predictor}),
}
)
@@ -613,17 +573,12 @@ def format_csv(
"metric_value": row["loss_increase"],
"metric_std": None,
"split": "test",
- "additional_info": json.dumps(
- {"removed_predictor": predictor}
- ),
+ "additional_info": json.dumps({"removed_predictor": predictor}),
}
)
# 6. Process progressive predictor inclusion
- if (
- progressive_inclusion_df is not None
- and not progressive_inclusion_df.empty
- ):
+ if progressive_inclusion_df is not None and not progressive_inclusion_df.empty:
for _, row in progressive_inclusion_df.iterrows():
step = row["step"]
predictor_added = row["predictor_added"]
@@ -636,9 +591,7 @@ def format_csv(
rows.append(
{
"type": "progressive_inclusion",
- "method": (
- best_method_name if best_method_name else "N/A"
- ),
+ "method": (best_method_name if best_method_name else "N/A"),
"variable": "N/A",
"quantile": "N/A",
"metric_name": "cumulative_improvement",
@@ -660,15 +613,11 @@ def format_csv(
)
# Add marginal improvement
- if "marginal_improvement" in row and pd.notna(
- row["marginal_improvement"]
- ):
+ if "marginal_improvement" in row and pd.notna(row["marginal_improvement"]):
rows.append(
{
"type": "progressive_inclusion",
- "method": (
- best_method_name if best_method_name else "N/A"
- ),
+ "method": (best_method_name if best_method_name else "N/A"),
"variable": "N/A",
"quantile": "N/A",
"metric_name": "marginal_improvement",
@@ -693,9 +642,7 @@ def format_csv(
)
# Validate that all imputed variables exist in both datasets
- _validate_imputed_variables(
- donor_data, receiver_data, imputed_variables
- )
+ _validate_imputed_variables(donor_data, receiver_data, imputed_variables)
# Generate histogram data for each imputed variable
for var in imputed_variables:
diff --git a/microimpute/utils/data.py b/microimpute/utils/data.py
index f0c404a..db2752c 100644
--- a/microimpute/utils/data.py
+++ b/microimpute/utils/data.py
@@ -86,9 +86,7 @@ def normalize_data(
# Only normalize specified columns that are not categorical
numeric_cols = [
- col
- for col in columns_to_normalize
- if col not in categorical_cols
+ col for col in columns_to_normalize if col not in categorical_cols
]
# Warn if user specified categorical columns
@@ -102,9 +100,7 @@ def normalize_data(
)
else:
# Get all numeric columns for normalization
- numeric_cols = [
- col for col in data.columns if col not in categorical_cols
- ]
+ numeric_cols = [col for col in data.columns if col not in categorical_cols]
if not numeric_cols:
logger.warning("No numeric columns found for normalization")
@@ -125,9 +121,7 @@ def normalize_data(
# Apply normalization only to numeric columns
data[numeric_cols] = (data[numeric_cols] - mean) / std
- logger.debug(
- f"Normalized {len(numeric_cols)} numeric columns successfully"
- )
+ logger.debug(f"Normalized {len(numeric_cols)} numeric columns successfully")
# Store normalization parameters only for numeric columns
normalization_params = {
@@ -199,9 +193,7 @@ def log_transform_data(
# Only transform specified columns that are not categorical
numeric_cols = [
- col
- for col in columns_to_transform
- if col not in categorical_cols
+ col for col in columns_to_transform if col not in categorical_cols
]
# Warn if user specified categorical columns
@@ -215,9 +207,7 @@ def log_transform_data(
)
else:
# Get all numeric columns for log transformation
- numeric_cols = [
- col for col in data.columns if col not in categorical_cols
- ]
+ numeric_cols = [col for col in data.columns if col not in categorical_cols]
if not numeric_cols:
logger.warning("No numeric columns found for log transformation")
@@ -316,9 +306,7 @@ def asinh_transform_data(
# Only transform specified columns that are not categorical
numeric_cols = [
- col
- for col in columns_to_transform
- if col not in categorical_cols
+ col for col in columns_to_transform if col not in categorical_cols
]
# Warn if user specified categorical columns
@@ -332,9 +320,7 @@ def asinh_transform_data(
)
else:
# Get all numeric columns for asinh transformation
- numeric_cols = [
- col for col in data.columns if col not in categorical_cols
- ]
+ numeric_cols = [col for col in data.columns if col not in categorical_cols]
if not numeric_cols:
logger.warning("No numeric columns found for asinh transformation")
@@ -350,9 +336,7 @@ def asinh_transform_data(
logger.debug(
f"Asinh transformed {len(numeric_cols)} numeric columns successfully"
)
- logger.debug(
- f"Asinh transformation parameters: {asinh_transform_params}"
- )
+ logger.debug(f"Asinh transformation parameters: {asinh_transform_params}")
return data_copy, asinh_transform_params
@@ -425,21 +409,15 @@ def preprocess_data(
RuntimeError: If data preprocessing fails
"""
- logger.debug(
- f"Preprocessing data with shape {data.shape}, full_data={full_data}"
- )
+ logger.debug(f"Preprocessing data with shape {data.shape}, full_data={full_data}")
if data.empty:
raise ValueError("Data must not be None or empty")
# Check which transformations are requested
normalize_requested = normalize is not False and normalize != []
- log_transform_requested = (
- log_transform is not False and log_transform != []
- )
- asinh_transform_requested = (
- asinh_transform is not False and asinh_transform != []
- )
+ log_transform_requested = log_transform is not False and log_transform != []
+ asinh_transform_requested = asinh_transform is not False and asinh_transform != []
# Collect transformation settings for conflict checking
transforms = []
@@ -526,9 +504,7 @@ def preprocess_data(
# Prepare transformation parameters to return
has_transformations = (
- normalize_requested
- or log_transform_requested
- or asinh_transform_requested
+ normalize_requested or log_transform_requested or asinh_transform_requested
)
if has_transformations:
# Merge parameter dicts, with a key to distinguish them
@@ -540,9 +516,7 @@ def preprocess_data(
if full_data:
if has_transformations:
- logger.info(
- "Returning full preprocessed dataset with transformations"
- )
+ logger.info("Returning full preprocessed dataset with transformations")
return (data, transform_params)
else:
logger.info("Returning full preprocessed dataset")
@@ -572,9 +546,7 @@ def preprocess_data(
@validate_call(config=VALIDATE_CONFIG)
-def unnormalize_predictions(
- imputations: dict, normalization_params: dict
-) -> dict:
+def unnormalize_predictions(imputations: dict, normalization_params: dict) -> dict:
"""Unnormalize predictions using stored normalization parameters.
Args:
@@ -590,9 +562,7 @@ def unnormalize_predictions(
logger.debug(f"Unnormalizing predictions for {len(imputations)} quantiles")
# Extract mean and std from normalization parameters
- mean = pd.Series(
- {col: p["mean"] for col, p in normalization_params.items()}
- )
+ mean = pd.Series({col: p["mean"] for col, p in normalization_params.items()})
std = pd.Series({col: p["std"] for col, p in normalization_params.items()})
unnormalized = {}
@@ -602,7 +572,9 @@ def unnormalize_predictions(
# Check that all columns have normalization parameters
missing_params = [col for col in cols if col not in mean.index]
if missing_params:
- error_msg = f"Missing normalization parameters for columns: {missing_params}"
+ error_msg = (
+ f"Missing normalization parameters for columns: {missing_params}"
+ )
logger.error(error_msg)
raise ValueError(error_msg)
@@ -616,9 +588,7 @@ def unnormalize_predictions(
@validate_call(config=VALIDATE_CONFIG)
-def unlog_transform_predictions(
- imputations: dict, log_transform_params: dict
-) -> dict:
+def unlog_transform_predictions(imputations: dict, log_transform_params: dict) -> dict:
"""Reverse log transformation on predictions using stored parameters.
Args:
@@ -634,22 +604,17 @@ def unlog_transform_predictions(
ValueError: If columns in imputations don't match log transformation
parameters.
"""
- logger.debug(
- f"Reversing log transformation for {len(imputations)} quantiles"
- )
+ logger.debug(f"Reversing log transformation for {len(imputations)} quantiles")
untransformed = {}
for q, df in imputations.items():
cols = df.columns
# Check that all columns have log transformation parameters
- missing_params = [
- col for col in cols if col not in log_transform_params
- ]
+ missing_params = [col for col in cols if col not in log_transform_params]
if missing_params:
error_msg = (
- f"Missing log transformation parameters for columns: "
- f"{missing_params}"
+ f"Missing log transformation parameters for columns: {missing_params}"
)
logger.error(error_msg)
raise ValueError(error_msg)
@@ -687,22 +652,17 @@ def un_asinh_transform_predictions(
ValueError: If columns in imputations don't match asinh transformation
parameters.
"""
- logger.debug(
- f"Reversing asinh transformation for {len(imputations)} quantiles"
- )
+ logger.debug(f"Reversing asinh transformation for {len(imputations)} quantiles")
untransformed = {}
for q, df in imputations.items():
cols = df.columns
# Check that all columns have asinh transformation parameters
- missing_params = [
- col for col in cols if col not in asinh_transform_params
- ]
+ missing_params = [col for col in cols if col not in asinh_transform_params]
if missing_params:
error_msg = (
- f"Missing asinh transformation parameters for columns: "
- f"{missing_params}"
+ f"Missing asinh transformation parameters for columns: {missing_params}"
)
logger.error(error_msg)
raise ValueError(error_msg)
diff --git a/microimpute/utils/statmatch_hotdeck.py b/microimpute/utils/statmatch_hotdeck.py
index 6e8712c..2d87742 100644
--- a/microimpute/utils/statmatch_hotdeck.py
+++ b/microimpute/utils/statmatch_hotdeck.py
@@ -83,9 +83,7 @@ def nnd_hotdeck_using_rpy2(
missing_in_receiver = [
v for v in matching_variables if v not in receiver.columns
]
- missing_in_donor = [
- v for v in matching_variables if v not in donor.columns
- ]
+ missing_in_donor = [v for v in matching_variables if v not in donor.columns]
if missing_in_receiver:
msg = f"Matching variables missing in receiver: {missing_in_receiver}"
log.error(msg)
@@ -134,12 +132,8 @@ def nnd_hotdeck_using_rpy2(
mtc_ids = mtc_ids_r
else:
mtc_array = np.array(mtc_ids_r)
- log.debug(
- f"mtc_array shape: {mtc_array.shape}, dtype: {mtc_array.dtype}"
- )
- log.debug(
- f"Receiver length: {len(receiver)}, Donor length: {len(donor)}"
- )
+ log.debug(f"mtc_array shape: {mtc_array.shape}, dtype: {mtc_array.dtype}")
+ log.debug(f"Receiver length: {len(receiver)}, Donor length: {len(donor)}")
# If we have a 1D array with strings, convert to integers
if mtc_array.dtype.kind in ["U", "S"]:
@@ -148,17 +142,13 @@ def nnd_hotdeck_using_rpy2(
# Check if mtc_array is empty or has unexpected shape
if mtc_array.size == 0:
log.error("mtc_array is empty!")
- raise ValueError(
- "No matching indices returned from NND_hotdeck"
- )
+ raise ValueError("No matching indices returned from NND_hotdeck")
# If the mtc.ids array has 2 values per recipient (recipient_idx, donor_idx pairs)
if len(mtc_array) == 2 * len(receiver):
donor_indices = mtc_array.reshape(-1, 2)[:, 1]
# Make sure these indices are within the valid range (1 to donor dataset size)
- donor_indices_valid = (
- np.remainder(donor_indices - 1, len(donor)) + 1
- )
+ donor_indices_valid = np.remainder(donor_indices - 1, len(donor)) + 1
else:
if len(mtc_array) >= len(receiver):
# Use the indices directly (up to the length of receiver)
@@ -176,15 +166,11 @@ def nnd_hotdeck_using_rpy2(
donor_indices_valid = np.concatenate(
[
mtc_array,
- np.repeat(
- fill_value, len(receiver) - len(mtc_array)
- ),
+ np.repeat(fill_value, len(receiver) - len(mtc_array)),
]
)
# Create the final mtc.ids matrix required by create_fused
- mtc_matrix = np.column_stack(
- (recipient_indices, donor_indices_valid)
- )
+ mtc_matrix = np.column_stack((recipient_indices, donor_indices_valid))
# Convert to R matrix
mtc_ids = ro.r.matrix(
ro.IntVector(mtc_matrix.flatten()),
@@ -219,14 +205,10 @@ def nnd_hotdeck_using_rpy2(
raise
except IndexError as e:
log.error(f"Index error in statistical matching: {e}")
- log.error(
- f"Receiver shape: {receiver.shape}, Donor shape: {donor.shape}"
- )
+ log.error(f"Receiver shape: {receiver.shape}, Donor shape: {donor.shape}")
log.error(f"Matching variables: {matching_variables}")
log.error(f"Z variables: {z_variables}")
- raise RuntimeError(
- f"Statistical matching failed with index error: {e}"
- ) from e
+ raise RuntimeError(f"Statistical matching failed with index error: {e}") from e
except Exception as e:
log.error(f"Unexpected error in statistical matching: {e}")
log.error(f"Error type: {type(e).__name__}")
diff --git a/microimpute/utils/type_handling.py b/microimpute/utils/type_handling.py
index 243b597..de4bbac 100644
--- a/microimpute/utils/type_handling.py
+++ b/microimpute/utils/type_handling.py
@@ -32,9 +32,9 @@ def is_boolean_variable(series: pd.Series) -> bool:
@staticmethod
def is_categorical_variable(series: pd.Series) -> bool:
"""Check if a series represents categorical string/object data."""
- return pd.api.types.is_string_dtype(
+ return pd.api.types.is_string_dtype(series) or pd.api.types.is_object_dtype(
series
- ) or pd.api.types.is_object_dtype(series)
+ )
@staticmethod
def is_numeric_categorical_variable(
@@ -84,9 +84,8 @@ def categorize_variable(
return "categorical", series.unique().tolist()
# Check if it would normally be numeric_categorical
- if (
- not force_numeric
- and VariableTypeDetector.is_numeric_categorical_variable(series)
+ if not force_numeric and VariableTypeDetector.is_numeric_categorical_variable(
+ series
):
categories = [float(i) for i in series.unique().tolist()]
logger.info(
@@ -95,9 +94,8 @@ def categorize_variable(
return "numeric_categorical", categories
# If force_numeric is True or it's not numeric_categorical, treat as numeric
- if (
- force_numeric
- and VariableTypeDetector.is_numeric_categorical_variable(series)
+ if force_numeric and VariableTypeDetector.is_numeric_categorical_variable(
+ series
):
logger.info(
f"Variable '{col_name}' forced to be treated as numeric (override numeric_categorical detection)"
@@ -112,9 +110,7 @@ class DummyVariableProcessor:
def __init__(self, logger: logging.Logger):
self.logger = logger
self.dummy_mapping = {} # Maps original column to dummy columns
- self.imputed_var_dummy_mapping = (
- {}
- ) # Pre-computed dummy info for imputed vars
+ self.imputed_var_dummy_mapping = {} # Pre-computed dummy info for imputed vars
def preprocess_predictors(
self,
@@ -181,9 +177,7 @@ def preprocess_predictors(
dtype="float64",
drop_first=True,
)
- dummy_cols = [
- c for c in dummy_df.columns if c.startswith(f"{col}_")
- ]
+ dummy_cols = [c for c in dummy_df.columns if c.startswith(f"{col}_")]
# Store pre-computed dummy info
self.imputed_var_dummy_mapping[col] = {
@@ -217,9 +211,7 @@ def preprocess_predictors(
# Track mapping for each original column
for orig_col in categorical_predictors:
dummy_cols = [
- col
- for col in dummy_df.columns
- if col.startswith(f"{orig_col}_")
+ col for col in dummy_df.columns if col.startswith(f"{orig_col}_")
]
self.dummy_mapping[orig_col] = dummy_cols
@@ -238,14 +230,10 @@ def preprocess_predictors(
# Convert boolean predictors to float (but keep as single column)
for col in predictors:
if col in data.columns:
- var_type, _ = detector.categorize_variable(
- data[col], col, self.logger
- )
+ var_type, _ = detector.categorize_variable(data[col], col, self.logger)
if var_type == "bool":
data[col] = data[col].astype("float64")
- self.logger.debug(
- f"Converted boolean predictor '{col}' to float64"
- )
+ self.logger.debug(f"Converted boolean predictor '{col}' to float64")
return data, updated_predictors
@@ -311,9 +299,7 @@ def sequential_imputed_predictor_encoding(
return data
- def get_sequential_predictor_columns(
- self, variables: List[str]
- ) -> List[str]:
+ def get_sequential_predictor_columns(self, variables: List[str]) -> List[str]:
"""
Get correct column names for sequential predictors.
@@ -386,9 +372,7 @@ def apply_dummy_encoding_to_test(
# Convert boolean predictors to float
for col in predictors:
if col in data.columns:
- var_type, _ = detector.categorize_variable(
- data[col], col, self.logger
- )
+ var_type, _ = detector.categorize_variable(data[col], col, self.logger)
if var_type == "bool":
data[col] = data[col].astype("float64")
diff --git a/microimpute/visualizations/comparison_plots.py b/microimpute/visualizations/comparison_plots.py
index 53248cf..fda9fc8 100644
--- a/microimpute/visualizations/comparison_plots.py
+++ b/microimpute/visualizations/comparison_plots.py
@@ -79,9 +79,7 @@ def __init__(
"Loss",
]
missing_cols = [
- col
- for col in required_cols
- if col not in self.comparison_data.columns
+ col for col in required_cols if col not in self.comparison_data.columns
]
if missing_cols:
error_msg = f"Missing required columns: {missing_cols}"
@@ -91,9 +89,7 @@ def __init__(
# Get unique methods and variables
if hasattr(self, "comparison_data"):
self.methods = self.comparison_data["Method"].unique().tolist()
- self.variables = (
- self.comparison_data["Imputed Variable"].unique().tolist()
- )
+ self.variables = self.comparison_data["Imputed Variable"].unique().tolist()
else:
# For dual metrics format
self.methods = list(self.dual_metrics_data.keys())
@@ -104,9 +100,7 @@ def __init__(
method_data["quantile_loss"].get("variables", [])
)
if "log_loss" in method_data:
- self.variables.extend(
- method_data["log_loss"].get("variables", [])
- )
+ self.variables.extend(method_data["log_loss"].get("variables", []))
self.variables = list(set(self.variables))
logger.debug(
@@ -148,9 +142,7 @@ def _process_wide_input(self, wide_data: pd.DataFrame):
# Regular quantile columns
# Use first imputed variable if specified, otherwise "y"
var_name = (
- self.imputed_variables[0]
- if self.imputed_variables
- else "y"
+ self.imputed_variables[0] if self.imputed_variables else "y"
)
long_format_data.append(
{
@@ -163,9 +155,7 @@ def _process_wide_input(self, wide_data: pd.DataFrame):
self.comparison_data = pd.DataFrame(long_format_data)
- def _process_dual_metrics_input(
- self, dual_data: Dict[str, Dict[str, Dict]]
- ):
+ def _process_dual_metrics_input(self, dual_data: Dict[str, Dict[str, Dict]]):
"""Process dual metrics format from cross-validation results.
Args:
@@ -185,10 +175,7 @@ def _process_dual_metrics_input(
and "quantile_loss" in method_results
):
ql_data = method_results["quantile_loss"]
- if (
- ql_data.get("results") is not None
- and not ql_data["results"].empty
- ):
+ if ql_data.get("results") is not None and not ql_data["results"].empty:
# Get test results (single row)
if "test" in ql_data["results"].index:
test_results = ql_data["results"].loc["test"]
@@ -204,9 +191,7 @@ def _process_dual_metrics_input(
# across variables, so create ONE row per
# (method, quantile) to avoid duplicate bars.
variables = ql_data.get("variables", ["y"])
- var_label = (
- variables[0] if len(variables) == 1 else "average"
- )
+ var_label = variables[0] if len(variables) == 1 else "average"
for quantile in test_results.index:
long_format_data.append(
@@ -238,15 +223,9 @@ def _process_dual_metrics_input(
)
# Process log loss if available
- if (
- self.metric in ["log_loss", "combined"]
- and "log_loss" in method_results
- ):
+ if self.metric in ["log_loss", "combined"] and "log_loss" in method_results:
ll_data = method_results["log_loss"]
- if (
- ll_data.get("results") is not None
- and not ll_data["results"].empty
- ):
+ if ll_data.get("results") is not None and not ll_data["results"].empty:
# Log loss is constant across quantiles.
# Same as quantile_loss: one row per method,
# not per variable, since values are aggregated.
@@ -255,9 +234,7 @@ def _process_dual_metrics_input(
test_std = ll_data.get("std_test", np.nan)
ll_variables = ll_data.get("variables", [])
ll_var_label = (
- ll_variables[0]
- if len(ll_variables) == 1
- else "average"
+ ll_variables[0] if len(ll_variables) == 1 else "average"
)
long_format_data.append(
{
@@ -356,9 +333,7 @@ def _plot_quantile_loss_comparison(
quantile_data = quantile_data[numeric_mask]
# Convert to numeric and filter to valid quantile range
- quantile_data["Percentile"] = pd.to_numeric(
- quantile_data["Percentile"]
- )
+ quantile_data["Percentile"] = pd.to_numeric(quantile_data["Percentile"])
melted_df = quantile_data[
(quantile_data["Percentile"] >= 0)
& (quantile_data["Percentile"] <= 1)
@@ -374,12 +349,9 @@ def _plot_quantile_loss_comparison(
melted_df = melted_df[numeric_mask]
# Convert to numeric and filter to valid quantile range
- melted_df["Percentile"] = pd.to_numeric(
- melted_df["Percentile"]
- )
+ melted_df["Percentile"] = pd.to_numeric(melted_df["Percentile"])
melted_df = melted_df[
- (melted_df["Percentile"] >= 0)
- & (melted_df["Percentile"] <= 1)
+ (melted_df["Percentile"] >= 0) & (melted_df["Percentile"] <= 1)
].copy()
melted_df = melted_df.rename(columns={"Loss": self.metric_name})
@@ -587,9 +559,7 @@ def _plot_combined_metrics(
# Convert to numeric and filter to valid quantile range
ql_df["Percentile"] = pd.to_numeric(ql_df["Percentile"])
- ql_df = ql_df[
- (ql_df["Percentile"] >= 0) & (ql_df["Percentile"] <= 1)
- ]
+ ql_df = ql_df[(ql_df["Percentile"] >= 0) & (ql_df["Percentile"] <= 1)]
else:
# Backward compatibility
ql_df = self.comparison_data.copy()
@@ -602,9 +572,7 @@ def _plot_combined_metrics(
# Convert to numeric and filter to valid quantile range
ql_df["Percentile"] = pd.to_numeric(ql_df["Percentile"])
- ql_df = ql_df[
- (ql_df["Percentile"] >= 0) & (ql_df["Percentile"] <= 1)
- ]
+ ql_df = ql_df[(ql_df["Percentile"] >= 0) & (ql_df["Percentile"] <= 1)]
if not ql_df.empty:
for i, method in enumerate(self.methods):
@@ -742,13 +710,9 @@ def _plot_stacked_contribution(
# Get losses for each method for this variable
method_losses = {}
for method in self.methods:
- method_var_data = var_data[
- var_data["Method"] == method
- ]
+ method_var_data = var_data[var_data["Method"] == method]
if not method_var_data.empty:
- method_losses[method] = method_var_data[
- "Loss"
- ].mean()
+ method_losses[method] = method_var_data["Loss"].mean()
else:
method_losses[method] = np.inf
@@ -773,18 +737,14 @@ def _plot_stacked_contribution(
"Method": method,
"Variable": var,
"Rank": (
- ranks[method]
- if method in ranks
- else len(self.methods)
+ ranks[method] if method in ranks else len(self.methods)
),
"Metric": metric_type,
}
)
if not contribution_data:
- logger.warning(
- "No data available for stacked contribution plot"
- )
+ logger.warning("No data available for stacked contribution plot")
return go.Figure()
contrib_df = pd.DataFrame(contribution_data)
@@ -907,13 +867,9 @@ def get_best_method(self, criterion: str = "mean") -> str:
logger.debug(f"Finding best method using {criterion} criterion")
if criterion == "mean":
- method_scores = self.comparison_data.groupby("Method")[
- "Loss"
- ].mean()
+ method_scores = self.comparison_data.groupby("Method")["Loss"].mean()
elif criterion == "median":
- method_scores = self.comparison_data.groupby("Method")[
- "Loss"
- ].median()
+ method_scores = self.comparison_data.groupby("Method")["Loss"].median()
else:
raise ValueError(f"Unknown criterion: {criterion}")
diff --git a/microimpute/visualizations/performance_plots.py b/microimpute/visualizations/performance_plots.py
index 6c88f70..12823d3 100644
--- a/microimpute/visualizations/performance_plots.py
+++ b/microimpute/visualizations/performance_plots.py
@@ -125,17 +125,11 @@ def plot(
logger.debug(f"Creating performance plot for metric: {self.metric}")
if self.metric == "quantile_loss":
- return self._plot_quantile_loss(
- title, save_path, figsize, show_error_bars
- )
+ return self._plot_quantile_loss(title, save_path, figsize, show_error_bars)
elif self.metric == "log_loss":
- return self._plot_log_loss(
- title, save_path, figsize, show_error_bars
- )
+ return self._plot_log_loss(title, save_path, figsize, show_error_bars)
elif self.metric == "combined":
- return self._plot_combined(
- title, save_path, figsize, show_error_bars
- )
+ return self._plot_combined(title, save_path, figsize, show_error_bars)
else:
raise ValueError(f"Invalid metric: {self.metric}")
@@ -167,14 +161,8 @@ def _plot_quantile_loss(
# Add bars for training data
if "train" in ql_data.index:
error_y_dict = None
- if (
- show_error_bars
- and ql_std is not None
- and "train" in ql_std.index
- ):
- error_y_dict = dict(
- type="data", array=ql_std.loc["train"].values
- )
+ if show_error_bars and ql_std is not None and "train" in ql_std.index:
+ error_y_dict = dict(type="data", array=ql_std.loc["train"].values)
fig.add_trace(
go.Bar(
x=[str(x) for x in ql_data.columns],
@@ -188,14 +176,8 @@ def _plot_quantile_loss(
# Add bars for test data
if "test" in ql_data.index:
error_y_dict = None
- if (
- show_error_bars
- and ql_std is not None
- and "test" in ql_std.index
- ):
- error_y_dict = dict(
- type="data", array=ql_std.loc["test"].values
- )
+ if show_error_bars and ql_std is not None and "test" in ql_std.index:
+ error_y_dict = dict(type="data", array=ql_std.loc["test"].values)
fig.add_trace(
go.Bar(
x=[str(x) for x in ql_data.columns],
@@ -392,9 +374,7 @@ def _plot_log_loss(
)
fig.update_xaxes(title_text="Class", row=current_row, col=1)
- fig.update_yaxes(
- title_text="Predicted Probability", row=current_row, col=1
- )
+ fig.update_yaxes(title_text="Predicted Probability", row=current_row, col=1)
if title is None:
title = f"Log loss performance - {self.model_name}"
@@ -470,14 +450,8 @@ def _plot_combined(
if "train" in ql_data.index:
error_y_dict = None
- if (
- show_error_bars
- and ql_std is not None
- and "train" in ql_std.index
- ):
- error_y_dict = dict(
- type="data", array=ql_std.loc["train"].values
- )
+ if show_error_bars and ql_std is not None and "train" in ql_std.index:
+ error_y_dict = dict(type="data", array=ql_std.loc["train"].values)
fig.add_trace(
go.Bar(
x=[str(x) for x in ql_data.columns],
@@ -493,14 +467,8 @@ def _plot_combined(
if "test" in ql_data.index:
error_y_dict = None
- if (
- show_error_bars
- and ql_std is not None
- and "test" in ql_std.index
- ):
- error_y_dict = dict(
- type="data", array=ql_std.loc["test"].values
- )
+ if show_error_bars and ql_std is not None and "test" in ql_std.index:
+ error_y_dict = dict(type="data", array=ql_std.loc["test"].values)
fig.add_trace(
go.Bar(
x=[str(x) for x in ql_data.columns],
@@ -659,10 +627,7 @@ def summary(self) -> pd.DataFrame:
else:
summary_data["Log loss test mean"] = [np.nan]
- if (
- "train" in ll_results_df.index
- and "test" in ll_results_df.index
- ):
+ if "train" in ll_results_df.index and "test" in ll_results_df.index:
train_loss = ll_results_df.loc["train"].mean()
test_loss = ll_results_df.loc["test"].mean()
summary_data["Log loss train/test ratio"] = [
@@ -673,9 +638,7 @@ def summary(self) -> pd.DataFrame:
# Add variable info
if "variables" in ll_data:
- summary_data["Log loss variables"] = [
- ", ".join(ll_data["variables"])
- ]
+ summary_data["Log loss variables"] = [", ".join(ll_data["variables"])]
summary_df = pd.DataFrame(summary_data)
logger.debug(f"Summary generated with shape {summary_df.shape}")
@@ -723,8 +686,7 @@ def _save_figure(fig: go.Figure, save_path: str) -> None:
html_path = save_path.rsplit(".", 1)[0] + ".html"
fig.write_html(html_path)
logger.warning(
- f"kaleido not available for image export. "
- f"Saved as HTML to {html_path}"
+ f"kaleido not available for image export. Saved as HTML to {html_path}"
)
except OSError as e:
error_msg = f"Failed to save plot to {save_path}: {str(e)}"
diff --git a/paper/benchmarking_datasets/benchmark_utils.py b/paper/benchmarking_datasets/benchmark_utils.py
index eb4489b..bbe19c5 100644
--- a/paper/benchmarking_datasets/benchmark_utils.py
+++ b/paper/benchmarking_datasets/benchmark_utils.py
@@ -124,9 +124,7 @@ def progressive_predictor_exclusion(
for step, pred_to_remove in enumerate(
tqdm(predictor_order, desc="Progressive exclusion"), start=1
):
- current_predictors = [
- p for p in current_predictors if p != pred_to_remove
- ]
+ current_predictors = [p for p in current_predictors if p != pred_to_remove]
if len(current_predictors) == 0:
# No predictors left - record as maximum degradation
@@ -153,9 +151,7 @@ def progressive_predictor_exclusion(
quantiles=quantiles,
)
- normalized_loss = (
- loss / baseline_loss if baseline_loss > 0 else np.nan
- )
+ normalized_loss = loss / baseline_loss if baseline_loss > 0 else np.nan
results.append(
{
@@ -169,9 +165,7 @@ def progressive_predictor_exclusion(
)
except Exception as e:
- log.warning(
- f"Failed to evaluate after removing {pred_to_remove}: {e}"
- )
+ log.warning(f"Failed to evaluate after removing {pred_to_remove}: {e}")
results.append(
{
"step": step,
@@ -469,9 +463,7 @@ def create_benchmark_summary_table(
# Compute ranks
valid_losses = {k: v for k, v in losses.items() if not np.isnan(v)}
if valid_losses:
- sorted_methods = sorted(
- valid_losses.keys(), key=lambda x: valid_losses[x]
- )
+ sorted_methods = sorted(valid_losses.keys(), key=lambda x: valid_losses[x])
ranks = {m: i + 1 for i, m in enumerate(sorted_methods)}
# Fill in ranks for methods with NaN losses
@@ -494,9 +486,9 @@ def create_benchmark_summary_table(
if cia_results and dataset_name in cia_results:
for method in methods:
if method in cia_results[dataset_name]:
- row[f"{method} CIA"] = cia_results[dataset_name][
- method
- ].get("sensitivity_score", np.nan)
+ row[f"{method} CIA"] = cia_results[dataset_name][method].get(
+ "sensitivity_score", np.nan
+ )
else:
row[f"{method} CIA"] = np.nan
@@ -565,9 +557,7 @@ def create_benchmark_heatmap(
# Handle both nested format and simple float format
if isinstance(val, dict) and "quantile_loss" in val:
# Nested format: cv_results[dataset][method]["quantile_loss"]["mean_test"]
- ql_row.append(
- val["quantile_loss"].get("mean_test", np.nan)
- )
+ ql_row.append(val["quantile_loss"].get("mean_test", np.nan))
elif isinstance(val, (int, float)):
# Simple format: cv_results[dataset][method] = float
ql_row.append(val)
@@ -681,9 +671,7 @@ def create_benchmark_heatmap(
x=[None],
y=[None],
mode="markers",
- marker=dict(
- size=12, color=METHOD_COLORS.get(method, "#999999")
- ),
+ marker=dict(size=12, color=METHOD_COLORS.get(method, "#999999")),
name=method,
)
)
diff --git a/paper/benchmarking_datasets/data_loader.py b/paper/benchmarking_datasets/data_loader.py
index 32421bd..fcc4f8b 100644
--- a/paper/benchmarking_datasets/data_loader.py
+++ b/paper/benchmarking_datasets/data_loader.py
@@ -137,9 +137,7 @@ def get_benchmark_suite_metadata(suite_id: int = 269) -> pd.DataFrame:
for feat in features.values():
feature_names.append(feat.name)
feat_type = feat.data_type
- feature_types[feat_type] = (
- feature_types.get(feat_type, 0) + 1
- )
+ feature_types[feat_type] = feature_types.get(feat_type, 0) + 1
# Calculate interpretability score
interpretability = calculate_interpretability_score(feature_names)
@@ -151,9 +149,7 @@ def get_benchmark_suite_metadata(suite_id: int = 269) -> pd.DataFrame:
"n_instances": qualities.get("NumberOfInstances"),
"n_features": qualities.get("NumberOfFeatures"),
"n_numeric_features": qualities.get("NumberOfNumericFeatures"),
- "n_categorical_features": qualities.get(
- "NumberOfSymbolicFeatures"
- ),
+ "n_categorical_features": qualities.get("NumberOfSymbolicFeatures"),
"n_missing_values": qualities.get("NumberOfMissingValues"),
"pct_missing": qualities.get("PercentageOfMissingValues"),
"n_instances_with_missing": qualities.get(
@@ -285,16 +281,12 @@ def filter_datasets(
# Filter by interpretability score
if min_interpretability_score is not None:
- mask = mask & (
- df["interpretability_score"] >= min_interpretability_score
- )
+ mask = mask & (df["interpretability_score"] >= min_interpretability_score)
# Exclude datasets by name pattern
if exclude_name_patterns:
for pattern in exclude_name_patterns:
- mask = mask & (
- ~df["name"].str.lower().str.contains(pattern.lower())
- )
+ mask = mask & (~df["name"].str.lower().str.contains(pattern.lower()))
filtered_df = df[mask].copy()
@@ -371,9 +363,7 @@ def download_filtered_datasets(
)
try:
- dataset = openml.datasets.get_dataset(
- dataset_id, download_data=True
- )
+ dataset = openml.datasets.get_dataset(dataset_id, download_data=True)
X, y, categorical_indicator, attribute_names = dataset.get_data(
target=dataset.default_target_attribute
)
@@ -385,9 +375,7 @@ def download_filtered_datasets(
df[target_name] = y
# Clean filename
- clean_name = (
- dataset_name.lower().replace(" ", "_").replace("-", "_")
- )
+ clean_name = dataset_name.lower().replace(" ", "_").replace("-", "_")
# Save to file
if file_format == "parquet":
@@ -406,9 +394,7 @@ def download_filtered_datasets(
"categorical_features": (
[
name
- for name, is_cat in zip(
- attribute_names, categorical_indicator
- )
+ for name, is_cat in zip(attribute_names, categorical_indicator)
if is_cat
]
if categorical_indicator
@@ -416,10 +402,7 @@ def download_filtered_datasets(
),
}
- print(
- f" Saved: {output_path} ({df.shape[0]} rows, "
- f"{df.shape[1]} cols)"
- )
+ print(f" Saved: {output_path} ({df.shape[0]} rows, {df.shape[1]} cols)")
except Exception as e:
print(f" ERROR: {e}")
@@ -554,9 +537,7 @@ def get_dataset_details(dataset_id: int) -> dict:
filtered_df = filter_datasets(metadata_df, **FILTER_PARAMS)
- print(
- f"\nDatasets meeting criteria: {len(filtered_df)} / {len(metadata_df)}"
- )
+ print(f"\nDatasets meeting criteria: {len(filtered_df)} / {len(metadata_df)}")
print("\n" + "-" * 80)
filter_cols = [
diff --git a/paper/benchmarking_datasets/dataset_analysis.ipynb b/paper/benchmarking_datasets/dataset_analysis.ipynb
index 19e57fb..efb9ea6 100644
--- a/paper/benchmarking_datasets/dataset_analysis.ipynb
+++ b/paper/benchmarking_datasets/dataset_analysis.ipynb
@@ -29,8 +29,8 @@
" leave_one_out_analysis,\n",
" autoimpute,\n",
" QRF,\n",
- " QuantReg, \n",
- " OLS, \n",
+ " QuantReg,\n",
+ " OLS,\n",
" Matching,\n",
")\n",
"from microimpute.comparisons import compare_distributions\n",
@@ -44,9 +44,9 @@
" run_cia_analysis_for_dataset,\n",
")\n",
"\n",
- "pd.set_option('display.max_columns', None)\n",
- "pd.set_option('display.width', None)\n",
- "pd.set_option('display.max_colwidth', 50)"
+ "pd.set_option(\"display.max_columns\", None)\n",
+ "pd.set_option(\"display.width\", None)\n",
+ "pd.set_option(\"display.max_colwidth\", 50)"
]
},
{
@@ -247,7 +247,7 @@
"print(f\"\\nShape: {df_space_ga.shape}\")\n",
"print(f\"\\nColumn names:\")\n",
"for i, col in enumerate(df_space_ga.columns):\n",
- " print(f\" {i+1}. {col}\")\n",
+ " print(f\" {i + 1}. {col}\")\n",
"print(f\"\\nData types:\")\n",
"print(df_space_ga.dtypes)\n",
"print(f\"\\nFirst few rows:\")\n",
@@ -690,7 +690,7 @@
" data=df_space_ga,\n",
" predictors=space_ga_predictors,\n",
" imputed_variables=[space_ga_target],\n",
- " method=\"all\"\n",
+ " method=\"all\",\n",
")\n",
"\n",
"print(\"\\n--- Pearson Correlation Matrix ---\")\n",
@@ -703,9 +703,11 @@
"display(space_ga_correlations[\"mutual_info\"])\n",
"\n",
"print(\"\\n--- Predictor-Target Mutual Information ---\")\n",
- "display(space_ga_correlations[\"predictor_target_mi\"].sort_values(\n",
- " by=space_ga_target, ascending=False\n",
- "))"
+ "display(\n",
+ " space_ga_correlations[\"predictor_target_mi\"].sort_values(\n",
+ " by=space_ga_target, ascending=False\n",
+ " )\n",
+ ")"
]
},
{
@@ -867,7 +869,7 @@
" imputed_variables=[space_ga_target],\n",
" model_class=QRF,\n",
" train_size=0.6,\n",
- " n_jobs=1\n",
+ " n_jobs=1,\n",
")\n",
"\n",
"print(\"\\n--- Leave-One-Out Results ---\")\n",
@@ -968,9 +970,9 @@
0.001641586846550629,
0.0014254160586643666,
0.0016126628972894675,
- 0.0018542282294491822,
+ 0.0018542282294491824,
0.001254829481032883,
- 0.0012616422274823419,
+ 0.001261642227482342,
0.0008925075876101453,
0.0009789985599894782,
0.0020550687969806075,
@@ -1050,17 +1052,17 @@
"array": [
0.0010833911164227247,
0.0011706862196016997,
- 0.0009853048017531005,
+ 0.0009853048017531003,
0.000892210416357871,
- 0.0009172973122542029,
+ 0.0009172973122542028,
0.001164622135044908,
0.0014272021984131826,
0.001686676762208881,
0.001831444691663165,
- 0.0019462562032744573,
+ 0.001946256203274457,
0.00194753722126197,
0.0019407490556580332,
- 0.0018799036550497994,
+ 0.0018799036550497992,
0.0017377065265933025,
0.001591968134523271,
0.0013560685519335932,
@@ -1108,7 +1110,7 @@
"y": [
0.013601495172351546,
0.022039520319063255,
- 0.028582415903448954,
+ 0.02858241590344895,
0.033730970460644716,
0.03799893409026435,
0.04147346522482332,
@@ -1117,7 +1119,7 @@
0.047597906635362466,
0.04809691002792157,
0.047873756746492246,
- 0.046863588384280196,
+ 0.04686358838428019,
0.04505972475506838,
0.042536489732329034,
0.03925222528048754,
@@ -1133,7 +1135,7 @@
"error_y": {
"array": [
0.011188471044211747,
- 0.011170466264537793,
+ 0.011170466264537791,
0.007572416709230087,
0.008046557525503314,
0.006599233742856614,
@@ -1144,7 +1146,7 @@
0.002763961428761023,
0.0030371975282827667,
0.003647703367441269,
- 0.0039005773788334955,
+ 0.003900577378833495,
0.004922978653361434,
0.005063572136316329,
0.005799875185466762,
@@ -1194,13 +1196,13 @@
0.08151629060086235,
0.08802879746544752,
0.08784592140541951,
- 0.09231444655153817,
+ 0.09231444655153816,
0.09395360366830244,
- 0.09430540688928825,
- 0.09437480622748887,
+ 0.09430540688928823,
+ 0.09437480622748888,
0.09371971036321496,
- 0.09283779711962045,
- 0.09115149545375179,
+ 0.09283779711962044,
+ 0.0911514954537518,
0.08931422613245024,
0.0865236553694542,
0.0837545184989678,
@@ -1219,14 +1221,14 @@
0.004990500230487147,
0.004500933987506405,
0.004039452394904967,
- 0.0036168217018443067,
+ 0.0036168217018443063,
0.0032482422133479484,
0.002954015980260504,
0.0027580424268144484,
0.0026819471146907023,
- 0.0027357516892624442,
+ 0.002735751689262444,
0.0029122652636774096,
- 0.0031911902651137842,
+ 0.003191190265113784,
0.0035484583035040856,
0.003962937299109404,
0.004418556599507702,
@@ -1300,7 +1302,7 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 0.0033196477456942774,
+ 0.003319647745694278,
0.004851665534085506,
0.005580743734245581,
0.00595074087756197,
@@ -1358,13 +1360,13 @@
],
"xaxis": "x",
"y": [
- 0.022783922599610946,
+ 0.022783922599610942,
0.03559129951075892,
0.04464404245361313,
0.05051369261676569,
0.05519761013291058,
0.05817472426610678,
- 0.059641272680632317,
+ 0.05964127268063232,
0.061052271278365086,
0.061756079716894605,
0.061930196518500166,
@@ -2346,7 +2348,7 @@
" predictors=space_ga_predictors,\n",
" imputed_variables=[space_ga_target],\n",
" impute_all=True,\n",
- " log_level=\"INFO\"\n",
+ " log_level=\"INFO\",\n",
")\n",
"\n",
"print(\"\\n--- Autoimpute CV Benchmarking Results ---\")\n",
@@ -2391,45 +2393,51 @@
" receiver_data=space_ga_result.receiver_data, # Imputed values\n",
" imputed_variables=[space_ga_target],\n",
")\n",
- "best_method_name = space_ga_result.fitted_models[\"best_method\"].__class__.__name__ \n",
+ "best_method_name = space_ga_result.fitted_models[\"best_method\"].__class__.__name__\n",
"best_method_name = best_method_name.replace(\"Results\", \"\")\n",
"space_ga_wasserstein[best_method_name] = distribution_comparison_space_ga[\n",
- " distribution_comparison_space_ga['Metric'] == 'wasserstein_distance'\n",
- "]['Distance'].values[0]\n",
- "print(f\"{best_method_name}: Wasserstein Distance = {space_ga_wasserstein.get(best_method_name, 'N/A'):.4f}\")\n",
- " \n",
+ " distribution_comparison_space_ga[\"Metric\"] == \"wasserstein_distance\"\n",
+ "][\"Distance\"].values[0]\n",
+ "print(\n",
+ " f\"{best_method_name}: Wasserstein Distance = {space_ga_wasserstein.get(best_method_name, 'N/A'):.4f}\"\n",
+ ")\n",
+ "\n",
"for method_name, imputations in space_ga_result.imputations.items():\n",
" # Skip the 'best_method' (it's a duplicate)\n",
- " if method_name == 'best_method': \n",
+ " if method_name == \"best_method\":\n",
" continue\n",
- " \n",
- " # Create a copy of receiver data with this method's imputations \n",
- " receiver_with_imputations = space_ga_receiver_no_target.copy() \n",
- " \n",
- " # Handle both dict (quantile->DataFrame) and DataFrame formats \n",
- " if isinstance(imputations, dict): \n",
- " # Get median quantile (0.5) imputations \n",
- " imp_df = imputations.get(0.5, list(imputations.values())[0]) \n",
- " else: \n",
+ "\n",
+ " # Create a copy of receiver data with this method's imputations\n",
+ " receiver_with_imputations = space_ga_receiver_no_target.copy()\n",
+ "\n",
+ " # Handle both dict (quantile->DataFrame) and DataFrame formats\n",
+ " if isinstance(imputations, dict):\n",
+ " # Get median quantile (0.5) imputations\n",
+ " imp_df = imputations.get(0.5, list(imputations.values())[0])\n",
+ " else:\n",
" imp_df = imputations\n",
- " \n",
- " # Add imputed values \n",
+ "\n",
+ " # Add imputed values\n",
" for var in [space_ga_target]:\n",
- " if var in imp_df.columns: \n",
- " receiver_with_imputations[var] = imp_df[var].values \n",
- " \n",
- " # Calculate distribution comparison \n",
- " dist_comparison = compare_distributions( \n",
- " donor_data=space_ga_receiver, # Ground truth \n",
+ " if var in imp_df.columns:\n",
+ " receiver_with_imputations[var] = imp_df[var].values\n",
+ "\n",
+ " # Calculate distribution comparison\n",
+ " dist_comparison = compare_distributions(\n",
+ " donor_data=space_ga_receiver, # Ground truth\n",
" receiver_data=receiver_with_imputations,\n",
- " imputed_variables=[space_ga_target], \n",
+ " imputed_variables=[space_ga_target],\n",
" )\n",
"\n",
- " # Extract Wasserstein distance \n",
- " wd = dist_comparison[dist_comparison['Metric'] == 'wasserstein_distance']['Distance'].values \n",
+ " # Extract Wasserstein distance\n",
+ " wd = dist_comparison[dist_comparison[\"Metric\"] == \"wasserstein_distance\"][\n",
+ " \"Distance\"\n",
+ " ].values\n",
" space_ga_wasserstein[method_name] = wd[0]\n",
- " \n",
- " print(f\"{method_name}: Wasserstein Distance = {space_ga_wasserstein.get(method_name, 'N/A'):.4f}\")"
+ "\n",
+ " print(\n",
+ " f\"{method_name}: Wasserstein Distance = {space_ga_wasserstein.get(method_name, 'N/A'):.4f}\"\n",
+ " )"
]
},
{
@@ -2586,7 +2594,7 @@
5
],
"y": [
- 0.038467598301926476,
+ 0.03846759830192648,
0.046349112806030976,
0.04892122406975649,
0.057574528391621846,
@@ -2678,7 +2686,7 @@
0.06999962605586944,
0.08382087593050129,
0.08962890291527795,
- 0.10580349377942175
+ 0.10580349377942176
]
},
{
@@ -3591,8 +3599,7 @@
"\n",
"# Plot degradation curves\n",
"space_ga_cia_fig = plot_cia_degradation_curves(\n",
- " space_ga_cia_results,\n",
- " title=\"CIA Sensitivity: Space Ga\"\n",
+ " space_ga_cia_results, title=\"CIA Sensitivity: Space Ga\"\n",
")\n",
"space_ga_cia_fig.show()\n",
"\n",
@@ -3860,7 +3867,7 @@
"print(f\"\\nShape: {df_elevators.shape}\")\n",
"print(f\"\\nColumn names:\")\n",
"for i, col in enumerate(df_elevators.columns):\n",
- " print(f\" {i+1}. {col}\")\n",
+ " print(f\" {i + 1}. {col}\")\n",
"print(f\"\\nData types:\")\n",
"print(df_elevators.dtypes)\n",
"print(f\"\\nFirst few rows:\")\n",
@@ -3883,7 +3890,16 @@
],
"source": [
"elevators_target = \"Goal\" # Target variable to impute\n",
- "elevators_predictors = [\"climbRate\", \"Sgz\", \"curRoll\", \"absRoll\", \"SaTime1\", \"SaTime2\", \"SaTime3\", \"SaTime4\"]\n",
+ "elevators_predictors = [\n",
+ " \"climbRate\",\n",
+ " \"Sgz\",\n",
+ " \"curRoll\",\n",
+ " \"absRoll\",\n",
+ " \"SaTime1\",\n",
+ " \"SaTime2\",\n",
+ " \"SaTime3\",\n",
+ " \"SaTime4\",\n",
+ "]\n",
"\n",
"print(f\"Target variable: {elevators_target}\")\n",
"print(f\"Predictors ({len(elevators_predictors)}): {elevators_predictors}\")"
@@ -4457,7 +4473,7 @@
" data=df_elevators,\n",
" predictors=elevators_predictors,\n",
" imputed_variables=[elevators_target],\n",
- " method=\"all\"\n",
+ " method=\"all\",\n",
")\n",
"\n",
"print(\"\\n--- Pearson Correlation Matrix ---\")\n",
@@ -4470,9 +4486,11 @@
"display(elevators_correlations[\"mutual_info\"])\n",
"\n",
"print(\"\\n--- Predictor-Target Mutual Information ---\")\n",
- "display(elevators_correlations[\"predictor_target_mi\"].sort_values(\n",
- " by=elevators_target, ascending=False\n",
- "))"
+ "display(\n",
+ " elevators_correlations[\"predictor_target_mi\"].sort_values(\n",
+ " by=elevators_target, ascending=False\n",
+ " )\n",
+ ")"
]
},
{
@@ -4658,7 +4676,7 @@
" imputed_variables=[elevators_target],\n",
" model_class=QRF,\n",
" train_size=0.6,\n",
- " n_jobs=1\n",
+ " n_jobs=1,\n",
")\n",
"\n",
"print(\"\\n--- Leave-One-Out Results ---\")\n",
@@ -4753,7 +4771,7 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 0.000007279832919092497,
+ 7.279832919092497e-6,
0.00002946086781168022,
0.000017680015605553013,
0.00003260064726269813,
@@ -4766,7 +4784,7 @@
0.00003541115170254013,
0.00002710084542648461,
0.000031513500876534857,
- 0.000020319534850442018,
+ 0.000020319534850442015,
0.000021683408229124396,
0.000027385567802449857,
0.00002121114757429098,
@@ -4811,16 +4829,16 @@
],
"xaxis": "x",
"y": [
- 0.00041733075698071036,
+ 0.0004173307569807104,
0.0006945274770604267,
- 0.0009126544374383521,
+ 0.000912654437438352,
0.0011107153879203403,
- 0.0012569050309727105,
+ 0.0012569050309727103,
0.0014289171149691683,
0.0015593428510627136,
- 0.0016715553625854499,
+ 0.00167155536258545,
0.0017485400744111554,
- 0.0018047005450228843,
+ 0.0018047005450228845,
0.0017793039233780928,
0.0017537879897288806,
0.001709326862951555,
@@ -4841,8 +4859,8 @@
0.000016322284753405822,
0.000018987350280795512,
0.00001816726623433957,
- 0.000018542001529198758,
- 0.000020704097797901034,
+ 0.00001854200152919876,
+ 0.00002070409779790103,
0.000021673718592615825,
0.000023117582879046623,
0.000024665445964902992,
@@ -4854,7 +4872,7 @@
0.00004613676606862346,
0.00004734341041703108,
0.00004705324688034628,
- 0.000044173751641560596,
+ 0.00004417375164156059,
0.000041323795815408205
]
},
@@ -4897,7 +4915,7 @@
"y": [
0.00042523916093980705,
0.0007002687973446931,
- 0.0009127106131948651,
+ 0.0009127106131948652,
0.0010828208840845696,
0.0012231928812307582,
0.0013408541000878575,
@@ -4908,11 +4926,11 @@
0.001671536236653303,
0.0016807631390262927,
0.0016638363311461753,
- 0.0016178087853864383,
+ 0.0016178087853864385,
0.001536460577821025,
- 0.0014138034451574141,
- 0.0012402417531022125,
- 0.0009999176374546677,
+ 0.001413803445157414,
+ 0.0012402417531022123,
+ 0.0009999176374546675,
0.0006625295568369386
],
"yaxis": "y"
@@ -4988,15 +5006,15 @@
0.0013292688760591382,
0.0014318493580148816,
0.0015135825366891982,
- 0.0015732220659344747,
+ 0.0015732220659344749,
0.001607286011995681,
0.001617206960286656,
0.0015990604549444656,
- 0.0015507169047136707,
+ 0.001550716904713671,
0.001469917186888279,
- 0.0013516167592724619,
+ 0.001351616759272462,
0.0011785829817773212,
- 0.0009424738375426881,
+ 0.000942473837542688,
0.0006030690360714073
],
"yaxis": "y"
@@ -5009,8 +5027,8 @@
0.00015311689125606818,
0.00014182058836153944,
0.00013078237364288925,
- 0.00012007344546271649,
- 0.00010979020189466915,
+ 0.00012007344546271648,
+ 0.00010979020189466916,
0.0001000639677207584,
0.00009107337655503052,
0.00008305766235220195,
@@ -5022,7 +5040,7 @@
0.0000727982283131681,
0.00007850075760122417,
0.00008571936140429814,
- 0.00009410580201198965,
+ 0.00009410580201198964,
0.00010337624534796863
]
},
@@ -5066,7 +5084,7 @@
0.0025563195070588732,
0.0025649158260364416,
0.0025735121450140086,
- 0.0025821084639915766,
+ 0.002582108463991577,
0.0025907047829691445,
0.0025993011019467115,
0.00260789742092428,
@@ -5074,12 +5092,12 @@
0.0026250900588794153,
0.002633686377856983,
0.0026422826968345507,
- 0.0026508790158121186,
+ 0.002650879015812118,
0.0026594753347896857,
0.002668071653767254,
0.002676667972744822,
0.0026852642917223894,
- 0.0026938606106999574,
+ 0.002693860610699958,
0.002702456929677525,
0.002711053248655093
],
@@ -5149,7 +5167,7 @@
"y": [
0.012602380012443976,
0.016513906298007766,
- 0.018324844300578954,
+ 0.01832484430057895,
0.019283949361179616,
0.01913778648904192,
0.01832900282100227,
@@ -5161,8 +5179,8 @@
0.015366839451844794,
0.014958599544623951,
0.014316459444683854,
- 0.013600495835174111,
- 0.012506342483222849,
+ 0.013600495835174113,
+ 0.012506342483222847,
0.01161429740353466,
0.009405304824591973,
0.00668313134903145
@@ -5205,8 +5223,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 0.0012825579129655041,
- "y1": 0.0012825579129655041
+ "y0": 0.001282557912965504,
+ "y1": 0.001282557912965504
},
{
"line": {
@@ -5244,8 +5262,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 0.015212923767235787,
- "y1": 0.015212923767235787
+ "y0": 0.015212923767235789,
+ "y1": 0.015212923767235789
}
],
"template": {
@@ -6134,7 +6152,7 @@
" predictors=elevators_predictors,\n",
" imputed_variables=[elevators_target],\n",
" impute_all=True,\n",
- " log_level=\"INFO\"\n",
+ " log_level=\"INFO\",\n",
")\n",
"\n",
"print(\"\\n--- Autoimpute CV Results ---\")\n",
@@ -6173,7 +6191,7 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 0.000007279832919092497,
+ 7.279832919092497e-6,
0.00002946086781168022,
0.000017680015605553013,
0.00003260064726269813,
@@ -6186,7 +6204,7 @@
0.00003541115170254013,
0.00002710084542648461,
0.000031513500876534857,
- 0.000020319534850442018,
+ 0.000020319534850442015,
0.000021683408229124396,
0.000027385567802449857,
0.00002121114757429098,
@@ -6231,16 +6249,16 @@
],
"xaxis": "x",
"y": [
- 0.00041733075698071036,
+ 0.0004173307569807104,
0.0006945274770604267,
- 0.0009126544374383521,
+ 0.000912654437438352,
0.0011107153879203403,
- 0.0012569050309727105,
+ 0.0012569050309727103,
0.0014289171149691683,
0.0015593428510627136,
- 0.0016715553625854499,
+ 0.00167155536258545,
0.0017485400744111554,
- 0.0018047005450228843,
+ 0.0018047005450228845,
0.0017793039233780928,
0.0017537879897288806,
0.001709326862951555,
@@ -6261,8 +6279,8 @@
0.000016322284753405822,
0.000018987350280795512,
0.00001816726623433957,
- 0.000018542001529198758,
- 0.000020704097797901034,
+ 0.00001854200152919876,
+ 0.00002070409779790103,
0.000021673718592615825,
0.000023117582879046623,
0.000024665445964902992,
@@ -6274,7 +6292,7 @@
0.00004613676606862346,
0.00004734341041703108,
0.00004705324688034628,
- 0.000044173751641560596,
+ 0.00004417375164156059,
0.000041323795815408205
]
},
@@ -6317,7 +6335,7 @@
"y": [
0.00042523916093980705,
0.0007002687973446931,
- 0.0009127106131948651,
+ 0.0009127106131948652,
0.0010828208840845696,
0.0012231928812307582,
0.0013408541000878575,
@@ -6328,11 +6346,11 @@
0.001671536236653303,
0.0016807631390262927,
0.0016638363311461753,
- 0.0016178087853864383,
+ 0.0016178087853864385,
0.001536460577821025,
- 0.0014138034451574141,
- 0.0012402417531022125,
- 0.0009999176374546677,
+ 0.001413803445157414,
+ 0.0012402417531022123,
+ 0.0009999176374546675,
0.0006625295568369386
],
"yaxis": "y"
@@ -6408,15 +6426,15 @@
0.0013292688760591382,
0.0014318493580148816,
0.0015135825366891982,
- 0.0015732220659344747,
+ 0.0015732220659344749,
0.001607286011995681,
0.001617206960286656,
0.0015990604549444656,
- 0.0015507169047136707,
+ 0.001550716904713671,
0.001469917186888279,
- 0.0013516167592724619,
+ 0.001351616759272462,
0.0011785829817773212,
- 0.0009424738375426881,
+ 0.000942473837542688,
0.0006030690360714073
],
"yaxis": "y"
@@ -6429,8 +6447,8 @@
0.00015311689125606818,
0.00014182058836153944,
0.00013078237364288925,
- 0.00012007344546271649,
- 0.00010979020189466915,
+ 0.00012007344546271648,
+ 0.00010979020189466916,
0.0001000639677207584,
0.00009107337655503052,
0.00008305766235220195,
@@ -6442,7 +6460,7 @@
0.0000727982283131681,
0.00007850075760122417,
0.00008571936140429814,
- 0.00009410580201198965,
+ 0.00009410580201198964,
0.00010337624534796863
]
},
@@ -6486,7 +6504,7 @@
0.0025563195070588732,
0.0025649158260364416,
0.0025735121450140086,
- 0.0025821084639915766,
+ 0.002582108463991577,
0.0025907047829691445,
0.0025993011019467115,
0.00260789742092428,
@@ -6494,12 +6512,12 @@
0.0026250900588794153,
0.002633686377856983,
0.0026422826968345507,
- 0.0026508790158121186,
+ 0.002650879015812118,
0.0026594753347896857,
0.002668071653767254,
0.002676667972744822,
0.0026852642917223894,
- 0.0026938606106999574,
+ 0.002693860610699958,
0.002702456929677525,
0.002711053248655093
],
@@ -6541,8 +6559,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 0.0012825579129655041,
- "y1": 0.0012825579129655041
+ "y0": 0.001282557912965504,
+ "y1": 0.001282557912965504
},
{
"line": {
@@ -7440,7 +7458,7 @@
}
],
"source": [
- "elevators_result.cv_results.pop('MDN', None)\n",
+ "elevators_result.cv_results.pop(\"MDN\", None)\n",
"\n",
"print(\"\\n--- Autoimpute CV Results (Zoomed In Without MDN) ---\")\n",
"comparison_viz = method_comparison_results(\n",
@@ -7483,45 +7501,51 @@
" receiver_data=elevators_result.receiver_data, # Imputed values\n",
" imputed_variables=[elevators_target],\n",
")\n",
- "best_method_name = elevators_result.fitted_models[\"best_method\"].__class__.__name__ \n",
+ "best_method_name = elevators_result.fitted_models[\"best_method\"].__class__.__name__\n",
"best_method_name = best_method_name.replace(\"Results\", \"\")\n",
"elevators_wasserstein[best_method_name] = distribution_comparison_elevators[\n",
- " distribution_comparison_elevators['Metric'] == 'wasserstein_distance'\n",
- "]['Distance'].values[0]\n",
- "print(f\"{best_method_name}: Wasserstein Distance = {elevators_wasserstein.get(best_method_name, 'N/A'):.4f}\")\n",
- " \n",
+ " distribution_comparison_elevators[\"Metric\"] == \"wasserstein_distance\"\n",
+ "][\"Distance\"].values[0]\n",
+ "print(\n",
+ " f\"{best_method_name}: Wasserstein Distance = {elevators_wasserstein.get(best_method_name, 'N/A'):.4f}\"\n",
+ ")\n",
+ "\n",
"for method_name, imputations in elevators_result.imputations.items():\n",
" # Skip the 'best_method' (it's a duplicate)\n",
- " if method_name == 'best_method': \n",
+ " if method_name == \"best_method\":\n",
" continue\n",
- " \n",
- " # Create a copy of receiver data with this method's imputations \n",
- " receiver_with_imputations = elevators_receiver_no_target.copy() \n",
- " \n",
- " # Handle both dict (quantile->DataFrame) and DataFrame formats \n",
- " if isinstance(imputations, dict): \n",
- " # Get median quantile (0.5) imputations \n",
- " imp_df = imputations.get(0.5, list(imputations.values())[0]) \n",
- " else: \n",
+ "\n",
+ " # Create a copy of receiver data with this method's imputations\n",
+ " receiver_with_imputations = elevators_receiver_no_target.copy()\n",
+ "\n",
+ " # Handle both dict (quantile->DataFrame) and DataFrame formats\n",
+ " if isinstance(imputations, dict):\n",
+ " # Get median quantile (0.5) imputations\n",
+ " imp_df = imputations.get(0.5, list(imputations.values())[0])\n",
+ " else:\n",
" imp_df = imputations\n",
- " \n",
- " # Add imputed values \n",
+ "\n",
+ " # Add imputed values\n",
" for var in [elevators_target]:\n",
- " if var in imp_df.columns: \n",
- " receiver_with_imputations[var] = imp_df[var].values \n",
- " \n",
- " # Calculate distribution comparison \n",
- " dist_comparison = compare_distributions( \n",
- " donor_data=elevators_receiver, # Ground truth \n",
+ " if var in imp_df.columns:\n",
+ " receiver_with_imputations[var] = imp_df[var].values\n",
+ "\n",
+ " # Calculate distribution comparison\n",
+ " dist_comparison = compare_distributions(\n",
+ " donor_data=elevators_receiver, # Ground truth\n",
" receiver_data=receiver_with_imputations,\n",
- " imputed_variables=[elevators_target], \n",
+ " imputed_variables=[elevators_target],\n",
" )\n",
"\n",
- " # Extract Wasserstein distance \n",
- " wd = dist_comparison[dist_comparison['Metric'] == 'wasserstein_distance']['Distance'].values \n",
+ " # Extract Wasserstein distance\n",
+ " wd = dist_comparison[dist_comparison[\"Metric\"] == \"wasserstein_distance\"][\n",
+ " \"Distance\"\n",
+ " ].values\n",
" elevators_wasserstein[method_name] = wd[0]\n",
- " \n",
- " print(f\"{method_name}: Wasserstein Distance = {elevators_wasserstein.get(method_name, 'N/A'):.4f}\")"
+ "\n",
+ " print(\n",
+ " f\"{method_name}: Wasserstein Distance = {elevators_wasserstein.get(method_name, 'N/A'):.4f}\"\n",
+ " )"
]
},
{
@@ -7714,11 +7738,11 @@
],
"y": [
0.0012500624185658913,
- 0.0012790174353942765,
+ 0.0012790174353942763,
0.0012791453105987344,
0.0012995060752243423,
0.0015676697180221847,
- 0.0016981605659520615,
+ 0.0016981605659520617,
0.0017195681477192664,
0.0017193279810154838
]
@@ -7751,8 +7775,8 @@
0.0011543742509937177,
0.0011703123646945586,
0.0013811625011721685,
- 0.0015298947409129993,
- 0.0015657802624936227,
+ 0.001529894740912999,
+ 0.0015657802624936229,
0.0015718894935134787
]
},
@@ -8703,8 +8727,7 @@
"\n",
"# Plot degradation curves\n",
"elevators_cia_fig = plot_cia_degradation_curves(\n",
- " elevators_cia_results,\n",
- " title=\"CIA Sensitivity: Elevators\"\n",
+ " elevators_cia_results, title=\"CIA Sensitivity: Elevators\"\n",
")\n",
"elevators_cia_fig.show()\n",
"\n",
@@ -8924,7 +8947,7 @@
"print(f\"\\nShape: {df_brazilian.shape}\")\n",
"print(f\"\\nColumn names:\")\n",
"for i, col in enumerate(df_brazilian.columns):\n",
- " print(f\" {i+1}. {col}\")\n",
+ " print(f\" {i + 1}. {col}\")\n",
"print(f\"\\nData types:\")\n",
"print(df_brazilian.dtypes)\n",
"print(f\"\\nFirst few rows:\")\n",
@@ -8947,7 +8970,13 @@
],
"source": [
"brazilian_target = \"rent_amount_(BRL)\" # Target variable to impute\n",
- "brazilian_predictors = [\"rooms\", \"floor\", \"hoa_(BRL)\", \"property_tax_(BRL)\", \"fire_insurance_(BRL)\"]\n",
+ "brazilian_predictors = [\n",
+ " \"rooms\",\n",
+ " \"floor\",\n",
+ " \"hoa_(BRL)\",\n",
+ " \"property_tax_(BRL)\",\n",
+ " \"fire_insurance_(BRL)\",\n",
+ "]\n",
"\n",
"print(f\"Target variable: {brazilian_target}\")\n",
"print(f\"Predictors ({len(brazilian_predictors)}): {brazilian_predictors}\")"
@@ -9335,7 +9364,7 @@
" data=df_brazilian,\n",
" predictors=brazilian_predictors,\n",
" imputed_variables=[brazilian_target],\n",
- " method=\"all\"\n",
+ " method=\"all\",\n",
")\n",
"\n",
"print(\"\\n--- Pearson Correlation Matrix ---\")\n",
@@ -9348,9 +9377,11 @@
"display(brazilian_correlations[\"mutual_info\"])\n",
"\n",
"print(\"\\n--- Predictor-Target Mutual Information ---\")\n",
- "display(brazilian_correlations[\"predictor_target_mi\"].sort_values(\n",
- " by=brazilian_target, ascending=False\n",
- "))"
+ "display(\n",
+ " brazilian_correlations[\"predictor_target_mi\"].sort_values(\n",
+ " by=brazilian_target, ascending=False\n",
+ " )\n",
+ ")"
]
},
{
@@ -9500,7 +9531,7 @@
" imputed_variables=[brazilian_target],\n",
" model_class=QRF,\n",
" train_size=0.6,\n",
- " n_jobs=1\n",
+ " n_jobs=1,\n",
")\n",
"\n",
"print(\"\\n--- Leave-One-Out Results ---\")\n",
@@ -9596,10 +9627,10 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 3.1072974576558354,
+ 3.107297457655835,
3.173472785580769,
2.845580589626428,
- 1.7549878759386435,
+ 1.7549878759386437,
4.177878882735426,
3.347338117083344,
3.3550935697327104,
@@ -9608,10 +9639,10 @@
4.78874671805982,
6.2170904100295425,
4.460696816140001,
- 7.5800773055275545,
- 7.8227182830889435,
+ 7.580077305527555,
+ 7.822718283088944,
7.401020283305162,
- 10.023676321476369,
+ 10.023676321476367,
9.723242741104022,
10.253533450814857,
11.13805573184919
@@ -9658,7 +9689,7 @@
35.71683554169915,
43.922470771628994,
49.21275136399065,
- 55.548480124707716,
+ 55.54848012470771,
60.572860483242394,
65.50799688230708,
66.43289166017146,
@@ -9688,10 +9719,10 @@
6.296534342783538,
5.6211587352247445,
4.908122927624412,
- 3.7955282448487293,
+ 3.7955282448487297,
2.818644336351974,
2.09174379627377,
- 1.8420185929381039,
+ 1.842018592938104,
2.05294500583899,
2.2709424329333614,
2.6048666108839944,
@@ -9746,7 +9777,7 @@
141.76553013766224,
146.21444277019924,
148.99821816017044,
- 151.32972095844445,
+ 151.32972095844443,
153.51725530867773,
154.60351178771742,
154.74132339224963,
@@ -9754,8 +9785,8 @@
149.65104502519253,
142.39016058555669,
131.17725926043133,
- 114.78010041972375,
- 91.63331439897473,
+ 114.78010041972377,
+ 91.63331439897472,
59.110454376877534
],
"yaxis": "y"
@@ -9825,18 +9856,18 @@
43.39814190414394,
69.36283126903236,
85.77857204469834,
- 102.36882753066385,
+ 102.36882753066384,
114.15020625795276,
- 122.93034306885575,
+ 122.93034306885576,
133.79421716721043,
152.16683197699382,
167.39144465711925,
- 162.35099337948319,
+ 162.3509933794832,
147.43137042156485,
131.3510126289026,
118.20775772834531,
- 104.96705980967423,
- 91.40677696892523,
+ 104.96705980967424,
+ 91.40677696892524,
85.65733745986346,
79.54944934430779,
48.28930320097427,
@@ -9848,15 +9879,15 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 20.787085449742314,
- 18.133934780109254,
+ 20.78708544974231,
+ 18.133934780109257,
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11.186939137986156,
9.623735377066708,
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@@ -9910,7 +9941,7 @@
359.5553702260328,
364.45199532346066,
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- 374.24524551831644,
+ 374.2452455183165,
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384.03849571317227,
388.93512081060015,
@@ -9918,7 +9949,7 @@
398.728371005456,
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408.5216212003118,
- 413.41824629773964,
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428.1081215900234,
@@ -9935,7 +9966,7 @@
17.82708018826267,
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19.507534048055877,
- 14.297240685602839,
+ 14.29724068560284,
33.842332189775505,
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105.56188466433146,
@@ -9943,14 +9974,14 @@
200.2093336892162,
254.519039676545,
312.86215715520075,
- 375.71540655326277,
+ 375.7154065532628,
442.0045411281469,
512.7219798864934,
587.260642342192,
667.7304038811378,
753.0074651554911,
845.6548833756536,
- 947.8124427082157
+ 947.8124427082156
]
},
"hovertemplate": "Method=MDN
Quantiles=%{x}
Quantile loss=%{y}",
@@ -9994,14 +10025,14 @@
406.6583397522585,
588.5803247627703,
760.992829991829,
- 925.0212772214775,
+ 925.0212772214776,
1080.747056574654,
1230.5342890000138,
1373.8658721081802,
1511.7503385087427,
1643.7695372411265,
1770.2148889244006,
- 1890.0146683348182,
+ 1890.0146683348185,
2004.0096345906543,
2110.7926275320788,
2209.449751827035,
@@ -10048,8 +10079,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 126.92676693194979,
- "y1": 126.92676693194979
+ "y0": 126.9267669319498,
+ "y1": 126.9267669319498
},
{
"line": {
@@ -10074,8 +10105,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 398.72837100545604,
- "y1": 398.72837100545604
+ "y0": 398.728371005456,
+ "y1": 398.728371005456
},
{
"line": {
@@ -10977,7 +11008,7 @@
" predictors=brazilian_predictors,\n",
" imputed_variables=[brazilian_target],\n",
" impute_all=True,\n",
- " log_level=\"INFO\"\n",
+ " log_level=\"INFO\",\n",
")\n",
"\n",
"print(\"\\n--- Autoimpute CV Results ---\")\n",
@@ -11016,10 +11047,10 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 3.1072974576558354,
+ 3.107297457655835,
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+ 1.7549878759386437,
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@@ -11028,10 +11059,10 @@
4.78874671805982,
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+ 10.023676321476367,
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@@ -11078,7 +11109,7 @@
35.71683554169915,
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+ 55.54848012470771,
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@@ -11108,10 +11139,10 @@
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+ 1.842018592938104,
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@@ -11166,7 +11197,7 @@
141.76553013766224,
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+ 151.32972095844443,
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@@ -11174,8 +11205,8 @@
149.65104502519253,
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- 114.78010041972375,
- 91.63331439897473,
+ 114.78010041972377,
+ 91.63331439897472,
59.110454376877534
],
"yaxis": "y"
@@ -11245,18 +11276,18 @@
43.39814190414394,
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- 102.36882753066385,
+ 102.36882753066384,
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+ 122.93034306885576,
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+ 104.96705980967424,
+ 91.40677696892524,
85.65733745986346,
79.54944934430779,
48.28930320097427,
@@ -11268,15 +11299,15 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 20.787085449742314,
- 18.133934780109254,
+ 20.78708544974231,
+ 18.133934780109257,
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@@ -11330,7 +11361,7 @@
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@@ -11338,7 +11369,7 @@
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@@ -11384,8 +11415,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 126.92676693194979,
- "y1": 126.92676693194979
+ "y0": 126.9267669319498,
+ "y1": 126.9267669319498
},
{
"line": {
@@ -11410,8 +11441,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 398.72837100545604,
- "y1": 398.72837100545604
+ "y0": 398.728371005456,
+ "y1": 398.728371005456
}
],
"template": {
@@ -12283,7 +12314,7 @@
}
],
"source": [
- "brazilian_result.cv_results.pop('MDN', None)\n",
+ "brazilian_result.cv_results.pop(\"MDN\", None)\n",
"\n",
"print(\"\\n--- Autoimpute CV Results (Zoomed In Without MDN) ---\")\n",
"comparison_viz = method_comparison_results(\n",
@@ -12371,46 +12402,52 @@
" receiver_data=brazilian_result.receiver_data, # Imputed values\n",
" imputed_variables=[brazilian_target],\n",
")\n",
- "best_method_name = brazilian_result.fitted_models[\"best_method\"].__class__.__name__ \n",
+ "best_method_name = brazilian_result.fitted_models[\"best_method\"].__class__.__name__\n",
"best_method_name = best_method_name.replace(\"Results\", \"\")\n",
"brazilian_wasserstein[best_method_name] = distribution_comparison_brazilian[\n",
- " distribution_comparison_brazilian['Metric'] == 'wasserstein_distance'\n",
- "]['Distance'].values[0]\n",
- "print(f\"{best_method_name}: Wasserstein Distance = {brazilian_wasserstein.get(best_method_name, 'N/A'):.4f}\")\n",
- " \n",
+ " distribution_comparison_brazilian[\"Metric\"] == \"wasserstein_distance\"\n",
+ "][\"Distance\"].values[0]\n",
+ "print(\n",
+ " f\"{best_method_name}: Wasserstein Distance = {brazilian_wasserstein.get(best_method_name, 'N/A'):.4f}\"\n",
+ ")\n",
+ "\n",
"for method_name, imputations in brazilian_result.imputations.items():\n",
" # Skip the 'best_method' (it's a duplicate)\n",
- " if method_name == 'best_method': \n",
+ " if method_name == \"best_method\":\n",
" continue\n",
- " \n",
- " # Create a copy of receiver data with this method's imputations \n",
- " receiver_with_imputations = brazilian_receiver_no_target.copy() \n",
- " \n",
- " # Handle both dict (quantile->DataFrame) and DataFrame formats \n",
- " if isinstance(imputations, dict): \n",
- " # Get median quantile (0.5) imputations \n",
- " imp_df = imputations.get(0.5, list(imputations.values())[0]) \n",
- " else: \n",
+ "\n",
+ " # Create a copy of receiver data with this method's imputations\n",
+ " receiver_with_imputations = brazilian_receiver_no_target.copy()\n",
+ "\n",
+ " # Handle both dict (quantile->DataFrame) and DataFrame formats\n",
+ " if isinstance(imputations, dict):\n",
+ " # Get median quantile (0.5) imputations\n",
+ " imp_df = imputations.get(0.5, list(imputations.values())[0])\n",
+ " else:\n",
" imp_df = imputations\n",
- " \n",
- " # Add imputed values \n",
+ "\n",
+ " # Add imputed values\n",
" for var in [brazilian_target]:\n",
- " if var in imp_df.columns: \n",
- " receiver_with_imputations[var] = imp_df[var].values \n",
- " \n",
- " # Calculate distribution comparison \n",
- " dist_comparison = compare_distributions( \n",
- " donor_data=brazilian_receiver, # Ground truth \n",
+ " if var in imp_df.columns:\n",
+ " receiver_with_imputations[var] = imp_df[var].values\n",
+ "\n",
+ " # Calculate distribution comparison\n",
+ " dist_comparison = compare_distributions(\n",
+ " donor_data=brazilian_receiver, # Ground truth\n",
" receiver_data=receiver_with_imputations,\n",
- " imputed_variables=[brazilian_target], \n",
+ " imputed_variables=[brazilian_target],\n",
" )\n",
"\n",
- " # Extract Wasserstein distance \n",
- " wd = dist_comparison[dist_comparison['Metric'] == 'wasserstein_distance']['Distance'].values \n",
+ " # Extract Wasserstein distance\n",
+ " wd = dist_comparison[dist_comparison[\"Metric\"] == \"wasserstein_distance\"][\n",
+ " \"Distance\"\n",
+ " ].values\n",
" brazilian_wasserstein[method_name] = wd[0]\n",
- " \n",
- " print(f\"{method_name}: Wasserstein Distance = {brazilian_wasserstein.get(method_name, 'N/A'):.4f}\")\n",
- " \n",
+ "\n",
+ " print(\n",
+ " f\"{method_name}: Wasserstein Distance = {brazilian_wasserstein.get(method_name, 'N/A'):.4f}\"\n",
+ " )\n",
+ "\n",
"# Display full comparison for best method\n",
"display(distribution_comparison_brazilian)"
]
@@ -12595,7 +12632,7 @@
4
],
"y": [
- 123.54322716393965,
+ 123.54322716393963,
746.9635077743502,
750.4300176323405,
751.0494055667007,
@@ -12622,7 +12659,7 @@
4
],
"y": [
- 91.14614542283971,
+ 91.14614542283972,
720.2594169403153,
783.1259101387384,
679.5062916677432,
@@ -12676,7 +12713,7 @@
4
],
"y": [
- 1479.5714570515665,
+ 1479.5714570515663,
1332.5799732178648,
1487.9663522328165,
1679.526102480061,
@@ -13564,8 +13601,7 @@
"\n",
"# Plot degradation curves\n",
"brazilian_cia_fig = plot_cia_degradation_curves(\n",
- " brazilian_cia_results,\n",
- " title=\"CIA Sensitivity: Brazilian Houses\"\n",
+ " brazilian_cia_results, title=\"CIA Sensitivity: Brazilian Houses\"\n",
")\n",
"brazilian_cia_fig.show()\n",
"\n",
@@ -14198,10 +14234,10 @@
"print(f\"\\nColumn names (first 30 and last 10):\")\n",
"cols = list(df_news.columns)\n",
"for i, col in enumerate(cols[:30]):\n",
- " print(f\" {i+1}. {col}\")\n",
+ " print(f\" {i + 1}. {col}\")\n",
"print(\" ...\")\n",
"for i, col in enumerate(cols[-10:]):\n",
- " print(f\" {len(cols)-10+i+1}. {col}\")\n",
+ " print(f\" {len(cols) - 10 + i + 1}. {col}\")\n",
"print(f\"\\nData types (sample):\")\n",
"print(df_news.dtypes[:10])\n",
"print(f\"\\nFirst few rows:\")\n",
@@ -14224,7 +14260,17 @@
],
"source": [
"news_target = \"shares\" # Target variable to impute\n",
- "news_predictors = [\"n_tokens_title\", \"n_tokens_content\", \"num_hrefs\", \"num_imgs\", \"num_videos\", \"global_subjectivity\", \"global_sentiment_polarity\", \"title_subjectivity\", \"title_sentiment_polarity\"]\n",
+ "news_predictors = [\n",
+ " \"n_tokens_title\",\n",
+ " \"n_tokens_content\",\n",
+ " \"num_hrefs\",\n",
+ " \"num_imgs\",\n",
+ " \"num_videos\",\n",
+ " \"global_subjectivity\",\n",
+ " \"global_sentiment_polarity\",\n",
+ " \"title_subjectivity\",\n",
+ " \"title_sentiment_polarity\",\n",
+ "]\n",
"\n",
"print(f\"Target variable: {news_target}\")\n",
"print(f\"Predictors {len(news_predictors)}: {news_predictors}\")"
@@ -14938,7 +14984,7 @@
" data=df_news,\n",
" predictors=news_predictors,\n",
" imputed_variables=[news_target],\n",
- " method=\"all\"\n",
+ " method=\"all\",\n",
")\n",
"\n",
"print(\"\\n--- Pearson Correlation Matrix ---\")\n",
@@ -14951,9 +14997,11 @@
"display(news_correlations[\"mutual_info\"])\n",
"\n",
"print(\"\\n--- Predictor-Target Mutual Information ---\")\n",
- "display(news_correlations[\"predictor_target_mi\"].sort_values(\n",
- " by=news_target, ascending=False\n",
- "))"
+ "display(\n",
+ " news_correlations[\"predictor_target_mi\"].sort_values(\n",
+ " by=news_target, ascending=False\n",
+ " )\n",
+ ")"
]
},
{
@@ -15151,7 +15199,7 @@
" imputed_variables=[news_target],\n",
" model_class=QRF,\n",
" train_size=0.6,\n",
- " n_jobs=1\n",
+ " n_jobs=1,\n",
")\n",
"\n",
"print(\"\\n--- Leave-One-Out Results ---\")\n",
@@ -15246,12 +15294,12 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 15.560146071647623,
+ 15.560146071647624,
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@@ -15317,7 +15365,7 @@
1774.2381807111894,
1841.788580990753,
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+ 1899.9221622620605,
1903.3988327084717,
1856.8536603963116,
1739.2884707393005,
@@ -15332,14 +15380,14 @@
"array": [
60.66262042015628,
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@@ -15389,7 +15437,7 @@
"xaxis": "x",
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+ 1326.551853512986,
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@@ -15397,7 +15445,7 @@
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@@ -15405,8 +15453,8 @@
2604.558954800209,
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- 1951.3192041531165,
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+ 1951.3192041531163,
+ 1389.5216274046843
],
"yaxis": "y"
},
@@ -15417,9 +15465,9 @@
4.91488994454613,
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+ 30.035137787030543,
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@@ -15432,7 +15480,7 @@
77.69495973538872,
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- 91.24919098584027
+ 91.24919098584029
]
},
"hovertemplate": "Method=QuantReg
Quantiles=%{x}
Quantile loss=%{y}",
@@ -15479,7 +15527,7 @@
658.0460032028413,
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- 998.6251910411793,
+ 998.6251910411792,
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@@ -15501,20 +15549,20 @@
242.2009251811794,
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211.42818424761032,
- 196.14703057240666,
+ 196.14703057240663,
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+ 121.8499919828934,
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+ 94.36027012785776,
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- 55.650312854818324,
+ 55.65031285481832,
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]
@@ -15556,25 +15604,25 @@
],
"xaxis": "x",
"y": [
- 1966.6652406524117,
+ 1966.6652406524115,
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+ 1970.681460802925,
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1974.6976809534376,
1976.7057910286935,
1978.7139011039508,
1980.7220111792071,
1982.7301212544637,
- 1984.7382313297198,
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+ 1984.73823132972,
+ 1986.746341404976,
1988.7544514802328,
1990.7625615554891,
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+ 1996.7868917812584,
1998.7950018565148,
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- 2002.8112220070282
+ 2002.811222007028
],
"yaxis": "y"
},
@@ -15599,7 +15647,7 @@
80.48103879439213,
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- 122.50664828922461,
+ 122.5066482892246,
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]
},
@@ -15657,7 +15705,7 @@
1606.055629435565,
1633.0053395677787,
1648.4900320672273,
- 1648.2520695118499,
+ 1648.25206951185,
1622.041442428474
],
"yaxis": "y"
@@ -16612,9 +16660,7 @@
"source": [
"print(\"Creating 60/40 donor/receiver split...\")\n",
"\n",
- "news_donor, news_receiver = train_test_split(\n",
- " df_news, train_size=0.6, random_state=42\n",
- ")\n",
+ "news_donor, news_receiver = train_test_split(df_news, train_size=0.6, random_state=42)\n",
"\n",
"news_receiver_no_target = news_receiver.drop(columns=[news_target])\n",
"\n",
@@ -16628,7 +16674,7 @@
" predictors=news_predictors,\n",
" imputed_variables=[news_target],\n",
" impute_all=True,\n",
- " log_level=\"INFO\"\n",
+ " log_level=\"INFO\",\n",
")\n",
"\n",
"print(\"\\n--- Autoimpute CV Results ---\")\n",
@@ -16717,47 +16763,53 @@
" receiver_data=news_result.receiver_data, # Imputed values\n",
" imputed_variables=[news_target],\n",
")\n",
- "best_method_name = news_result.fitted_models[\"best_method\"].__class__.__name__ \n",
+ "best_method_name = news_result.fitted_models[\"best_method\"].__class__.__name__\n",
"best_method_name = best_method_name.replace(\"Results\", \"\")\n",
"news_wasserstein[best_method_name] = distribution_comparison_news[\n",
- " distribution_comparison_news['Metric'] == 'wasserstein_distance'\n",
- "]['Distance'].values[0]\n",
- "print(f\"{best_method_name}: Wasserstein Distance = {news_wasserstein.get(best_method_name, 'N/A'):.4f}\")\n",
- " \n",
+ " distribution_comparison_news[\"Metric\"] == \"wasserstein_distance\"\n",
+ "][\"Distance\"].values[0]\n",
+ "print(\n",
+ " f\"{best_method_name}: Wasserstein Distance = {news_wasserstein.get(best_method_name, 'N/A'):.4f}\"\n",
+ ")\n",
+ "\n",
"for method_name, imputations in news_result.imputations.items():\n",
" # Skip the 'best_method' (it's a duplicate)\n",
- " if method_name == 'best_method': \n",
+ " if method_name == \"best_method\":\n",
" continue\n",
- " \n",
- " # Create a copy of receiver data with this method's imputations \n",
- " receiver_with_imputations = news_receiver_no_target.copy() \n",
- " \n",
- " # Handle both dict (quantile->DataFrame) and DataFrame formats \n",
- " if isinstance(imputations, dict): \n",
- " # Get median quantile (0.5) imputations \n",
- " imp_df = imputations.get(0.5, list(imputations.values())[0]) \n",
- " else: \n",
+ "\n",
+ " # Create a copy of receiver data with this method's imputations\n",
+ " receiver_with_imputations = news_receiver_no_target.copy()\n",
+ "\n",
+ " # Handle both dict (quantile->DataFrame) and DataFrame formats\n",
+ " if isinstance(imputations, dict):\n",
+ " # Get median quantile (0.5) imputations\n",
+ " imp_df = imputations.get(0.5, list(imputations.values())[0])\n",
+ " else:\n",
" imp_df = imputations\n",
- " \n",
- " # Add imputed values \n",
+ "\n",
+ " # Add imputed values\n",
" for var in [news_target]:\n",
- " if var in imp_df.columns: \n",
- " receiver_with_imputations[var] = imp_df[var].values \n",
- " \n",
- " # Calculate distribution comparison \n",
- " dist_comparison = compare_distributions( \n",
- " donor_data=news_receiver, # Ground truth \n",
+ " if var in imp_df.columns:\n",
+ " receiver_with_imputations[var] = imp_df[var].values\n",
+ "\n",
+ " # Calculate distribution comparison\n",
+ " dist_comparison = compare_distributions(\n",
+ " donor_data=news_receiver, # Ground truth\n",
" receiver_data=receiver_with_imputations,\n",
- " imputed_variables=[news_target], \n",
+ " imputed_variables=[news_target],\n",
" )\n",
"\n",
- " # Extract Wasserstein distance \n",
- " wd = dist_comparison[dist_comparison['Metric'] == 'wasserstein_distance']['Distance'].values \n",
+ " # Extract Wasserstein distance\n",
+ " wd = dist_comparison[dist_comparison[\"Metric\"] == \"wasserstein_distance\"][\n",
+ " \"Distance\"\n",
+ " ].values\n",
" news_wasserstein[method_name] = wd[0]\n",
- " \n",
- " print(f\"{method_name}: Wasserstein Distance = {news_wasserstein.get(method_name, 'N/A'):.4f}\")\n",
- " \n",
- "# Display full comparison for best method \n",
+ "\n",
+ " print(\n",
+ " f\"{method_name}: Wasserstein Distance = {news_wasserstein.get(method_name, 'N/A'):.4f}\"\n",
+ " )\n",
+ "\n",
+ "# Display full comparison for best method\n",
"display(distribution_comparison_news)"
]
},
@@ -16919,7 +16971,7 @@
],
"y": [
1349.170298902762,
- 1338.5774423004161,
+ 1338.577442300416,
1340.6648202799847,
1344.2901425148189,
1388.673739437508,
@@ -16953,9 +17005,9 @@
8
],
"y": [
- 1944.3780501930905,
+ 1944.3780501930903,
1946.2777404678332,
- 1950.0966315694538,
+ 1950.0966315694536,
1949.746157368029,
1949.73516300043,
1950.6366114918412,
@@ -16990,9 +17042,9 @@
"y": [
1028.4151968891467,
1031.2314284887393,
- 1035.1585047313433,
- 1035.5391673039437,
- 1035.7129814449897,
+ 1035.158504731343,
+ 1035.5391673039435,
+ 1035.7129814449895,
1036.1345577111144,
1036.287670986901,
1037.121203579935,
@@ -17029,8 +17081,8 @@
1913.2927859755328,
1875.07951822424,
1965.5614831630724,
- 1963.9431201917014,
- 2026.2640938327659,
+ 1963.9431201917016,
+ 2026.264093832766,
2075.2908311262454
]
},
@@ -17060,8 +17112,8 @@
"y": [
1480.7821241797008,
1595.6940868019838,
- 1150.7572682511513,
- 1530.8876713279699,
+ 1150.7572682511511,
+ 1530.88767132797,
1689.688169321423,
1673.4469904669077,
1515.8517578336116,
@@ -17950,8 +18002,7 @@
"\n",
"# Plot degradation curves\n",
"news_cia_fig = plot_cia_degradation_curves(\n",
- " news_cia_results,\n",
- " title=\"CIA Sensitivity: News\"\n",
+ " news_cia_results, title=\"CIA Sensitivity: News\"\n",
")\n",
"news_cia_fig.show()\n",
"\n",
@@ -18132,7 +18183,7 @@
"print(f\"\\nShape: {df_abalone.shape}\")\n",
"print(f\"\\nColumn names:\")\n",
"for i, col in enumerate(df_abalone.columns):\n",
- " print(f\" {i+1}. {col}\")\n",
+ " print(f\" {i + 1}. {col}\")\n",
"print(f\"\\nData types:\")\n",
"print(df_abalone.dtypes)\n",
"print(f\"\\nFirst few rows:\")\n",
@@ -18729,7 +18780,7 @@
" data=df_abalone,\n",
" predictors=abalone_predictors,\n",
" imputed_variables=[abalone_target],\n",
- " method=\"all\"\n",
+ " method=\"all\",\n",
")\n",
"\n",
"print(\"\\n--- Pearson Correlation Matrix ---\")\n",
@@ -18742,9 +18793,11 @@
"display(abalone_correlations[\"mutual_info\"])\n",
"\n",
"print(\"\\n--- Predictor-Target Mutual Information ---\")\n",
- "display(abalone_correlations[\"predictor_target_mi\"].sort_values(\n",
- " by=abalone_target, ascending=False\n",
- "))"
+ "display(\n",
+ " abalone_correlations[\"predictor_target_mi\"].sort_values(\n",
+ " by=abalone_target, ascending=False\n",
+ " )\n",
+ ")"
]
},
{
@@ -18930,7 +18983,7 @@
" imputed_variables=[abalone_target],\n",
" model_class=QRF,\n",
" train_size=0.6,\n",
- " n_jobs=1\n",
+ " n_jobs=1,\n",
")\n",
"\n",
"print(\"\\n--- Leave-One-Out Results ---\")\n",
@@ -19035,10 +19088,10 @@
0.00029024551341653494,
0.00029087801345889877,
0.00045216813532945134,
- 0.00020430631948203218,
+ 0.00020430631948203216,
0.0006033058929973939,
- 0.00040279946387063235,
- 0.00045993561342650747,
+ 0.0004027994638706323,
+ 0.0004599356134265074,
0.0002963424751161734,
0.0003998795374206044,
0.00015180504092043815,
@@ -19090,12 +19143,12 @@
0.00977020928262996,
0.010720453793608002,
0.011885963511224562,
- 0.012002792423121883,
+ 0.012002792423121885,
0.012249910279043508,
0.012238157748248522,
- 0.012173357885821981,
+ 0.01217335788582198,
0.011792550357452424,
- 0.011332565884167919,
+ 0.01133256588416792,
0.010413367488131308,
0.009396204403941122,
0.008270376378716669,
@@ -19119,15 +19172,15 @@
0.00036724649880935217,
0.00039053815097184126,
0.00041447525982789247,
- 0.00045442297546678445,
+ 0.0004544229754667845,
0.0004970591309255817,
0.0005384563278486812,
0.0005448871576265109,
0.0005371124042098624,
0.0005085526161979347,
0.0004891847358885552,
- 0.00047062940171582203,
- 0.00045756947185525556
+ 0.000470629401715822,
+ 0.0004575694718552555
]
},
"hovertemplate": "Method=OLS
Quantiles=%{x}
Quantile loss=%{y}",
@@ -19255,7 +19308,7 @@
0.00415203322551073,
0.00536652434879917,
0.006295003165407054,
- 0.0069760216580269764,
+ 0.006976021658026976,
0.007467090953206039,
0.007856627683712415,
0.008124981770825527,
@@ -19277,17 +19330,17 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 0.0020365104336603802,
- 0.0019380406463349053,
- 0.0018407949105962973,
+ 0.00203651043366038,
+ 0.0019380406463349051,
+ 0.0018407949105962971,
0.0017449778836709257,
- 0.0016508383572344301,
- 0.0015586803086344201,
- 0.0014688767419103995,
+ 0.00165083835723443,
+ 0.00155868030863442,
+ 0.0014688767419103997,
0.0013818867598028154,
- 0.0012982760517331199,
+ 0.00129827605173312,
0.0012187403134718854,
- 0.0011441296921467239,
+ 0.001144129692146724,
0.0010754697154333852,
0.001013969930900011,
0.0009610059772534084,
@@ -19336,19 +19389,19 @@
"xaxis": "x",
"y": [
0.014285100178129794,
- 0.014380612201891041,
+ 0.01438061220189104,
0.014476124225652282,
- 0.014571636249413525,
- 0.014667148273174765,
+ 0.014571636249413523,
+ 0.014667148273174764,
0.01476266029693601,
0.014858172320697253,
- 0.014953684344458493,
+ 0.014953684344458491,
0.015049196368219737,
- 0.015144708391980977,
+ 0.015144708391980975,
0.015240220415742222,
- 0.015335732439503463,
+ 0.015335732439503464,
0.015431244463264706,
- 0.015526756487025947,
+ 0.015526756487025948,
0.015622268510787189,
0.015717780534548434,
0.015813292558309676,
@@ -19420,7 +19473,7 @@
"xaxis": "x",
"y": [
0.012276574516477503,
- 0.018432648819518734,
+ 0.018432648819518737,
0.02317406481400918,
0.02672731973268183,
0.029399918741729713,
@@ -19437,7 +19490,7 @@
0.02272632121067506,
0.01919042724359827,
0.014768039973995072,
- 0.009304198960612093
+ 0.009304198960612091
],
"yaxis": "y"
}
@@ -19464,8 +19517,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 0.009056111106576949,
- "y1": 0.009056111106576949
+ "y0": 0.009056111106576947,
+ "y1": 0.009056111106576947
},
{
"line": {
@@ -19503,8 +19556,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 0.015144708391980977,
- "y1": 0.015144708391980977
+ "y0": 0.015144708391980975,
+ "y1": 0.015144708391980975
},
{
"line": {
@@ -20406,7 +20459,7 @@
" predictors=abalone_predictors,\n",
" imputed_variables=[abalone_target],\n",
" impute_all=True,\n",
- " log_level=\"INFO\"\n",
+ " log_level=\"INFO\",\n",
")\n",
"\n",
"print(\"\\n--- Autoimpute CV Results ---\")\n",
@@ -20495,46 +20548,52 @@
" receiver_data=abalone_result.receiver_data, # Imputed values\n",
" imputed_variables=[abalone_target],\n",
")\n",
- "best_method_name = abalone_result.fitted_models[\"best_method\"].__class__.__name__ \n",
+ "best_method_name = abalone_result.fitted_models[\"best_method\"].__class__.__name__\n",
"best_method_name = best_method_name.replace(\"Results\", \"\")\n",
"abalone_wasserstein[best_method_name] = distribution_comparison_abalone[\n",
- " distribution_comparison_abalone['Metric'] == 'wasserstein_distance'\n",
- "]['Distance'].values[0]\n",
- "print(f\"{best_method_name}: Wasserstein Distance = {abalone_wasserstein.get(best_method_name, 'N/A'):.4f}\")\n",
- " \n",
+ " distribution_comparison_abalone[\"Metric\"] == \"wasserstein_distance\"\n",
+ "][\"Distance\"].values[0]\n",
+ "print(\n",
+ " f\"{best_method_name}: Wasserstein Distance = {abalone_wasserstein.get(best_method_name, 'N/A'):.4f}\"\n",
+ ")\n",
+ "\n",
"for method_name, imputations in abalone_result.imputations.items():\n",
" # Skip the 'best_method' (it's a duplicate)\n",
- " if method_name == 'best_method': \n",
+ " if method_name == \"best_method\":\n",
" continue\n",
- " \n",
- " # Create a copy of receiver data with this method's imputations \n",
- " receiver_with_imputations = abalone_receiver_no_target.copy() \n",
- " \n",
- " # Handle both dict (quantile->DataFrame) and DataFrame formats \n",
- " if isinstance(imputations, dict): \n",
- " # Get median quantile (0.5) imputations \n",
- " imp_df = imputations.get(0.5, list(imputations.values())[0]) \n",
- " else: \n",
+ "\n",
+ " # Create a copy of receiver data with this method's imputations\n",
+ " receiver_with_imputations = abalone_receiver_no_target.copy()\n",
+ "\n",
+ " # Handle both dict (quantile->DataFrame) and DataFrame formats\n",
+ " if isinstance(imputations, dict):\n",
+ " # Get median quantile (0.5) imputations\n",
+ " imp_df = imputations.get(0.5, list(imputations.values())[0])\n",
+ " else:\n",
" imp_df = imputations\n",
- " \n",
- " # Add imputed values \n",
+ "\n",
+ " # Add imputed values\n",
" for var in [abalone_target]:\n",
- " if var in imp_df.columns: \n",
- " receiver_with_imputations[var] = imp_df[var].values \n",
- " \n",
- " # Calculate distribution comparison \n",
- " dist_comparison = compare_distributions( \n",
- " donor_data=abalone_receiver, # Ground truth \n",
+ " if var in imp_df.columns:\n",
+ " receiver_with_imputations[var] = imp_df[var].values\n",
+ "\n",
+ " # Calculate distribution comparison\n",
+ " dist_comparison = compare_distributions(\n",
+ " donor_data=abalone_receiver, # Ground truth\n",
" receiver_data=receiver_with_imputations,\n",
- " imputed_variables=[abalone_target], \n",
+ " imputed_variables=[abalone_target],\n",
" )\n",
"\n",
- " # Extract Wasserstein distance \n",
- " wd = dist_comparison[dist_comparison['Metric'] == 'wasserstein_distance']['Distance'].values \n",
+ " # Extract Wasserstein distance\n",
+ " wd = dist_comparison[dist_comparison[\"Metric\"] == \"wasserstein_distance\"][\n",
+ " \"Distance\"\n",
+ " ].values\n",
" abalone_wasserstein[method_name] = wd[0]\n",
- " \n",
- " print(f\"{method_name}: Wasserstein Distance = {abalone_wasserstein.get(method_name, 'N/A'):.4f}\")\n",
- " \n",
+ "\n",
+ " print(\n",
+ " f\"{method_name}: Wasserstein Distance = {abalone_wasserstein.get(method_name, 'N/A'):.4f}\"\n",
+ " )\n",
+ "\n",
"# Display full comparison for best method\n",
"display(distribution_comparison_abalone)"
]
@@ -20698,7 +20757,7 @@
0.008693014354066986,
0.011421351674641148,
0.012103755980861244,
- 0.013193911483253589,
+ 0.013193911483253587,
0.014684198564593302,
0.018883708133971288,
0.031053277511961724,
@@ -20762,13 +20821,13 @@
],
"y": [
0.00710735783241504,
- 0.010448574624384293,
+ 0.010448574624384291,
0.010626440020242458,
0.01077682863240476,
- 0.011957216167721843,
+ 0.011957216167721845,
0.016090966710173474,
0.026312275154905133,
- 0.030935669438922498
+ 0.030935669438922495
]
},
{
@@ -20794,7 +20853,7 @@
7
],
"y": [
- 0.014497906698564595,
+ 0.014497906698564596,
0.01839712918660287,
0.018835825358851675,
0.01921471291866029,
@@ -21718,8 +21777,7 @@
"\n",
"# Plot degradation curves\n",
"abalone_cia_fig = plot_cia_degradation_curves(\n",
- " abalone_cia_results,\n",
- " title=\"CIA Sensitivity: Abalone\"\n",
+ " abalone_cia_results, title=\"CIA Sensitivity: Abalone\"\n",
")\n",
"abalone_cia_fig.show()\n",
"\n",
@@ -22018,7 +22076,7 @@
"print(f\"\\nShape: {df_house.shape}\")\n",
"print(f\"\\nColumn names:\")\n",
"for i, col in enumerate(df_house.columns):\n",
- " print(f\" {i+1}. {col}\")\n",
+ " print(f\" {i + 1}. {col}\")\n",
"print(f\"\\nData types:\")\n",
"print(df_house.dtypes)\n",
"print(f\"\\nFirst few rows:\")\n",
@@ -22041,7 +22099,18 @@
],
"source": [
"house_target = \"price\" # Target variable to impute\n",
- "house_predictors = [\"bedrooms\", \"bathrooms\", \"sqft_living\", \"sqft_lot\", \"waterfront\", \"view\", \"condition\", \"grade\", \"sqft_above\", \"sqft_basement\"]\n",
+ "house_predictors = [\n",
+ " \"bedrooms\",\n",
+ " \"bathrooms\",\n",
+ " \"sqft_living\",\n",
+ " \"sqft_lot\",\n",
+ " \"waterfront\",\n",
+ " \"view\",\n",
+ " \"condition\",\n",
+ " \"grade\",\n",
+ " \"sqft_above\",\n",
+ " \"sqft_basement\",\n",
+ "]\n",
"\n",
"print(f\"Target variable: {house_target}\")\n",
"print(f\"Predictors ({len(house_predictors)}): {house_predictors}\")"
@@ -22769,7 +22838,7 @@
" data=df_house,\n",
" predictors=house_predictors,\n",
" imputed_variables=[house_target],\n",
- " method=\"all\"\n",
+ " method=\"all\",\n",
")\n",
"\n",
"print(\"\\n--- Pearson Correlation Matrix ---\")\n",
@@ -22782,9 +22851,11 @@
"display(house_correlations[\"mutual_info\"])\n",
"\n",
"print(\"\\n--- Predictor-Target Mutual Information ---\")\n",
- "display(house_correlations[\"predictor_target_mi\"].sort_values(\n",
- " by=house_target, ascending=False\n",
- "))"
+ "display(\n",
+ " house_correlations[\"predictor_target_mi\"].sort_values(\n",
+ " by=house_target, ascending=False\n",
+ " )\n",
+ ")"
]
},
{
@@ -22994,7 +23065,7 @@
" imputed_variables=[house_target],\n",
" model_class=QRF,\n",
" train_size=0.6,\n",
- " n_jobs=1\n",
+ " n_jobs=1,\n",
")\n",
"\n",
"print(\"\\n--- Leave-One-Out Results ---\")\n",
@@ -23090,7 +23161,7 @@
"error_y": {
"array": [
719.328189753778,
- 940.3832731105417,
+ 940.3832731105416,
1482.848168298151,
981.7573126166058,
893.1098804459698,
@@ -23147,7 +23218,7 @@
],
"xaxis": "x",
"y": [
- 20709.781289760314,
+ 20709.78128976031,
32711.011025779328,
44535.18256502814,
53703.12974447841,
@@ -23175,15 +23246,15 @@
"array": [
194.028361567845,
270.40611376212814,
- 424.35766919963396,
+ 424.3576691996339,
655.5625728964469,
859.3968486998843,
- 1081.5312202798207,
+ 1081.531220279821,
1290.8863186105052,
- 1397.2462643130293,
+ 1397.246264313029,
1540.027505583281,
- 1619.1472241056717,
- 1689.5007427358473,
+ 1619.1472241056715,
+ 1689.500742735847,
1708.6528162176992,
1692.2362737940518,
1724.0607813481622,
@@ -23247,7 +23318,7 @@
71527.86883635657,
67858.72913281275,
62484.32419192977,
- 54953.553220199516,
+ 54953.55322019952,
44734.247783678955,
30447.100712490104
],
@@ -23268,7 +23339,7 @@
2127.8709907744037,
1718.448175433048,
1352.7976079507,
- 1559.8561647881027,
+ 1559.856164788103,
1275.6384343952695,
2065.063847283456,
1573.1974067010156,
@@ -23343,7 +23414,7 @@
"array": [
1980.2054753191617,
1703.6432646631138,
- 1455.5825320961067,
+ 1455.582532096107,
1253.0658913312486,
1121.0536267527482,
1085.579027918468,
@@ -23407,17 +23478,17 @@
102226.80206862911,
102390.5592771491,
102554.31648566911,
- 102718.07369418911,
+ 102718.07369418912,
102881.83090270912,
103045.58811122912,
- 103209.34531974913,
+ 103209.34531974912,
103373.10252826913,
- 103536.85973678913,
- 103700.61694530913,
- 103864.37415382915,
- 104028.13136234913,
+ 103536.85973678912,
+ 103700.61694530911,
+ 103864.37415382917,
+ 104028.13136234912,
104191.88857086914,
- 104355.64577938915
+ 104355.64577938916
],
"yaxis": "y"
},
@@ -23428,7 +23499,7 @@
284.7326924435361,
569.2486180173503,
853.6520482378114,
- 1137.9652075982785,
+ 1137.9652075982783,
1422.2045123242642,
1706.368936967098,
1990.47200208671,
@@ -23436,7 +23507,7 @@
2558.4736744376673,
2842.3662015453397,
3126.183792588692,
- 3409.9218109892086,
+ 3409.921810989209,
3693.5727944433816,
3977.115932312908,
4260.527678263926,
@@ -23496,7 +23567,7 @@
295533.39161048527,
322396.1651101296,
349258.13342021353,
- 376119.19087407645,
+ 376119.1908740765,
402979.11892909755,
429837.5833290928,
456694.0042487571,
@@ -24470,7 +24541,7 @@
" predictors=house_predictors,\n",
" imputed_variables=[house_target],\n",
" impute_all=True,\n",
- " log_level=\"INFO\"\n",
+ " log_level=\"INFO\",\n",
")\n",
"\n",
"print(\"\\n--- Autoimpute CV Results ---\")\n",
@@ -24510,7 +24581,7 @@
"error_y": {
"array": [
719.328189753778,
- 940.3832731105417,
+ 940.3832731105416,
1482.848168298151,
981.7573126166058,
893.1098804459698,
@@ -24567,7 +24638,7 @@
],
"xaxis": "x",
"y": [
- 20709.781289760314,
+ 20709.78128976031,
32711.011025779328,
44535.18256502814,
53703.12974447841,
@@ -24595,15 +24666,15 @@
"array": [
194.028361567845,
270.40611376212814,
- 424.35766919963396,
+ 424.3576691996339,
655.5625728964469,
859.3968486998843,
- 1081.5312202798207,
+ 1081.531220279821,
1290.8863186105052,
- 1397.2462643130293,
+ 1397.246264313029,
1540.027505583281,
- 1619.1472241056717,
- 1689.5007427358473,
+ 1619.1472241056715,
+ 1689.500742735847,
1708.6528162176992,
1692.2362737940518,
1724.0607813481622,
@@ -24667,7 +24738,7 @@
71527.86883635657,
67858.72913281275,
62484.32419192977,
- 54953.553220199516,
+ 54953.55322019952,
44734.247783678955,
30447.100712490104
],
@@ -24688,7 +24759,7 @@
2127.8709907744037,
1718.448175433048,
1352.7976079507,
- 1559.8561647881027,
+ 1559.856164788103,
1275.6384343952695,
2065.063847283456,
1573.1974067010156,
@@ -24763,7 +24834,7 @@
"array": [
1980.2054753191617,
1703.6432646631138,
- 1455.5825320961067,
+ 1455.582532096107,
1253.0658913312486,
1121.0536267527482,
1085.579027918468,
@@ -24827,17 +24898,17 @@
102226.80206862911,
102390.5592771491,
102554.31648566911,
- 102718.07369418911,
+ 102718.07369418912,
102881.83090270912,
103045.58811122912,
- 103209.34531974913,
+ 103209.34531974912,
103373.10252826913,
- 103536.85973678913,
- 103700.61694530913,
- 103864.37415382915,
- 104028.13136234913,
+ 103536.85973678912,
+ 103700.61694530911,
+ 103864.37415382917,
+ 104028.13136234912,
104191.88857086914,
- 104355.64577938915
+ 104355.64577938916
],
"yaxis": "y"
}
@@ -25776,7 +25847,7 @@
}
],
"source": [
- "house_result.cv_results.pop('MDN', None)\n",
+ "house_result.cv_results.pop(\"MDN\", None)\n",
"\n",
"print(\"\\n--- Autoimpute CV Results ---\")\n",
"comparison_viz = method_comparison_results(\n",
@@ -25864,46 +25935,52 @@
" receiver_data=house_result.receiver_data, # Imputed values\n",
" imputed_variables=[house_target],\n",
")\n",
- "best_method_name = house_result.fitted_models[\"best_method\"].__class__.__name__ \n",
+ "best_method_name = house_result.fitted_models[\"best_method\"].__class__.__name__\n",
"best_method_name = best_method_name.replace(\"Results\", \"\")\n",
"house_wasserstein[best_method_name] = distribution_comparison_house[\n",
- " distribution_comparison_house['Metric'] == 'wasserstein_distance'\n",
- "]['Distance'].values[0]\n",
- "print(f\"{best_method_name}: Wasserstein Distance = {house_wasserstein.get(best_method_name, 'N/A'):.4f}\")\n",
- " \n",
+ " distribution_comparison_house[\"Metric\"] == \"wasserstein_distance\"\n",
+ "][\"Distance\"].values[0]\n",
+ "print(\n",
+ " f\"{best_method_name}: Wasserstein Distance = {house_wasserstein.get(best_method_name, 'N/A'):.4f}\"\n",
+ ")\n",
+ "\n",
"for method_name, imputations in house_result.imputations.items():\n",
" # Skip the 'best_method' (it's a duplicate)\n",
- " if method_name == 'best_method': \n",
+ " if method_name == \"best_method\":\n",
" continue\n",
- " \n",
- " # Create a copy of receiver data with this method's imputations \n",
- " receiver_with_imputations = house_receiver_no_target.copy() \n",
- " \n",
- " # Handle both dict (quantile->DataFrame) and DataFrame formats \n",
- " if isinstance(imputations, dict): \n",
- " # Get median quantile (0.5) imputations \n",
- " imp_df = imputations.get(0.5, list(imputations.values())[0]) \n",
- " else: \n",
+ "\n",
+ " # Create a copy of receiver data with this method's imputations\n",
+ " receiver_with_imputations = house_receiver_no_target.copy()\n",
+ "\n",
+ " # Handle both dict (quantile->DataFrame) and DataFrame formats\n",
+ " if isinstance(imputations, dict):\n",
+ " # Get median quantile (0.5) imputations\n",
+ " imp_df = imputations.get(0.5, list(imputations.values())[0])\n",
+ " else:\n",
" imp_df = imputations\n",
- " \n",
- " # Add imputed values \n",
+ "\n",
+ " # Add imputed values\n",
" for var in [house_target]:\n",
- " if var in imp_df.columns: \n",
- " receiver_with_imputations[var] = imp_df[var].values \n",
- " \n",
- " # Calculate distribution comparison \n",
- " dist_comparison = compare_distributions( \n",
- " donor_data=house_receiver, # Ground truth \n",
+ " if var in imp_df.columns:\n",
+ " receiver_with_imputations[var] = imp_df[var].values\n",
+ "\n",
+ " # Calculate distribution comparison\n",
+ " dist_comparison = compare_distributions(\n",
+ " donor_data=house_receiver, # Ground truth\n",
" receiver_data=receiver_with_imputations,\n",
- " imputed_variables=[house_target], \n",
+ " imputed_variables=[house_target],\n",
" )\n",
"\n",
- " # Extract Wasserstein distance \n",
- " wd = dist_comparison[dist_comparison['Metric'] == 'wasserstein_distance']['Distance'].values \n",
+ " # Extract Wasserstein distance\n",
+ " wd = dist_comparison[dist_comparison[\"Metric\"] == \"wasserstein_distance\"][\n",
+ " \"Distance\"\n",
+ " ].values\n",
" house_wasserstein[method_name] = wd[0]\n",
- " \n",
- " print(f\"{method_name}: Wasserstein Distance = {house_wasserstein.get(method_name, 'N/A'):.4f}\")\n",
- " \n",
+ "\n",
+ " print(\n",
+ " f\"{method_name}: Wasserstein Distance = {house_wasserstein.get(method_name, 'N/A'):.4f}\"\n",
+ " )\n",
+ "\n",
"# Display full comparison for best method\n",
"display(distribution_comparison_house)"
]
@@ -26073,9 +26150,9 @@
95291.8059356928,
101646.55424473745,
109849.02169326856,
- 95995.41586398333,
+ 95995.41586398332,
105091.3555817719,
- 102283.51127920425
+ 102283.51127920423
]
},
{
@@ -26112,7 +26189,7 @@
84974.7763763976,
85183.47568120067,
89951.50817774664,
- 90481.03740947727
+ 90481.03740947729
]
},
{
@@ -26178,7 +26255,7 @@
],
"y": [
108071.47490168863,
- 108118.89174184595,
+ 108118.89174184596,
108526.14018043026,
139644.67545685868,
143404.15105250984,
@@ -27107,8 +27184,7 @@
"\n",
"# Plot degradation curves\n",
"house_cia_fig = plot_cia_degradation_curves(\n",
- " house_cia_results,\n",
- " title=\"CIA Sensitivity: House\"\n",
+ " house_cia_results, title=\"CIA Sensitivity: House\"\n",
")\n",
"house_cia_fig.show()\n",
"\n",
@@ -27382,15 +27458,15 @@
"all_cv_results_df = pd.DataFrame(all_cv_results).T\n",
"all_cv_results_df.to_csv(\"benchmark_cv_results.csv\")\n",
"\n",
- "# Collect all Wasserstein results \n",
+ "# Collect all Wasserstein results\n",
"all_wasserstein_results = {\n",
- " \"space_ga\": space_ga_wasserstein, \n",
- " \"elevators\": elevators_wasserstein, \n",
- " \"brazilian_houses\": brazilian_wasserstein, \n",
- " \"onlinenewspopularity\": news_wasserstein, \n",
- " \"abalone\": abalone_wasserstein, \n",
- " \"house_sales\": house_wasserstein, \n",
- "} \n",
+ " \"space_ga\": space_ga_wasserstein,\n",
+ " \"elevators\": elevators_wasserstein,\n",
+ " \"brazilian_houses\": brazilian_wasserstein,\n",
+ " \"onlinenewspopularity\": news_wasserstein,\n",
+ " \"abalone\": abalone_wasserstein,\n",
+ " \"house_sales\": house_wasserstein,\n",
+ "}\n",
"all_wasserstein_results_df = pd.DataFrame(all_wasserstein_results).T\n",
"all_wasserstein_results_df.to_csv(\"benchmark_wasserstein_results.csv\")\n",
"\n",
@@ -27419,7 +27495,9 @@
"outputs": [],
"source": [
"all_cv_results = pd.read_csv(\"benchmark_cv_results.csv\", index_col=0).to_dict()\n",
- "all_wasserstein_results = pd.read_csv(\"benchmark_wasserstein_results.csv\", index_col=0).to_dict()\n",
+ "all_wasserstein_results = pd.read_csv(\n",
+ " \"benchmark_wasserstein_results.csv\", index_col=0\n",
+ ").to_dict()\n",
"all_cia_results = pd.read_csv(\"benchmark_cia_results.csv\", index_col=0).to_dict()"
]
},
@@ -29879,7 +29957,7 @@
"heatmap_fig = create_benchmark_heatmap(\n",
" all_cv_results,\n",
" wasserstein_results=all_wasserstein_results,\n",
- " title=\"Cross-Dataset Performance Comparison\"\n",
+ " title=\"Cross-Dataset Performance Comparison\",\n",
")\n",
"heatmap_fig.show()"
]
diff --git a/paper/imputing-from-scf-to-cps.ipynb b/paper/imputing-from-scf-to-cps.ipynb
index d514dae..92882d2 100644
--- a/paper/imputing-from-scf-to-cps.ipynb
+++ b/paper/imputing-from-scf-to-cps.ipynb
@@ -97,9 +97,7 @@
" logger.debug(f\"Generating SCF URL for year {year}\")\n",
"\n",
" if year not in VALID_YEARS:\n",
- " logger.error(\n",
- " f\"Invalid SCF year: {year}. Valid years are {VALID_YEARS}\"\n",
- " )\n",
+ " logger.error(f\"Invalid SCF year: {year}. Valid years are {VALID_YEARS}\")\n",
" raise ValueError(\n",
" f\"The SCF is not available for {year}. Valid years are {VALID_YEARS}\"\n",
" )\n",
@@ -178,12 +176,8 @@
" f for f in z.namelist() if f.endswith(\".dta\")\n",
" ]\n",
" if not dta_files:\n",
- " logger.error(\n",
- " f\"No Stata files found in zip for year {year}\"\n",
- " )\n",
- " raise ValueError(\n",
- " f\"No Stata files found in zip for year {year}\"\n",
- " )\n",
+ " logger.error(f\"No Stata files found in zip for year {year}\")\n",
+ " raise ValueError(f\"No Stata files found in zip for year {year}\")\n",
"\n",
" logger.debug(f\"Found Stata files: {dta_files}\")\n",
"\n",
@@ -191,12 +185,8 @@
" try:\n",
" logger.debug(f\"Reading Stata file: {dta_files[0]}\")\n",
" with z.open(dta_files[0]) as f:\n",
- " df = pd.read_stata(\n",
- " io.BytesIO(f.read()), columns=columns\n",
- " )\n",
- " logger.debug(\n",
- " f\"Read DataFrame with shape {df.shape}\"\n",
- " )\n",
+ " df = pd.read_stata(io.BytesIO(f.read()), columns=columns)\n",
+ " logger.debug(f\"Read DataFrame with shape {df.shape}\")\n",
"\n",
" # Ensure 'wgt' is included\n",
" if (\n",
@@ -214,9 +204,7 @@
" io.BytesIO(f.read()),\n",
" columns=cols_with_weight,\n",
" )\n",
- " logger.debug(\n",
- " f\"Re-read DataFrame with shape {df.shape}\"\n",
- " )\n",
+ " logger.debug(f\"Re-read DataFrame with shape {df.shape}\")\n",
" except Exception as e:\n",
" logger.error(\n",
" f\"Error reading Stata file for year {year}: {str(e)}\"\n",
@@ -251,9 +239,7 @@
" )\n",
" return result\n",
" else:\n",
- " logger.info(\n",
- " f\"Returning data for single year, shape: {all_data[0].shape}\"\n",
- " )\n",
+ " logger.info(f\"Returning data for single year, shape: {all_data[0].shape}\")\n",
" return all_data[0]\n",
"\n",
" except Exception as e:\n",
@@ -348,14 +334,13 @@
"\n",
"# Monkey patch the requests library to use the unverified context\n",
"old_get = requests.get\n",
- "requests.get = lambda *args, **kwargs: old_get(\n",
- " *args, **{**kwargs, \"verify\": False}\n",
- ")\n",
+ "requests.get = lambda *args, **kwargs: old_get(*args, **{**kwargs, \"verify\": False})\n",
"\n",
"from policyengine_us_data import EnhancedCPS_2024\n",
"\n",
"ENHANCED_CPS_PATH = str(EnhancedCPS_2024().file_path)\n",
"\n",
+ "\n",
"def load_dataset(file_path):\n",
" \"\"\"Load all datasets from H5 file into a flat dictionary.\n",
"\n",
@@ -363,7 +348,7 @@
" time-period format (variable/year -> array) used by\n",
" EnhancedCPS datasets.\n",
" \"\"\"\n",
- " with h5py.File(file_path, 'r') as f:\n",
+ " with h5py.File(file_path, \"r\") as f:\n",
" data = {}\n",
"\n",
" for table_name in f.keys():\n",
@@ -381,6 +366,7 @@
"\n",
" return data\n",
"\n",
+ "\n",
"cps = load_dataset(ENHANCED_CPS_PATH)\n",
"\n",
"# Drop existing net_worth — the EnhancedCPS already contains\n",
@@ -427,9 +413,7 @@
"\n",
"# Apply the mapping to recode the race values\n",
"cps[\"race\"] = np.vectorize(cps_race_mapping.get)(cps[\"cps_race\"])\n",
- "cps[\"farm_self_employment_income\"] = (\n",
- " cps[\"self_employment_income\"] + cps[\"farm_income\"]\n",
- ")\n",
+ "cps[\"farm_self_employment_income\"] = cps[\"self_employment_income\"] + cps[\"farm_income\"]\n",
"cps[\"interest_dividend_income\"] = (\n",
" cps[\"taxable_interest_income\"]\n",
" + cps[\"tax_exempt_interest_income\"]\n",
@@ -459,9 +443,7 @@
" \"is_female\": cps[\"is_female\"][mask_head],\n",
" \"age\": cps[\"age\"][mask_head],\n",
" \"race\": cps[\"race\"][mask_head],\n",
- " \"own_children_in_household\": cps[\"own_children_in_household\"][\n",
- " mask_head\n",
- " ],\n",
+ " \"own_children_in_household\": cps[\"own_children_in_household\"][mask_head],\n",
" }\n",
")\n",
"hh_level = heads.merge(household_sums, on=\"household_id\", how=\"left\")\n",
@@ -638,7 +620,7 @@
7.694453071201986,
7.645842982599933,
7.595983500842188,
- 7.6671780425310825,
+ 7.667178042531083,
7.635193083142834,
6.559533310235051,
6.557378752983201,
@@ -662,7 +644,7 @@
7.957650277573836,
3.4418825744626234,
3.5434092769185965,
- 3.7242850002878973,
+ 3.7242850002878978,
3.588247347394967,
3.7885042677927663,
2.739584626777677,
@@ -718,7 +700,7 @@
4.172800395798258,
4.233096297685509,
4.202558230436047,
- 3.9631005357614963,
+ 3.963100535761496,
4.358035034293809,
7.648729041466286,
7.6224374213921875,
@@ -806,7 +788,7 @@
-4.758541044415323,
-4.758541044415323,
4.1368559756072285,
- 3.2540302045211518,
+ 3.254030204521152,
4.470118450216522,
4.415221986635266,
-3.4963377324600904,
@@ -911,9 +893,9 @@
8.152025788513182,
8.152132399125913,
2.579883783910165,
- 1.9778237925822026,
+ 1.9778237925822024,
1.324611278806859,
- 1.0235812831428779,
+ 1.023581283142878,
2.278853788246184,
5.376862622688169,
5.376862622688169,
@@ -1141,7 +1123,7 @@
5.424119272534824,
5.425842665597558,
-3.667033959629065,
- -3.8159729726411316,
+ -3.815972972641131,
-3.6738888062748143,
-3.737911042888111,
-3.739584626777677,
@@ -1173,7 +1155,7 @@
7.286748486698794,
7.286985350486846,
7.29047591371143,
- 7.2853245846509145,
+ 7.285324584650915,
7.289370461054727,
5.423081944457488,
5.4367133335777496,
@@ -1369,7 +1351,7 @@
7.764002401055045,
7.547956631321748,
7.69078666432515,
- 7.6029772704467495,
+ 7.602977270446749,
6.567189973339429,
6.58425293223572,
6.583404151855853,
@@ -1423,7 +1405,7 @@
3.985002377209326,
3.981667131663963,
3.9840520606771768,
- 3.9915969971365195,
+ 3.991596997136519,
3.99112925936174,
7.854496351876592,
7.869212557458004,
@@ -1438,7 +1420,7 @@
7.916940179227872,
7.910664387894294,
7.91377419552629,
- 7.9010217419982745,
+ 7.901021741998275,
7.925976386210913,
-5.04510134725691,
-5.015471587078903,
@@ -1500,11 +1482,11 @@
3.5917830072098726,
3.5917830072098726,
3.5917830072098726,
- 1.9778237925822026,
- 1.9778237925822026,
- 1.9778237925822026,
- 1.9778237925822026,
- 1.9778237925822026,
+ 1.9778237925822024,
+ 1.9778237925822024,
+ 1.9778237925822024,
+ 1.9778237925822024,
+ 1.9778237925822024,
8.209647985661773,
8.208786325270601,
8.1972430913186,
@@ -1520,11 +1502,11 @@
6.11861107979133,
6.11861107979133,
6.118959882702942,
- 3.8159729726411316,
+ 3.815972972641131,
3.8869041432633336,
3.7311514592408144,
4.019216477740428,
- 3.8159729726411316,
+ 3.815972972641131,
4.942083422779052,
4.947291477539441,
4.942083422779052,
@@ -1685,10 +1667,10 @@
4.4889641345912965,
4.4814631798768705,
4.51494297697715,
- 3.7833246507406026,
- 3.7833246507406026,
+ 3.783324650740602,
+ 3.783324650740602,
3.78400376656609,
- 3.8618005050505038,
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@@ -42735,7 +42717,7 @@
6.1273850040988345,
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- 3.6910342360328317,
+ 3.691034236032832,
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5.516481294251581,
@@ -42937,7 +42919,7 @@
5.118401663497678,
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- 7.1069405478036956,
+ 7.106940547803696,
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5.264878670252871,
@@ -43038,7 +43020,7 @@
6.479795438500867,
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- 3.6570497387224643,
+ 3.657049738722464,
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4.088039272369796,
@@ -43138,7 +43120,7 @@
5.510450793811094,
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+ -3.964097768075445,
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@@ -43171,7 +43153,7 @@
5.69604859621096,
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- 1.9778237925822026,
+ 1.9778237925822024,
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5.4867274198692275,
@@ -43290,7 +43272,7 @@
3.852885055973903,
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+ 3.910072008315359,
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6.848390251473901,
@@ -43309,7 +43291,7 @@
4.906105821097758,
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- -3.9320663020215276,
+ -3.932066302021528,
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@@ -43515,7 +43497,7 @@
4.8002824671312885,
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- 3.6519702131931893,
+ 3.6519702131931897,
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5.665847902045551,
@@ -43581,7 +43563,7 @@
6.123917359076573,
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3.5409091654372515,
- 3.6570497387224643,
+ 3.657049738722464,
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6.4303768558111285,
@@ -43671,7 +43653,7 @@
5.351960886581615,
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+ 7.294841893630315,
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6.531118735348583,
@@ -43731,7 +43713,7 @@
6.481874581121964,
5.449929856930385,
5.102328017416485,
- 3.8159729726411316,
+ 3.815972972641131,
5.107334660712889,
5.207704637382549,
5.445020673362928,
@@ -43747,7 +43729,7 @@
0.02358128314287784,
6.531789033552391,
6.250438853431602,
- 3.8451603110548866,
+ 3.845160311054886,
4.9241122655956335,
5.713333979282035,
5.085033762230071,
@@ -43781,7 +43763,7 @@
-3.4797954385008665,
6.4303768558111285,
4.915285959382061,
- -3.7510412040007868,
+ -3.751041204000787,
5.03256448695835,
5.247336736800119,
6.466985100030004,
@@ -43808,8 +43790,8 @@
5.321997663204172,
5.837095554684761,
5.514662696566065,
- 1.8686793231571346,
- 3.9631005357614963,
+ 1.8686793231571344,
+ 3.963100535761496,
5.657756154905478,
4.682546125807312,
5.275778738507754,
@@ -43887,7 +43869,7 @@
4.440221790481159,
5.325067048775476,
-3.5007025378625403,
- 3.7833246507406026,
+ 3.783324650740602,
5.572110734060795,
6.085263666326771,
6.703372454123232,
@@ -43929,7 +43911,7 @@
5.549661974944907,
4.328071810916366,
5.9454185652540925,
- 3.8791610056445958,
+ 3.879161005644596,
5.555698399391682,
3.9373951355265944,
5.745227049432624,
@@ -43983,14 +43965,14 @@
4.874839631861954,
4.1849492853778525,
5.907810052775481,
- 3.6570497387224643,
+ 3.657049738722464,
5.571601978048409,
4.925148573171328,
6.258394575628903,
3.4948729942018164,
5.7742447003957125,
5.2481402420837195,
- 7.2948418936303145,
+ 7.294841893630315,
5.772001905610447,
4.8236175249708575,
-4.52696334661661,
@@ -44070,7 +44052,7 @@
5.632214272632915,
6.036848373067899,
6.192461625556521,
- 3.9530512993203675,
+ 3.953051299320367,
5.40106966651901,
6.367011541890702,
6.310531498930427,
@@ -44183,7 +44165,7 @@
5.161883981309159,
6.210413355336989,
5.417331923490958,
- 3.7833246507406026,
+ 3.783324650740602,
5.9943464429236455,
5.981140685040358,
6.16317704161285,
@@ -44324,7 +44306,7 @@
3.0610077810835015,
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5.435470235538727,
- -3.8803101735257606,
+ -3.880310173525761,
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6.515614912874836,
5.249845995038571,
@@ -44414,7 +44396,7 @@
-2.8017325335265215,
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- 3.8159729726411316,
+ 3.815972972641131,
4.789324324563922,
6.480083960868728,
-4.331077321056091,
@@ -44575,7 +44557,7 @@
3.438554631113696,
6.221688282016279,
5.250181188350235,
- 5.3793966382586484,
+ 5.379396638258648,
5.990895201429962,
4.100585609936228,
6.51487292383047,
@@ -44631,7 +44613,7 @@
5.307673473785712,
7.119761067857676,
6.5255301428141586,
- 7.6001069102080825,
+ 7.600106910208083,
5.864959526728658,
4.995784121521942,
4.3395516285997955,
@@ -44665,7 +44647,7 @@
6.663893739733008,
4.290987701895782,
3.8351562890134714,
- 3.7328512441187085,
+ 3.732851244118709,
7.48082109968569,
-4.823541810548861,
4.227701265798802,
@@ -44681,7 +44663,7 @@
6.413066323851024,
6.241065227356784,
7.119412908081212,
- -3.9100720083153595,
+ -3.910072008315359,
6.153754172035414,
6.1031966066698216,
3.849656085843704,
@@ -44834,7 +44816,7 @@
-4.673888806274815,
6.628357957637747,
5.858313448020985,
- -3.8364946397857334,
+ -3.836494639785734,
6.1435296431738,
5.109406816663621,
4.9526986527234325,
@@ -44877,7 +44859,7 @@
5.662200309863108,
5.7084266447872904,
5.896960019552019,
- 3.9299163249479685,
+ 3.929916324947969,
7.177761715254702,
5.461331845963266,
4.835156289013471,
@@ -44938,7 +44920,7 @@
5.941873795898234,
6.196184214352738,
4.798185828843203,
- -3.9670757990489802,
+ -3.96707579904898,
5.773549366652281,
5.216984186205296,
6.219483701750193,
@@ -44949,9 +44931,9 @@
5.358035034293809,
5.990895201429962,
5.849610709422905,
- 7.2167615567082235,
+ 7.216761556708224,
5.6477602088909,
- 3.8243670734706403,
+ 3.82436707347064,
6.341435772474347,
5.571601978048409,
-4.849137215433235,
@@ -44986,7 +44968,7 @@
6.024479343548242,
5.3818969232250735,
6.039066781297333,
- -3.2540302045211518,
+ -3.254030204521152,
7.620420196968035,
4.9783060741219405,
5.832298525192125,
@@ -45025,7 +45007,7 @@
2.064973968301103,
5.693548653051382,
4.575640817330762,
- 3.9631005357614963,
+ 3.963100535761496,
6.69963440779475,
5.741318339586678,
5.258351578303794,
@@ -45314,7 +45296,7 @@
5.02790265692552,
5.032181454904795,
6.69963440779475,
- 3.9320663020215276,
+ 3.932066302021528,
5.980517696987074,
-4.170607998365108,
5.397596023434789,
@@ -45363,7 +45345,7 @@
5.118401663497678,
5.107334660712889,
4.963599438150541,
- 7.2424842340057065,
+ 7.242484234005707,
5.998323187643828,
5.345593721725279,
4.83246715050269,
@@ -45438,7 +45420,7 @@
6.157120191513095,
-4.238425131190576,
5.842466697736888,
- 3.6147572334546694,
+ 3.61475723345467,
5.877826580208996,
5.3493431684621084,
5.459425649127319,
@@ -45483,7 +45465,7 @@
6.210413355336989,
3.863059330517076,
6.581329024784346,
- -3.8754508838726442,
+ -3.875450883872644,
6.16317704161285,
2.6468305735407784,
5.381498986601737,
@@ -45593,7 +45575,7 @@
6.695152123876541,
5.698075000439228,
-3.283652671127953,
- 3.8002824671312885,
+ 3.800282467131289,
5.022189312457972,
5.630929059911291,
3.999931262146151,
@@ -45612,7 +45594,7 @@
5.646830573540778,
5.405508213780105,
5.346833383314565,
- -3.9953208740306563,
+ -3.995320874030656,
4.346007335548831,
4.924494350880547,
4.261627386271673,
@@ -45652,7 +45634,7 @@
5.695679141078595,
6.013698049210782,
6.515349062546572,
- 1.9778237925822026,
+ 1.9778237925822024,
5.325695660099079,
6.163460369544114,
1.7225512874788966,
@@ -45710,7 +45692,7 @@
5.885235153356789,
-4.299353183307809,
4.80462541201048,
- 7.3707709352257655,
+ 7.370770935225765,
7.177761715254702,
-4.081589515858281,
4.3671867912470494,
@@ -45921,7 +45903,7 @@
5.008333561258292,
5.032224030708163,
5.1709483909366645,
- 3.6570497387224643,
+ 3.657049738722464,
6.703372454123232,
6.1764808795366255,
5.451553996751087,
@@ -45929,7 +45911,7 @@
6.356705991859761,
5.44584273395648,
5.234701824400927,
- 3.2540302045211518,
+ 3.254030204521152,
5.216232353810857,
5.750145499305122,
5.995922999468626,
@@ -45980,8 +45962,8 @@
4.290776486288845,
-5.279769665732653,
5.797221476402903,
- 3.9631005357614963,
- -3.9967091367425764,
+ 3.963100535761496,
+ -3.996709136742576,
5.004039175418978,
0.02358128314287784,
5.909394657803367,
@@ -46019,7 +46001,7 @@
4.785967218343694,
5.941873795898234,
4.846402928445983,
- 3.8010806027332427,
+ 3.801080602733242,
6.467219714309333,
4.470118450216522,
4.950540771523154,
@@ -46037,7 +46019,7 @@
5.040781626666713,
6.083106835881568,
5.425670633714975,
- 3.9700335481559508,
+ 3.9700335481559503,
5.301671507180482,
4.927213799227116,
6.350101475246418,
@@ -46076,7 +46058,7 @@
5.232640317271676,
5.630929059911291,
5.866714355223827,
- 3.9100720083153595,
+ 3.910072008315359,
4.8236175249708575,
2.849656085843704,
5.294260119287585,
@@ -46114,7 +46096,7 @@
5.6949392865863695,
5.505310480102894,
5.176175361070348,
- 3.8624303738801333,
+ 3.8624303738801338,
5.050992054847304,
5.494872994201816,
4.1798274735402225,
@@ -46297,7 +46279,7 @@
5.407037579667631,
4.953771035757784,
6.555556800135656,
- 2.8364946397857334,
+ 2.836494639785734,
4.676214351225988,
4.880913779574146,
5.266867429226324,
@@ -46364,7 +46346,7 @@
5.028976315029584,
6.165344513418666,
-2.8222319285881468,
- 3.9925309641242204,
+ 3.992530964124221,
5.227701265798802,
5.181943775238127,
4.091767144889039,
@@ -46430,7 +46412,7 @@
5.2079887685551975,
6.670890886506432,
6.41682296059198,
- 3.8418071767568334,
+ 3.841807176756834,
4.866814061240887,
5.484479125899425,
6.226205806721856,
@@ -46462,7 +46444,7 @@
5.933832055443026,
-3.712890142266498,
2.977823792582203,
- 3.8624303738801333,
+ 3.8624303738801338,
5.436041830572839,
5.896960019552019,
5.240538490503975,
@@ -47543,7 +47525,7 @@
" if x >= 0:\n",
" tick_labels.append(f\"${10**x:,.0f}\")\n",
" else:\n",
- " tick_labels.append(f\"-${10**abs(x):,.0f}\")\n",
+ " tick_labels.append(f\"-${10 ** abs(x):,.0f}\")\n",
"\n",
"fig.update_xaxes(\n",
" tickvals=tick_values,\n",
@@ -47668,7 +47650,8 @@
" weight_col=\"wgt\",\n",
" tune_hyperparameters=True, # enable automated hyperparameter tuning\n",
" impute_all=True,\n",
- " preprocessing={\"networth\":\"asinh\",\n",
+ " preprocessing={\n",
+ " \"networth\": \"asinh\",\n",
" },\n",
" force_retrain=True,\n",
")\n",
@@ -47710,13 +47693,13 @@
0.018123686039833317,
0.013748875767821123,
0.019055124107031283,
- 0.025696473980543134,
+ 0.025696473980543137,
0.01924866930591882,
0.02482384317981785,
0.025474641667659437,
0.02192632278197827,
- 0.026021624134663326,
- 0.0075995165668755445,
+ 0.02602162413466333,
+ 0.007599516566875544,
0.01659715507177827,
0.013329211243174837,
0.008088085625984825,
@@ -47789,15 +47772,15 @@
"alignmentgroup": "True",
"error_y": {
"array": [
- 0.36640189764138636,
+ 0.3664018976413864,
0.3506716163976202,
0.3316461815006992,
0.31174603919989097,
0.29185156065606505,
0.2721368789788306,
0.2527163808589779,
- 0.23291548415990318,
- 0.21292055848980854,
+ 0.2329154841599032,
+ 0.21292055848980856,
0.1916979837107699,
0.1701911399010428,
0.15060008534169392,
@@ -47875,18 +47858,18 @@
"array": [
0.17677483861165888,
0.22282434768405687,
- 0.16176361423792673,
+ 0.1617636142379267,
0.23831380034757152,
0.21494482556586733,
0.2804304797139823,
0.2685094170370646,
0.414277426028729,
0.3546966921813161,
- 0.37332303306761533,
+ 0.3733230330676154,
0.356330809811131,
0.2505759392283411,
0.20236976749251676,
- 0.24042738781697762,
+ 0.2404273878169776,
0.2659484489343179,
0.24094675627154183,
0.1445468264663141,
@@ -47940,7 +47923,7 @@
2.6903248044880193,
2.5278325081741593,
2.5453607223826453,
- 2.5136277942150946,
+ 2.513627794215094,
2.568381765731581,
2.4035353488825977,
2.2983093410104147,
@@ -47948,7 +47931,7 @@
2.075129630823423,
1.8251006160501273,
1.493100418484074,
- 1.0734758403637599,
+ 1.07347584036376,
0.6409964323769163
],
"yaxis": "y"
@@ -47967,11 +47950,11 @@
0.009030876830568562,
0.00957738674732371,
0.010274280541972077,
- 0.011093252344146093,
+ 0.011093252344146091,
0.012009352924078868,
0.013002067947356855,
0.014055172973987475,
- 0.015156084814956535,
+ 0.015156084814956537,
0.016295116743246293,
0.01746481192576428,
0.018659404725152224,
@@ -48015,7 +47998,7 @@
],
"xaxis": "x",
"y": [
- 0.49944351036232637,
+ 0.4994435103623264,
0.510441953585877,
0.5214403968094274,
0.532438840032978,
@@ -48058,7 +48041,7 @@
0.1185515538217377,
0.0999524013285133,
0.07832337541805975,
- 0.053099018136740124,
+ 0.05309901813674013,
0.030508562679399633
]
},
@@ -48100,7 +48083,7 @@
"xaxis": "x",
"y": [
2.7900699297711133,
- 3.1162729776491918,
+ 3.116272977649192,
3.253480915633962,
3.2871453167887723,
3.269472543233115,
@@ -48109,11 +48092,11 @@
2.9945104366758137,
2.844936958732362,
2.6749908166664804,
- 2.4866264941775786,
+ 2.486626494177578,
2.2928426661963845,
2.0742968234602968,
1.843024784235416,
- 1.5960042202227183,
+ 1.5960042202227185,
1.3311791707871576,
1.0520775321051636,
0.7492732526223268,
@@ -48157,8 +48140,8 @@
"type": "line",
"x0": -0.5,
"x1": 18.5,
- "y0": 1.8655510318108859,
- "y1": 1.8655510318108859
+ "y0": 1.865551031810886,
+ "y1": 1.865551031810886
},
{
"line": {
@@ -49373,21 +49356,18 @@
}
],
"source": [
- "from microimpute.evaluations.predictor_analysis import progressive_predictor_inclusion, leave_one_out_analysis\n",
+ "from microimpute.evaluations.predictor_analysis import (\n",
+ " progressive_predictor_inclusion,\n",
+ " leave_one_out_analysis,\n",
+ ")\n",
"from microimpute.models import QRF\n",
"\n",
"leave_one_out_results = leave_one_out_analysis(\n",
- " scf_data,\n",
- " predictors,\n",
- " imputed_variables,\n",
- " QRF\n",
+ " scf_data, predictors, imputed_variables, QRF\n",
")\n",
"\n",
"predictor_inclusion_results = progressive_predictor_inclusion(\n",
- " scf_data,\n",
- " predictors,\n",
- " imputed_variables,\n",
- " QRF\n",
+ " scf_data, predictors, imputed_variables, QRF\n",
")"
]
},
@@ -49577,7 +49557,7 @@
"print(f\"Optimal loss: {predictor_inclusion_results['optimal_loss']}\")\n",
"\n",
"# For step-by-step details:\n",
- "print(predictor_inclusion_results['results_df'])"
+ "print(predictor_inclusion_results[\"results_df\"])"
]
},
{
@@ -49624,21 +49604,25 @@
"from microimpute.comparisons.metrics import compare_distributions\n",
"\n",
"for model, imputations in autoimpute_results.imputations.items():\n",
- " print(f\"Model: {model}, distribution similarity: \\n{compare_distributions(\n",
- " donor_data=pd.DataFrame(scf_data[\"networth\"]),\n",
- " receiver_data=pd.DataFrame(imputations[\"networth\"]),\n",
- " donor_weights=scf_data[\"wgt\"],\n",
- " receiver_weights=cps_data[\"household_weight\"],\n",
- " imputed_variables=imputed_variables,\n",
- ")}\")\n",
+ " print(\n",
+ " f\"Model: {model}, distribution similarity: \\n{\n",
+ " compare_distributions(\n",
+ " donor_data=pd.DataFrame(scf_data['networth']),\n",
+ " receiver_data=pd.DataFrame(imputations['networth']),\n",
+ " donor_weights=scf_data['wgt'],\n",
+ " receiver_weights=cps_data['household_weight'],\n",
+ " imputed_variables=imputed_variables,\n",
+ " )\n",
+ " }\"\n",
+ " )\n",
" if model == \"best_method\":\n",
" distribution_comparison_results = compare_distributions(\n",
- " donor_data=pd.DataFrame(scf_data[\"networth\"]),\n",
- " receiver_data=pd.DataFrame(imputations[\"networth\"]),\n",
- " donor_weights=scf_data[\"wgt\"],\n",
- " receiver_weights=cps_data[\"household_weight\"],\n",
- " imputed_variables=imputed_variables,\n",
- " )"
+ " donor_data=pd.DataFrame(scf_data[\"networth\"]),\n",
+ " receiver_data=pd.DataFrame(imputations[\"networth\"]),\n",
+ " donor_weights=scf_data[\"wgt\"],\n",
+ " receiver_weights=cps_data[\"household_weight\"],\n",
+ " imputed_variables=imputed_variables,\n",
+ " )"
]
},
{
@@ -49724,8 +49708,7 @@
" # Custom tick formatting\n",
" tick_values = [-6, -4, -2, 0, 2, 4, 6, 8]\n",
" tick_labels = [\n",
- " \"$\" + format(10**x if x >= 0 else -(10 ** abs(x)), \",.0f\")\n",
- " for x in tick_values\n",
+ " \"$\" + format(10**x if x >= 0 else -(10 ** abs(x)), \",.0f\") for x in tick_values\n",
" ]\n",
"\n",
" # Plot positions for 3x2 grid (row, col)\n",
@@ -49880,11 +49863,7 @@
" # Update layout\n",
" fig.update_layout(\n",
" title=dict(\n",
- " text=(\n",
- " title\n",
- " if title\n",
- " else \"Log-transformed net worth imputations by model\"\n",
- " ),\n",
+ " text=(title if title else \"Log-transformed net worth imputations by model\"),\n",
" font=dict(size=18),\n",
" x=0.5,\n",
" xanchor=\"center\",\n",
@@ -49982,29 +49961,29 @@
}
],
"source": [
- "# --- Access tuned hyperparameters for each model --- \n",
- "for model_name, fitted_model in autoimpute_results.fitted_models.items(): \n",
- " print(f\"\\n{'='*50}\")\n",
+ "# --- Access tuned hyperparameters for each model ---\n",
+ "for model_name, fitted_model in autoimpute_results.fitted_models.items():\n",
+ " print(f\"\\n{'=' * 50}\")\n",
" print(f\"Model: {model_name}\")\n",
- " print(f\"{'='*50}\")\n",
- " \n",
+ " print(f\"{'=' * 50}\")\n",
+ "\n",
" # QRF: params stored on the internal RandomForestQuantileRegressor\n",
- " if hasattr(fitted_model, 'models'):\n",
+ " if hasattr(fitted_model, \"models\"):\n",
" for var_name, m in fitted_model.models.items():\n",
- " if hasattr(m, 'qrf'): # QRF numeric target\n",
+ " if hasattr(m, \"qrf\"): # QRF numeric target\n",
" print(f\" {var_name} (QRF):\")\n",
" print(f\" n_estimators: {m.qrf.n_estimators}\")\n",
" print(f\" min_samples_split: {m.qrf.min_samples_split}\")\n",
" print(f\" min_samples_leaf: {m.qrf.min_samples_leaf}\")\n",
" print(f\" max_features: {m.qrf.max_features}\")\n",
" print(f\" bootstrap: {m.qrf.bootstrap}\")\n",
- " elif hasattr(m, 'num_gaussian'): # MDN\n",
+ " elif hasattr(m, \"num_gaussian\"): # MDN\n",
" print(f\" {var_name} (MDN):\")\n",
" print(f\" num_gaussian: {m.num_gaussian}\")\n",
" print(f\" learning_rate: {m.learning_rate}\")\n",
"\n",
" # Matching: params stored in .hyperparameters dict\n",
- " if hasattr(fitted_model, 'hyperparameters'):\n",
+ " if hasattr(fitted_model, \"hyperparameters\"):\n",
" print(f\" Matching hyperparameters: {fitted_model.hyperparameters}\")"
]
},
@@ -50133,7 +50112,7 @@
5.8756951439926395,
5.223473526669197,
5.854529042490261,
- -3.7221167757048788,
+ -3.7221167757048783,
-4.091767144889039,
-4.080486134479351,
5.132315391745243,
@@ -50192,7 +50171,7 @@
4.0739610394043355,
5.247077224105272,
5.385875221107109,
- 3.9873691104884332,
+ 3.9873691104884337,
-4.16877768925706,
5.333211450568776,
5.187934138927315,
@@ -50230,7 +50209,7 @@
5.577342981532882,
5.009456640451272,
5.725442727923085,
- 3.9530512993203675,
+ 3.953051299320367,
5.441219022795107,
6.320421910482902,
6.1235035153398,
@@ -50291,7 +50270,7 @@
5.582073852437189,
5.082007307599883,
4.980709480819691,
- -3.8803101735257606,
+ -3.880310173525761,
4.115950982771999,
3.5261449522502413,
5.643456891642921,
@@ -50371,7 +50350,7 @@
6.771078543074562,
4.750723847304711,
3.6416293798549706,
- -1.9778237925822026,
+ -1.9778237925822024,
5.2547449246479765,
5.169709318821116,
-4.8350892630882045,
@@ -50463,7 +50442,7 @@
3.935803339675293,
5.745227049432624,
3.6363651398626136,
- 6.0374923741718804,
+ 6.03749237417188,
5.266048169378219,
6.017149765932605,
4.814569758231694,
@@ -50479,7 +50458,7 @@
6.827788888224919,
5.476899623189915,
4.341644618105639,
- 3.8002824671312885,
+ 3.800282467131289,
6.165657744216163,
4.388319838198276,
3.8580019868244104,
@@ -50499,7 +50478,7 @@
4.997170906570135,
2.5917830072098726,
6.153754172035414,
- 7.3509606544357275,
+ 7.350960654435728,
-4.507168580111772,
4.522029686316878,
2.794433294785022,
@@ -50748,9 +50727,9 @@
5.182245263956867,
5.489647758801424,
7.275584303681336,
- 1.9778237925822026,
+ 1.9778237925822024,
2.5007025378625403,
- 3.7328512441187085,
+ 3.732851244118709,
5.759977785419521,
2.779456138815369,
5.7048315682259245,
@@ -50781,7 +50760,7 @@
-4.608025590308054,
-3.614645890169377,
4.880913779574146,
- 3.8364946397857334,
+ 3.836494639785734,
5.277645736057216,
5.678239037792403,
-4.485229851206332,
@@ -50817,7 +50796,7 @@
5.625098066792888,
4.724976552156798,
5.147204287894152,
- 1.0235812831428779,
+ 1.023581283142878,
5.495166337328068,
-3.639005236028822,
5.464647689782141,
@@ -50923,7 +50902,7 @@
4.543409276918597,
5.330756295183223,
3.8431252186847464,
- 3.7899941302552773,
+ 3.7899941302552778,
5.563409841520776,
6.783702068407446,
4.113486394582276,
@@ -50946,7 +50925,7 @@
5.073186895737851,
5.100949188427034,
5.548820506872623,
- 3.8138664471761197,
+ 3.81386644717612,
6.104928590947011,
5.6098309220089195,
-4.130791252790746,
@@ -50990,7 +50969,7 @@
4.001304888431726,
3.8145697582316935,
4.8649407535977325,
- 3.8364946397857334,
+ 3.836494639785734,
5.254413236574706,
3.994857131880983,
3.0235812831428777,
@@ -51038,7 +51017,7 @@
5.208499736695339,
4.938188850913958,
3.64693396468087,
- -3.9670757990489802,
+ -3.96707579904898,
5.072799305813059,
5.685270948463714,
-1.6256412744708402,
@@ -51090,7 +51069,7 @@
6.503875743145884,
4.919837360420066,
4.976862456024701,
- 7.4929024676581255,
+ 7.492902467658125,
5.415926438504082,
5.808982308135265,
4.615758040538744,
@@ -51248,13 +51227,13 @@
5.818069329802047,
5.606212722632514,
5.964844192461827,
- 3.6570497387224643,
+ 3.657049738722464,
6.320027077349274,
-3.829761257126765,
6.8837994678116345,
-5.147040902586216,
4.375377590039902,
- 3.8636873775996357,
+ 3.863687377599636,
5.181460957532031,
4.120491296150934,
6.965390756151716,
@@ -51313,7 +51292,7 @@
5.998461256149809,
5.124984633698209,
-3.5078811224896636,
- 3.6067800571115005,
+ 3.606780057111501,
4.809551408274888,
5.155199947492004,
5.675440552389826,
@@ -51486,7 +51465,7 @@
5.74241500144674,
5.246817556245875,
5.459425649127319,
- 3.9967091367425764,
+ 3.996709136742576,
5.681401739158575,
5.437216280341434,
5.470428993298687,
@@ -51526,7 +51505,7 @@
4.435200989106108,
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- 3.9273222237644165,
+ 3.927322223764417,
6.328502445043769,
3.9373951355265944,
5.613195823774144,
@@ -51537,7 +51516,7 @@
5.673276281634512,
5.486114041177967,
5.988076622697971,
- -3.8803101735257606,
+ -3.880310173525761,
5.0812473870527075,
5.532448879685702,
5.5048232011558005,
@@ -51569,7 +51548,7 @@
5.879033805536015,
6.333094345583303,
4.699084667870834,
- -3.9967091367425764,
+ -3.996709136742576,
6.242799202325842,
4.898352920327176,
5.446827157079686,
@@ -51646,7 +51625,7 @@
5.565784065576906,
6.5153630587270435,
5.308462997798331,
- 3.8159729726411316,
+ 3.815972972641131,
-2.0013048884317257,
5.143826078689243,
6.994207059831172,
@@ -51717,7 +51696,7 @@
4.532111002114164,
5.856090195849114,
5.205281987558839,
- 3.9915969971365195,
+ 3.991596997136519,
4.98807662269797,
6.420390655697259,
5.1230547391570465,
@@ -51904,7 +51883,7 @@
5.063352210074457,
5.899625833388973,
6.039066781297333,
- 7.5263524247261815,
+ 7.526352424726181,
6.435957819646587,
6.117002968305113,
4.219480935552111,
@@ -51913,13 +51892,13 @@
5.1345075254092984,
6.2532631254605535,
4.238689864195971,
- 1.8686793231571346,
+ 1.8686793231571344,
4.3136158945053955,
2.696602190271774,
6.396382858223615,
6.04462579325268,
5.377689722290278,
- 3.6488937341045515,
+ 3.648893734104551,
7.063807704910154,
-3.8692993011095367,
5.208840048439463,
@@ -52113,7 +52092,7 @@
5.5629710652153825,
4.764022928092643,
5.650639747143868,
- 3.8754508838726442,
+ 3.875450883872644,
5.911800103370819,
-4.093619149750633,
5.217262312684159,
@@ -52155,7 +52134,7 @@
5.424464498691241,
5.524297906698357,
5.178522327873205,
- 3.9826226754639715,
+ 3.982622675463971,
5.364025397982997,
4.297508063243403,
6.069295342083746,
@@ -52169,7 +52148,7 @@
4.320246473404409,
5.143298942248372,
5.969984417041933,
- 3.9179527369991156,
+ 3.917952736999115,
4.888685257784005,
4.149712690404862,
5.811466634083803,
@@ -52253,13 +52232,13 @@
6.6796794851557095,
4.550695394782683,
4.91845093988813,
- 3.8418071767568334,
+ 3.841807176756834,
5.439255617709138,
4.044853297093916,
5.872441343985381,
5.861611366128198,
-5.033032178941572,
- 3.8636873775996357,
+ 3.863687377599636,
5.81295043673436,
5.701644188117223,
6.398219667263065,
@@ -52374,13 +52353,13 @@
6.324806667364586,
6.44742508835529,
5.677757825020838,
- 3.6729161418550196,
+ 3.672916141855019,
4.2082727139604765,
5.176175361070348,
5.6899588440763695,
5.532783805473981,
5.620552041074058,
- 3.6729161418550196,
+ 3.672916141855019,
6.171112303074431,
4.2666193318291725,
-4.200511367593383,
@@ -52416,7 +52395,7 @@
4.542095223020765,
5.274101308054993,
5.38326502037923,
- -3.9953208740306563,
+ -3.995320874030656,
6.698992976857741,
6.187338807124834,
4.2588571598299305,
@@ -52539,7 +52518,7 @@
5.192814734443975,
4.243950915594272,
6.586302281741822,
- 3.9826226754639715,
+ 3.982622675463971,
3.9921109275177176,
4.525598497970026,
6.124158585932474,
@@ -52610,7 +52589,7 @@
6.67452957351612,
5.909394657803367,
6.436019989295337,
- 3.6033648797596878,
+ 3.603364879759688,
4.536798883210817,
6.5386864873096675,
4.046009654328365,
@@ -52702,7 +52681,7 @@
5.526758892560938,
4.825670541024611,
6.376205591742779,
- 3.6033648797596878,
+ 3.603364879759688,
-5.04510134725691,
5.907991443393929,
5.946928939640704,
@@ -52826,7 +52805,7 @@
5.737239799351234,
2.695679141078595,
5.184649668614052,
- 3.7137773631713915,
+ 3.713777363171391,
4.919059079418592,
6.704479473383816,
4.879590787452954,
@@ -52840,7 +52819,7 @@
5.36543110722763,
2.438554631113696,
4.914560880132567,
- 3.6729161418550196,
+ 3.672916141855019,
5.611067748553842,
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5.560139725714408,
@@ -52906,7 +52885,7 @@
5.5232683657612816,
-4.743078299753459,
3.737911042888111,
- 3.6519702131931893,
+ 3.6519702131931897,
4.703916796557441,
5.1709483909366645,
5.961099175160224,
@@ -52925,7 +52904,7 @@
5.7246309138720175,
5.636153237208054,
5.86420796779936,
- 3.8002824671312885,
+ 3.800282467131289,
5.3856866024366505,
4.32026840691528,
5.186145689665897,
@@ -53065,7 +53044,7 @@
5.86358067365147,
4.140520929693634,
3.847055512313179,
- 1.9778237925822026,
+ 1.9778237925822024,
6.643581925442946,
5.185308552858898,
5.299122971544187,
@@ -53086,12 +53065,12 @@
3.662070540097515,
5.686022719924412,
6.936440758305233,
- -2.2540302045211518,
+ -2.254030204521152,
-4.160301850299285,
4.117353064641607,
4.970033548155951,
6.231107938606212,
- 3.6067800571115005,
+ 3.606780057111501,
5.378841188670256,
5.74241500144674,
6.180554225039175,
@@ -53169,7 +53148,7 @@
5.629209505150497,
6.002765316817296,
5.894979064630362,
- 3.7002748927677445,
+ 3.700274892767745,
5.978040885554433,
5.367973556827988,
5.555302826374015,
@@ -53216,7 +53195,7 @@
5.159047878774402,
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4.045597022960598,
- 1.9778237925822026,
+ 1.9778237925822024,
4.1703292967735175,
4.139191794817178,
4.8649407535977325,
@@ -53294,7 +53273,7 @@
4.610168587814633,
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6.049787580225996,
- -3.2540302045211518,
+ -3.254030204521152,
5.561401637038913,
7.089067631326432,
4.558875403185648,
@@ -53343,7 +53322,7 @@
5.722551287478897,
3.5409091654372515,
5.031329061143618,
- 3.8159729726411316,
+ 3.815972972641131,
6.7924856503027895,
5.422042132751101,
5.283891228937798,
@@ -53622,7 +53601,7 @@
5.329147596658181,
5.4883688027888144,
2.169709318821116,
- 3.6729161418550196,
+ 3.672916141855019,
-3.7575805696812647,
7.151870671798002,
6.193431985004376,
@@ -53653,7 +53632,7 @@
6.079952462618406,
3.199672542198559,
6.3830373032638645,
- 3.6519702131931893,
+ 3.6519702131931897,
6.093249380054473,
6.470300144873126,
5.307447631616347,
@@ -53686,7 +53665,7 @@
5.142837172420815,
3.0235812831428777,
5.834284144089994,
- 3.6033648797596878,
+ 3.603364879759688,
4.375377590039902,
2.403792524854484,
-5.440088652993328,
@@ -53717,9 +53696,9 @@
6.16973723685474,
-4.478121268107697,
4.983099660115876,
- 3.9787878206848197,
+ 3.97878782068482,
6.05733063740697,
- 3.7420829720101523,
+ 3.742082972010152,
0.02358128314287784,
4.3395516285997955,
5.6992954837648,
@@ -53772,7 +53751,7 @@
4.591783007209873,
-5.164403463252189,
6.010039495880184,
- 3.9826226754639715,
+ 3.982622675463971,
-2.9524889733868305,
5.323524183165645,
5.999059046308752,
@@ -53841,7 +53820,7 @@
4.585874147599353,
6.268970554314088,
5.571970701275796,
- 1.9778237925822026,
+ 1.9778237925822024,
4.235768887546835,
6.150653670262333,
5.793844021313471,
@@ -53940,14 +53919,14 @@
3.166596083396973,
6.043029920636514,
4.828912196285647,
- 3.8159729726411316,
+ 3.815972972641131,
5.9364434122599805,
5.363330764823754,
4.37828502776869,
4.25850331457107,
5.013920137930479,
4.622371789905993,
- 3.9826226754639715,
+ 3.982622675463971,
4.831183953059372,
3.7146627752658463,
5.51494297697715,
@@ -53965,14 +53944,14 @@
5.891796504133174,
6.019874001684199,
5.698075000439228,
- 3.6519702131931893,
+ 3.6519702131931897,
6.021578919550882,
7.085459554097654,
-4.904965939913451,
4.326777340563367,
2.8229218325964593,
5.9872274696277215,
- 1.9778237925822026,
+ 1.9778237925822024,
5.353995056492069,
5.980109874923115,
4.487474272128785,
@@ -54141,7 +54120,7 @@
5.92159302194038,
5.091042716894707,
5.32699335373962,
- 3.7137773631713915,
+ 3.713777363171391,
5.658461423909404,
-5.249381453713423,
-4.381516130143331,
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@@ -278306,7 +278285,7 @@
7.649042932880817,
5.516620871460529,
3.4073966491233088,
- 3.9299163249479685,
+ 3.929916324947969,
5.949357336979625,
5.362635018852018,
6.945491349715413,
@@ -278427,7 +278406,7 @@
3.1908986178910537,
5.832264792271848,
5.349404702145622,
- 3.6190775049684523,
+ 3.619077504968452,
3.321341794242012,
6.848942479095512,
6.084279123496489,
@@ -278500,7 +278479,7 @@
6.212260158892926,
5.866487561856668,
5.3084629977983315,
- 7.2206136020608795,
+ 7.22061360206088,
4.978547014201299,
7.275220963402021,
6.123089276870843,
@@ -278679,7 +278658,7 @@
5.443537031632636,
6.7333090477026465,
5.3084629977983315,
- -3.9198318456045156,
+ -3.919831845604515,
4.280058489384555,
5.842466697736888,
6.44971302278206,
@@ -278813,7 +278792,7 @@
3.7702154820804568,
5.2488905648687405,
6.530032194483201,
- 7.2206136020608795,
+ 7.22061360206088,
3.9680639552930463,
5.490152355529232,
4.922854470460481,
@@ -278856,7 +278835,7 @@
4.34786373844057,
6.555723907863212,
6.310531498930427,
- 3.9631504517987795,
+ 3.963150451798779,
5.92436276974418,
5.237690610359785,
-4.563910757933752,
@@ -278913,7 +278892,7 @@
5.419951642108291,
6.583529780283995,
5.660830783379288,
- 7.2206136020608795,
+ 7.22061360206088,
-3.992064231696813,
6.167501221528715,
6.225251462789459,
@@ -278958,9 +278937,9 @@
5.402233508051379,
5.842466697736888,
4.280058489384555,
- 3.7870092767058154,
+ 3.787009276705815,
3.8437827426285183,
- 3.7527460728356474,
+ 3.752746072835647,
6.227565527563003,
5.574078693079149,
6.254260064289353,
@@ -278977,7 +278956,7 @@
5.913443004401066,
6.32044383513555,
5.606105036932231,
- -3.7575805696812643,
+ -3.757580569681264,
6.1234345030272594,
-4.987652253700833,
6.237059194780936,
@@ -279031,7 +279010,7 @@
5.866487561856668,
4.711010659523228,
6.066939630879419,
- -3.9967091367425764,
+ -3.996709136742576,
6.525300138732842,
6.478037389936247,
-4.384363973016158,
@@ -279088,7 +279067,7 @@
5.625927904111577,
5.989629104219332,
3.341644618105639,
- 3.6190775049684523,
+ 3.619077504968452,
5.637042534395584,
5.768812267671019,
6.024708983419913,
@@ -279128,7 +279107,7 @@
4.060409716519991,
4.937395135526595,
6.411472908075169,
- 1.9778237925822026,
+ 1.9778237925822024,
4.268755127041771,
6.1234345030272594,
6.2848441519353715,
@@ -279232,7 +279211,7 @@
-4.823541810548861,
5.904937404515963,
4.153915051637884,
- 7.2206136020608795,
+ 7.22061360206088,
5.699084667870835,
4.742580921021596,
-4.759977785419521,
@@ -279251,7 +279230,7 @@
6.878263752780333,
4.972134657354552,
5.93047919629231,
- 7.2206136020608795,
+ 7.22061360206088,
6.2848441519353715,
6.411472908075169,
5.217262312684159,
@@ -279288,7 +279267,7 @@
-3.8986425465345778,
6.739960296015108,
3.753555568842433,
- 3.9787878206848197,
+ 3.97878782068482,
5.8002824671312885,
5.70509386978184,
4.839822583134661,
@@ -279397,13 +279376,13 @@
-3.992064231696813,
5.949357336979625,
4.450092544507453,
- 7.2206136020608795,
+ 7.22061360206088,
5.532798595436695,
3.755975042965846,
6.225251462789459,
6.1996725421985595,
6.411472908075169,
- 3.6190775049684523,
+ 3.619077504968452,
6.0333083058315005,
5.142837172420815,
5.771929661407004,
@@ -279426,7 +279405,7 @@
5.506454866751632,
6.31062121066012,
5.342062008317395,
- 3.7870092767058154,
+ 3.787009276705815,
5.556705866113101,
5.104964600605163,
5.8272432063791015,
@@ -279459,7 +279438,7 @@
6.320750664112112,
6.10858628221953,
5.637042534395584,
- 7.2206136020608795,
+ 7.22061360206088,
5.004810770562878,
4.990137172000555,
6.661441943838416,
@@ -279478,12 +279457,12 @@
6.237059194780936,
5.464490365208095,
5.502032336290028,
- 3.9603619168297364,
+ 3.960361916829736,
5.812951319649617,
6.443024823658369,
- -3.9967091367425764,
+ -3.996709136742576,
2.0235812831428777,
- 1.2268068016769327,
+ 1.226806801676933,
5.863687377599636,
5.943704609433602,
6.52670571873672,
@@ -279499,8 +279478,8 @@
6.017722394269001,
5.425842665597558,
5.378879518354046,
- 3.7870092767058154,
- 2.5798837839101654,
+ 3.787009276705815,
+ 2.579883783910166,
5.346454690209442,
5.943443536698416,
6.551056511555086,
@@ -279527,7 +279506,7 @@
6.721726795730526,
6.225251462789459,
6.424233227506816,
- 2.5798837839101654,
+ 2.579883783910166,
5.874411242991409,
6.033456916855037,
6.32044383513555,
@@ -279764,12 +279743,12 @@
5.86437144828587,
4.741750688534185,
5.86437144828587,
- 7.2206136020608795,
+ 7.22061360206088,
5.27926376168876,
6.167532399566841,
6.430547033901826,
-4.095463290449003,
- 3.9631504517987795,
+ 3.963150451798779,
5.598711372304525,
5.863687377599636,
6.989577444147565,
@@ -279794,7 +279773,7 @@
6.102586966356688,
4.729786825024848,
4.923948411799348,
- 7.2206136020608795,
+ 7.22061360206088,
7.303880142141896,
6.4890844523379805,
6.142110540674617,
@@ -279810,7 +279789,7 @@
5.377575166362531,
-2.5007025378625403,
6.810829163474833,
- 3.9744327420314245,
+ 3.974432742031425,
5.828367924912582,
5.801732533526522,
4.02790265692552,
@@ -280016,7 +279995,7 @@
5.922854470460481,
4.647863378978546,
4.744821698211539,
- 3.6256412744708397,
+ 3.62564127447084,
5.799955533876995,
5.927051111650048,
5.801732533526522,
@@ -280048,7 +280027,7 @@
5.933832055443026,
6.373797918536032,
5.346317287612828,
- -3.9198318456045156,
+ -3.919831845604515,
5.340913218588774,
5.034725956491985,
5.642674613769621,
@@ -280116,7 +280095,7 @@
5.635930628088596,
4.466374509082646,
6.274444665328096,
- -3.9967091367425764,
+ -3.996709136742576,
3.3246112788068594,
5.342062008317395,
6.918715338393355,
@@ -281592,8 +281571,10 @@
"\n",
"# Create and show the combined plot\n",
"combined_fig = plot_all_models_log_distributions(\n",
- " scf_data, model_results,\n",
- " donor_weights=scf_data[\"wgt\"], receiver_weights=cps_data[\"household_weight\"],\n",
+ " scf_data,\n",
+ " model_results,\n",
+ " donor_weights=scf_data[\"wgt\"],\n",
+ " receiver_weights=cps_data[\"household_weight\"],\n",
")\n",
"combined_fig.show()"
]
@@ -281657,11 +281638,11 @@
],
"xaxis": "x",
"y": [
- 12204.978922454151,
- 13696.181044781777,
+ 12204.978922454153,
+ 13696.181044781775,
11307.869279127077,
12836.469058776544,
- 12368.478469346193,
+ 12368.478469346192,
13598.10150751803,
16867.98973375356,
18558.349134212065,
@@ -281702,7 +281683,7 @@
"y": [
122665.5909391442,
137008.79938146682,
- 124828.72614090609,
+ 124828.72614090607,
138939.3591996851,
132162.38394891558,
138254.91123788903,
@@ -281743,10 +281724,10 @@
],
"xaxis": "x",
"y": [
- 105315.95917875119,
+ 105315.9591787512,
114976.5208477795,
- 98717.21486930561,
- 130391.18755464435,
+ 98717.2148693056,
+ 130391.18755464436,
131769.00536645658,
152985.28807018616,
178271.67626398135,
@@ -282725,16 +282706,18 @@
" cps_data[wealth_col] = imputations\n",
"\n",
" # Create a temporary dataframe with the imputed values\n",
- " tmp = pd.DataFrame({\n",
- " 'networth': imputations.values.flatten(),\n",
- " 'income_decile': income_deciles.values\n",
- " })\n",
+ " tmp = pd.DataFrame(\n",
+ " {\n",
+ " \"networth\": imputations.values.flatten(),\n",
+ " \"income_decile\": income_deciles.values,\n",
+ " }\n",
+ " )\n",
"\n",
" # Mean wealth in each decile\n",
" out = (\n",
- " tmp.groupby('income_decile')['networth']\n",
- " .median()\n",
- " .reset_index(name='median_wealth')\n",
+ " tmp.groupby(\"income_decile\")[\"networth\"]\n",
+ " .median()\n",
+ " .reset_index(name=\"median_wealth\")\n",
" )\n",
" out[\"Model\"] = model\n",
" decile_means.append(out)\n",
@@ -282814,7 +282797,7 @@
" distribution_comparison_df=distribution_comparison_results,\n",
" predictor_correlations=correlations,\n",
" predictor_importance_df=leave_one_out_results,\n",
- " progressive_inclusion_df=predictor_inclusion_results['results_df'],\n",
+ " progressive_inclusion_df=predictor_inclusion_results[\"results_df\"],\n",
" best_method_name=\"QRF\",\n",
" donor_data=scf_data,\n",
" receiver_data=autoimpute_results.receiver_data,\n",
@@ -282885,9 +282868,7 @@
"total_expenditure = float(ssi.sum())\n",
"total_recipients = float((ssi > 0).astype(float).sum())\n",
"avg_monthly_benefit = (\n",
- " total_expenditure / total_recipients / 12\n",
- " if total_recipients > 0\n",
- " else 0\n",
+ " total_expenditure / total_recipients / 12 if total_recipients > 0 else 0\n",
")\n",
"\n",
"# Administrative totals (SSI Annual Statistical Report, 2024)\n",
@@ -282898,15 +282879,9 @@
"print(\"=\" * 65)\n",
"print(\"SSI BASELINE: No wealth data (all eligible pass resource test)\")\n",
"print(\"=\" * 65)\n",
- "print(\n",
- " f\"{'Metric':<35} {'Simulated':>14} {'Admin Total':>14}\"\n",
- ")\n",
+ "print(f\"{'Metric':<35} {'Simulated':>14} {'Admin Total':>14}\")\n",
"print(\"-\" * 65)\n",
- "print(\n",
- " f\"{'Total recipients':<35} \"\n",
- " f\"{total_recipients:>14,.0f} \"\n",
- " f\"{admin_recipients:>14,}\"\n",
- ")\n",
+ "print(f\"{'Total recipients':<35} {total_recipients:>14,.0f} {admin_recipients:>14,}\")\n",
"print(\n",
" f\"{'Total expenditure ($B)':<35} \"\n",
" f\"{total_expenditure / 1e9:>14.2f} \"\n",
@@ -282919,8 +282894,7 @@
")\n",
"print(\"-\" * 65)\n",
"print(\n",
- " f\"{'Recipient ratio (sim/admin)':<35} \"\n",
- " f\"{total_recipients / admin_recipients:>14.2%}\"\n",
+ " f\"{'Recipient ratio (sim/admin)':<35} {total_recipients / admin_recipients:>14.2%}\"\n",
")\n",
"print(\n",
" f\"{'Expenditure ratio (sim/admin)':<35} \"\n",
@@ -284048,9 +284022,7 @@
" def formula(person, period, parameters):\n",
" p = parameters(period).gov.ssa.ssi\n",
" joint_claim = person(\"ssi_claim_is_joint\", period)\n",
- " personal_resources = person(\n",
- " \"ssi_countable_resources\", period\n",
- " )\n",
+ " personal_resources = person(\"ssi_countable_resources\", period)\n",
" countable_resources = where(\n",
" joint_claim,\n",
" person.marital_unit.sum(personal_resources),\n",
@@ -284076,19 +284048,21 @@
"for model_name, imputations in model_results.items():\n",
" # Map household-level imputed net worth to person-level\n",
" person_df = pd.DataFrame({\"household_id\": person_hh_ids})\n",
- " hh_df = pd.DataFrame({\n",
- " \"household_id\": hh_ids,\n",
- " \"networth\": imputations[\"networth\"].values.flatten(),\n",
- " })\n",
+ " hh_df = pd.DataFrame(\n",
+ " {\n",
+ " \"household_id\": hh_ids,\n",
+ " \"networth\": imputations[\"networth\"].values.flatten(),\n",
+ " }\n",
+ " )\n",
" merged = person_df.merge(hh_df, on=\"household_id\", how=\"left\")\n",
" person_networth = merged[\"networth\"].fillna(0).values\n",
"\n",
" # Assign to household heads only (non-heads get 0);\n",
" # meets_ssi_resource_test uses marital_unit.sum for joint\n",
" # claims, so the head's value propagates correctly.\n",
- " person_resources = np.where(\n",
- " is_head, np.maximum(person_networth, 0), 0.0\n",
- " ).astype(np.float32)\n",
+ " person_resources = np.where(is_head, np.maximum(person_networth, 0), 0.0).astype(\n",
+ " np.float32\n",
+ " )\n",
"\n",
" # Create simulation with reform that uses actual resource logic\n",
" sim = Microsimulation(\n",
@@ -284156,7 +284130,8 @@
"expenditures = [r[\"expenditure\"] / 1e9 for r in ssi_results.values()]\n",
"\n",
"fig = make_subplots(\n",
- " rows=1, cols=2,\n",
+ " rows=1,\n",
+ " cols=2,\n",
" subplot_titles=[\n",
" \"SSI recipients by model\",\n",
" \"SSI expenditure by model ($B)\",\n",
@@ -284166,34 +284141,44 @@
"\n",
"fig.add_trace(\n",
" go.Bar(\n",
- " x=models, y=recipients,\n",
- " marker_color=model_colors[:len(models)],\n",
+ " x=models,\n",
+ " y=recipients,\n",
+ " marker_color=model_colors[: len(models)],\n",
" showlegend=False,\n",
" ),\n",
- " row=1, col=1,\n",
+ " row=1,\n",
+ " col=1,\n",
")\n",
"fig.add_hline(\n",
- " y=admin_recipients, line_dash=\"dash\",\n",
- " line_color=\"black\", line_width=1,\n",
+ " y=admin_recipients,\n",
+ " line_dash=\"dash\",\n",
+ " line_color=\"black\",\n",
+ " line_width=1,\n",
" annotation_text=\"Admin total\",\n",
" annotation_position=\"top right\",\n",
- " row=1, col=1,\n",
+ " row=1,\n",
+ " col=1,\n",
")\n",
"\n",
"fig.add_trace(\n",
" go.Bar(\n",
- " x=models, y=expenditures,\n",
- " marker_color=model_colors[:len(models)],\n",
+ " x=models,\n",
+ " y=expenditures,\n",
+ " marker_color=model_colors[: len(models)],\n",
" showlegend=False,\n",
" ),\n",
- " row=1, col=2,\n",
+ " row=1,\n",
+ " col=2,\n",
")\n",
"fig.add_hline(\n",
- " y=admin_expenditure / 1e9, line_dash=\"dash\",\n",
- " line_color=\"black\", line_width=1,\n",
+ " y=admin_expenditure / 1e9,\n",
+ " line_dash=\"dash\",\n",
+ " line_color=\"black\",\n",
+ " line_width=1,\n",
" annotation_text=\"Admin total\",\n",
" annotation_position=\"top right\",\n",
- " row=1, col=2,\n",
+ " row=1,\n",
+ " col=2,\n",
")\n",
"\n",
"fig.update_layout(\n",
@@ -284278,17 +284263,19 @@
"for model_name, imputations in model_results.items():\n",
" # Map household-level imputed net worth to person-level\n",
" person_df = pd.DataFrame({\"household_id\": person_hh_ids})\n",
- " hh_df = pd.DataFrame({\n",
- " \"household_id\": hh_ids,\n",
- " \"networth\": imputations[\"networth\"].values.flatten(),\n",
- " })\n",
+ " hh_df = pd.DataFrame(\n",
+ " {\n",
+ " \"household_id\": hh_ids,\n",
+ " \"networth\": imputations[\"networth\"].values.flatten(),\n",
+ " }\n",
+ " )\n",
" merged = person_df.merge(hh_df, on=\"household_id\", how=\"left\")\n",
" person_networth = merged[\"networth\"].fillna(0).values\n",
"\n",
" # Assign to household heads only\n",
- " person_resources = np.where(\n",
- " is_head, np.maximum(person_networth, 0), 0.0\n",
- " ).astype(np.float32)\n",
+ " person_resources = np.where(is_head, np.maximum(person_networth, 0), 0.0).astype(\n",
+ " np.float32\n",
+ " )\n",
"\n",
" # Create resource override reform\n",
" resource_reform = make_ssi_resource_reform(person_resources)\n",
@@ -284301,13 +284288,9 @@
" ssi_reform = sim_reform.calculate(\"ssi\", period=2024)\n",
"\n",
" total_exp_reform = float(ssi_reform.sum())\n",
- " total_recip_reform = float(\n",
- " (ssi_reform > 0).astype(float).sum()\n",
- " )\n",
+ " total_recip_reform = float((ssi_reform > 0).astype(float).sum())\n",
" avg_mo_reform = (\n",
- " total_exp_reform / total_recip_reform / 12\n",
- " if total_recip_reform > 0\n",
- " else 0\n",
+ " total_exp_reform / total_recip_reform / 12 if total_recip_reform > 0 else 0\n",
" )\n",
"\n",
" ssi_reform_results[model_name] = {\n",
@@ -284325,13 +284308,9 @@
")\n",
"ssi_baseline_reform = sim_baseline_reform.calculate(\"ssi\", period=2024)\n",
"baseline_reform_exp = float(ssi_baseline_reform.sum())\n",
- "baseline_reform_recip = float(\n",
- " (ssi_baseline_reform > 0).astype(float).sum()\n",
- ")\n",
+ "baseline_reform_recip = float((ssi_baseline_reform > 0).astype(float).sum())\n",
"baseline_reform_avg = (\n",
- " baseline_reform_exp / baseline_reform_recip / 12\n",
- " if baseline_reform_recip > 0\n",
- " else 0\n",
+ " baseline_reform_exp / baseline_reform_recip / 12 if baseline_reform_recip > 0 else 0\n",
")\n",
"\n",
"ssi_reform_results[\"Baseline (no wealth)\"] = {\n",
@@ -284382,14 +284361,10 @@
" reform_impact[model_name] = {\n",
" \"current_recipients\": current[\"recipients\"],\n",
" \"reform_recipients\": reformed[\"recipients\"],\n",
- " \"additional_recipients\": (\n",
- " reformed[\"recipients\"] - current[\"recipients\"]\n",
- " ),\n",
+ " \"additional_recipients\": (reformed[\"recipients\"] - current[\"recipients\"]),\n",
" \"current_expenditure\": current[\"expenditure\"],\n",
" \"reform_expenditure\": reformed[\"expenditure\"],\n",
- " \"additional_expenditure\": (\n",
- " reformed[\"expenditure\"] - current[\"expenditure\"]\n",
- " ),\n",
+ " \"additional_expenditure\": (reformed[\"expenditure\"] - current[\"expenditure\"]),\n",
" \"pct_increase_recipients\": (\n",
" (reformed[\"recipients\"] - current[\"recipients\"])\n",
" / current[\"recipients\"]\n",
@@ -284408,14 +284383,8 @@
"\n",
"# --- Display reform impact table ---\n",
"print(\"=\" * 100)\n",
- "print(\n",
- " \"SSI SAVINGS PENALTY ELIMINATION ACT: \"\n",
- " \"REFORM IMPACT BY IMPUTATION METHOD\"\n",
- ")\n",
- "print(\n",
- " \"Resource limits: $2K/$3K (current) -> \"\n",
- " \"$10K/$20K (reform)\"\n",
- ")\n",
+ "print(\"SSI SAVINGS PENALTY ELIMINATION ACT: REFORM IMPACT BY IMPUTATION METHOD\")\n",
+ "print(\"Resource limits: $2K/$3K (current) -> $10K/$20K (reform)\")\n",
"print(\"=\" * 100)\n",
"print(\n",
" f\"{'Model':<25} \"\n",
@@ -284512,7 +284481,7 @@
15566883.581850216,
8372911.592673004,
12964562.561107814,
- 9783289.028952777,
+ 9783289.028952776,
12713617.619923756
],
"yaxis": "y"
@@ -284573,7 +284542,7 @@
"y": [
124.5603771981509,
75.56197564242918,
- 100.59410156761615,
+ 100.59410156761616,
84.44236978406886,
106.2176830370023
],
@@ -285542,8 +285511,7 @@
"from pathlib import Path\n",
"\n",
"FIGURES_DIR = Path(\n",
- " \"/Users/movil1/Desktop/PYTHONJOBS/PolicyEngine\"\n",
- " \"/microimpute/paper/figures\"\n",
+ " \"/Users/movil1/Desktop/PYTHONJOBS/PolicyEngine/microimpute/paper/figures\"\n",
")\n",
"\n",
"model_colors = {\n",
@@ -285557,9 +285525,7 @@
"\n",
"# Exclude baseline from the main comparison chart since its\n",
"# reform impact is 0 and distorts the scale\n",
- "plot_models = [\n",
- " m for m in reform_impact if m != \"Baseline (no wealth)\"\n",
- "]\n",
+ "plot_models = [m for m in reform_impact if m != \"Baseline (no wealth)\"]\n",
"\n",
"fig = make_subplots(\n",
" rows=1,\n",
@@ -285575,14 +285541,9 @@
"fig.add_trace(\n",
" go.Bar(\n",
" x=plot_models,\n",
- " y=[\n",
- " ssi_results[m][\"recipients\"] for m in plot_models\n",
- " ],\n",
+ " y=[ssi_results[m][\"recipients\"] for m in plot_models],\n",
" name=\"Current law\",\n",
- " marker_color=[\n",
- " model_colors.get(m, \"#999999\")\n",
- " for m in plot_models\n",
- " ],\n",
+ " marker_color=[model_colors.get(m, \"#999999\") for m in plot_models],\n",
" opacity=0.5,\n",
" showlegend=True,\n",
" ),\n",
@@ -285594,15 +285555,9 @@
"fig.add_trace(\n",
" go.Bar(\n",
" x=plot_models,\n",
- " y=[\n",
- " ssi_reform_results[m][\"recipients\"]\n",
- " for m in plot_models\n",
- " ],\n",
+ " y=[ssi_reform_results[m][\"recipients\"] for m in plot_models],\n",
" name=\"Reform ($10K/$20K)\",\n",
- " marker_color=[\n",
- " model_colors.get(m, \"#999999\")\n",
- " for m in plot_models\n",
- " ],\n",
+ " marker_color=[model_colors.get(m, \"#999999\") for m in plot_models],\n",
" opacity=1.0,\n",
" showlegend=True,\n",
" ),\n",
@@ -285626,15 +285581,9 @@
"fig.add_trace(\n",
" go.Bar(\n",
" x=plot_models,\n",
- " y=[\n",
- " ssi_results[m][\"expenditure\"] / 1e9\n",
- " for m in plot_models\n",
- " ],\n",
+ " y=[ssi_results[m][\"expenditure\"] / 1e9 for m in plot_models],\n",
" name=\"Current law\",\n",
- " marker_color=[\n",
- " model_colors.get(m, \"#999999\")\n",
- " for m in plot_models\n",
- " ],\n",
+ " marker_color=[model_colors.get(m, \"#999999\") for m in plot_models],\n",
" opacity=0.5,\n",
" showlegend=False,\n",
" ),\n",
@@ -285646,15 +285595,9 @@
"fig.add_trace(\n",
" go.Bar(\n",
" x=plot_models,\n",
- " y=[\n",
- " ssi_reform_results[m][\"expenditure\"] / 1e9\n",
- " for m in plot_models\n",
- " ],\n",
+ " y=[ssi_reform_results[m][\"expenditure\"] / 1e9 for m in plot_models],\n",
" name=\"Reform ($10K/$20K)\",\n",
- " marker_color=[\n",
- " model_colors.get(m, \"#999999\")\n",
- " for m in plot_models\n",
- " ],\n",
+ " marker_color=[model_colors.get(m, \"#999999\") for m in plot_models],\n",
" opacity=1.0,\n",
" showlegend=False,\n",
" ),\n",
@@ -285674,10 +285617,7 @@
")\n",
"\n",
"fig.update_layout(\n",
- " title=(\n",
- " \"SSI savings penalty elimination act: \"\n",
- " \"Reform impact by imputation method\"\n",
- " ),\n",
+ " title=(\"SSI savings penalty elimination act: Reform impact by imputation method\"),\n",
" width=PLOT_CONFIG[\"width\"] + 200,\n",
" height=PLOT_CONFIG[\"height\"] - 100,\n",
" paper_bgcolor=PLOT_CONFIG[\"paper_bgcolor\"],\n",
diff --git a/pyproject.toml b/pyproject.toml
index 4240fc1..bbedcc4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,12 +36,10 @@ dev = [
"pytest>=8.0.0,<9.0.0",
"pytest-cov>=6.0.0,<7.0.0",
"flake8>=7.0.0,<8.0.0",
- "black>=24.0.0",
- "isort>=5.13.0,<6.0.0",
+ "ruff>=0.9.0",
"mypy>=1.2.3,<2.0.0",
"build>=1.2.0,<2.0.0",
- "linecheck>=0.1.0,<0.3.0", "towncrier>=24.8.0",
-
+ "towncrier>=24.8.0",
]
matching = [
@@ -72,14 +70,6 @@ include-package-data = true
[tool.setuptools.package-data]
"microimpute" = ["**/*"]
-[tool.isort]
-profile = "black"
-line_length = 79
-
-[tool.black]
-line-length = 79
-target-version = ["py312", "py313"]
-
[tool.towncrier]
package = "microimpute"
directory = "changelog.d"
diff --git a/tests/test_autoimpute.py b/tests/test_autoimpute.py
index 8b2ceb8..1118657 100644
--- a/tests/test_autoimpute.py
+++ b/tests/test_autoimpute.py
@@ -63,9 +63,7 @@ def simple_data() -> tuple:
}
)
- receiver = pd.DataFrame(
- {"x1": np.random.randn(50), "x2": np.random.randn(50)}
- )
+ receiver = pd.DataFrame({"x1": np.random.randn(50), "x2": np.random.randn(50)})
return donor, receiver
@@ -111,9 +109,7 @@ def test_autoimpute_basic_structure(
# Check receiver_data structure
assert isinstance(results.receiver_data, pd.DataFrame)
assert len(results.receiver_data) == len(diabetes_receiver)
- assert all(
- var in results.receiver_data.columns for var in imputed_variables
- )
+ assert all(var in results.receiver_data.columns for var in imputed_variables)
# Check cv_results structure - now a dict with dual metrics
assert isinstance(results.cv_results, dict)
@@ -150,9 +146,7 @@ def test_autoimpute_all_models(
assert len(results.imputations) > 2 # At least 2 models + best_method
# Check that different models might produce different results
- model_names = [
- name for name in results.imputations.keys() if name != "best_method"
- ]
+ model_names = [name for name in results.imputations.keys() if name != "best_method"]
if len(model_names) >= 2:
model1_imputations = results.imputations[model_names[0]]
model2_imputations = results.imputations[model_names[1]]
@@ -182,9 +176,7 @@ def test_autoimpute_specific_models(
# Should have best_method and at least one of the specified models
assert "best_method" in results.imputations
# At least one of the specified models should be present
- model_names = [
- name for name in results.imputations.keys() if name != "best_method"
- ]
+ model_names = [name for name in results.imputations.keys() if name != "best_method"]
assert len(model_names) >= 1
# CV results should have both models as dict keys
@@ -295,9 +287,9 @@ def test_autoimpute_best_method_selection(simple_data: tuple) -> None:
assert best_method_name is not None
# Check that best_method key exists in fitted_models
- assert (
- "best_method" in results.fitted_models
- ), "best_method key not found in fitted_models"
+ assert "best_method" in results.fitted_models, (
+ "best_method key not found in fitted_models"
+ )
# Get the actual class name of the selected best method
best_method_instance = results.fitted_models["best_method"]
@@ -307,9 +299,9 @@ def test_autoimpute_best_method_selection(simple_data: tuple) -> None:
)
# Verify that autoimpute selected the model with the lowest loss
- assert (
- actual_best_model_name == best_method_name
- ), f"Expected {best_method_name} to be selected as best, but got {actual_best_model_name}"
+ assert actual_best_model_name == best_method_name, (
+ f"Expected {best_method_name} to be selected as best, but got {actual_best_model_name}"
+ )
# Additionally verify the loss values are consistent
all_losses = []
@@ -320,9 +312,9 @@ def test_autoimpute_best_method_selection(simple_data: tuple) -> None:
# The best method we found should have the minimum loss
if all_losses:
- assert (
- abs(best_loss - min(all_losses)) < 1e-6
- ), f"Best loss {best_loss} doesn't match minimum loss {min(all_losses)}"
+ assert abs(best_loss - min(all_losses)) < 1e-6, (
+ f"Best loss {best_loss} doesn't match minimum loss {min(all_losses)}"
+ )
def test_autoimpute_cv_results_structure(simple_data: tuple) -> None:
@@ -441,11 +433,7 @@ def test_autoimpute_consistency(simple_data: tuple) -> None:
# Compare quantile_loss mean_test values for each model
for model_name in results1.cv_results:
if model_name in results2.cv_results:
- loss1 = results1.cv_results[model_name]["quantile_loss"][
- "mean_test"
- ]
- loss2 = results2.cv_results[model_name]["quantile_loss"][
- "mean_test"
- ]
+ loss1 = results1.cv_results[model_name]["quantile_loss"]["mean_test"]
+ loss2 = results2.cv_results[model_name]["quantile_loss"]["mean_test"]
if not np.isnan(loss1) and not np.isnan(loss2):
np.testing.assert_allclose(loss1, loss2, rtol=0.10)
diff --git a/tests/test_dashboard_formatter.py b/tests/test_dashboard_formatter.py
index f42b3d4..7dfd0f1 100644
--- a/tests/test_dashboard_formatter.py
+++ b/tests/test_dashboard_formatter.py
@@ -208,9 +208,7 @@ def test_empty_inputs_returns_empty_dataframe(self):
def test_output_has_correct_columns(self, sample_autoimpute_result):
"""Test that output has all expected columns."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -232,9 +230,7 @@ def test_all_types_are_valid(
sample_progressive_inclusion,
):
"""Test that all type values are from the valid set."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -249,9 +245,9 @@ def test_all_types_are_valid(
)
unique_types = set(result["type"].unique())
- assert unique_types.issubset(
- VALID_TYPES
- ), f"Invalid types found: {unique_types - VALID_TYPES}"
+ assert unique_types.issubset(VALID_TYPES), (
+ f"Invalid types found: {unique_types - VALID_TYPES}"
+ )
assert len(unique_types) > 0
finally:
Path(output_path).unlink()
@@ -260,9 +256,7 @@ def test_metric_values_are_numeric(
self, sample_autoimpute_result, sample_distribution_comparison
):
"""Test that metric_value column contains numeric values."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -281,9 +275,7 @@ def test_additional_info_is_valid_json(
self, sample_autoimpute_result, sample_progressive_inclusion
):
"""Test that additional_info contains valid JSON."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -309,9 +301,7 @@ class TestFormatCSVBenchmarkLoss:
def test_benchmark_loss_from_autoimpute(self, sample_autoimpute_result):
"""Test benchmark loss formatting from autoimpute results."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -335,20 +325,14 @@ def test_benchmark_loss_from_autoimpute(self, sample_autoimpute_result):
# Check quantile values
quantiles = benchmark_rows["quantile"].unique()
assert "mean" in quantiles # Mean should be present
- numeric_quantiles = [
- q for q in quantiles if isinstance(q, (int, float))
- ]
- assert all(
- 0 <= q <= 1 for q in numeric_quantiles
- ) # Valid quantile range
+ numeric_quantiles = [q for q in quantiles if isinstance(q, (int, float))]
+ assert all(0 <= q <= 1 for q in numeric_quantiles) # Valid quantile range
finally:
Path(output_path).unlink()
def test_best_method_marked_correctly(self, sample_autoimpute_result):
"""Test that best method has correct suffix."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -361,28 +345,18 @@ def test_best_method_marked_correctly(self, sample_autoimpute_result):
benchmark_rows = result[result["type"] == "benchmark_loss"]
# Check that OLS has _best_method suffix
- ols_rows = benchmark_rows[
- benchmark_rows["method"].str.contains("OLS")
- ]
- assert all(
- "_best_method" in method for method in ols_rows["method"]
- )
+ ols_rows = benchmark_rows[benchmark_rows["method"].str.contains("OLS")]
+ assert all("_best_method" in method for method in ols_rows["method"])
# Check that QRF does not have the suffix
- qrf_rows = benchmark_rows[
- benchmark_rows["method"].str.contains("QRF")
- ]
- assert all(
- "_best_method" not in method for method in qrf_rows["method"]
- )
+ qrf_rows = benchmark_rows[benchmark_rows["method"].str.contains("QRF")]
+ assert all("_best_method" not in method for method in qrf_rows["method"])
finally:
Path(output_path).unlink()
def test_metric_std_column_populated(self, sample_autoimpute_result):
"""Test that metric_std column is populated from CV results."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -425,9 +399,7 @@ def test_metric_std_column_populated(self, sample_autoimpute_result):
& (benchmark_rows["split"] == "test")
]
assert len(test_mean_row) == 1
- assert np.isclose(
- test_mean_row.iloc[0]["metric_std"], 0.0025, rtol=1e-10
- )
+ assert np.isclose(test_mean_row.iloc[0]["metric_std"], 0.0025, rtol=1e-10)
finally:
Path(output_path).unlink()
@@ -436,13 +408,9 @@ def test_metric_std_column_populated(self, sample_autoimpute_result):
class TestFormatCSVDistributionDistance:
"""Tests for distribution_distance type formatting."""
- def test_distribution_distance_formatting(
- self, sample_distribution_comparison
- ):
+ def test_distribution_distance_formatting(self, sample_distribution_comparison):
"""Test distribution distance formatting."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -460,8 +428,7 @@ def test_distribution_distance_formatting(
# Check metric name is formatted correctly
assert all(
- metric == "wasserstein_distance"
- for metric in dist_rows["metric_name"]
+ metric == "wasserstein_distance" for metric in dist_rows["metric_name"]
)
# Check split is 'full'
@@ -476,13 +443,9 @@ def test_distribution_distance_formatting(
class TestFormatCSVPredictorCorrelations:
"""Tests for predictor_correlation and predictor_target_mi types."""
- def test_predictor_correlation_formatting(
- self, sample_predictor_correlations
- ):
+ def test_predictor_correlation_formatting(self, sample_predictor_correlations):
"""Test predictor correlation formatting."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -511,13 +474,9 @@ def test_predictor_correlation_formatting(
finally:
Path(output_path).unlink()
- def test_predictor_target_mi_formatting(
- self, sample_predictor_correlations
- ):
+ def test_predictor_target_mi_formatting(self, sample_predictor_correlations):
"""Test predictor-target MI formatting."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -532,9 +491,7 @@ def test_predictor_target_mi_formatting(
assert len(mi_rows) == 6
# Check metric name
- assert all(
- metric == "mutual_info" for metric in mi_rows["metric_name"]
- )
+ assert all(metric == "mutual_info" for metric in mi_rows["metric_name"])
# Check additional_info contains target
for info in mi_rows["additional_info"]:
@@ -548,13 +505,9 @@ def test_predictor_target_mi_formatting(
class TestFormatCSVPredictorImportance:
"""Tests for predictor_importance type formatting."""
- def test_predictor_importance_formatting(
- self, sample_predictor_importance
- ):
+ def test_predictor_importance_formatting(self, sample_predictor_importance):
"""Test predictor importance formatting."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -586,13 +539,9 @@ def test_predictor_importance_formatting(
class TestFormatCSVProgressiveInclusion:
"""Tests for progressive_inclusion type formatting."""
- def test_progressive_inclusion_formatting(
- self, sample_progressive_inclusion
- ):
+ def test_progressive_inclusion_formatting(self, sample_progressive_inclusion):
"""Test progressive inclusion formatting."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -627,20 +576,15 @@ def test_progressive_inclusion_formatting(
prog_rows["metric_name"] == "cumulative_improvement"
]
steps = [
- json.loads(info)["step"]
- for info in cumulative_rows["additional_info"]
+ json.loads(info)["step"] for info in cumulative_rows["additional_info"]
]
assert steps == sorted(steps) # Should be in ascending order
finally:
Path(output_path).unlink()
- def test_progressive_inclusion_step_ordering(
- self, sample_progressive_inclusion
- ):
+ def test_progressive_inclusion_step_ordering(self, sample_progressive_inclusion):
"""Test that steps are correctly numbered and ordered."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -734,12 +678,8 @@ def sample_donor_receiver_data(self):
# Create receiver data (slightly different distributions)
receiver_data = pd.DataFrame(
{
- "numerical_var1": np.random.normal(
- 102, 14, 150
- ), # Shifted mean
- "numerical_var2": np.random.exponential(
- 2.1, 150
- ), # Different rate
+ "numerical_var1": np.random.normal(102, 14, 150), # Shifted mean
+ "numerical_var2": np.random.exponential(2.1, 150), # Different rate
"categorical_var": np.random.choice(
["A", "B", "C"], 150, p=[0.4, 0.4, 0.2]
),
@@ -759,9 +699,7 @@ def test_distribution_bins_created(self, sample_donor_receiver_data):
"categorical_var",
]
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -798,17 +736,13 @@ def test_distribution_bins_created(self, sample_donor_receiver_data):
finally:
Path(output_path).unlink()
- def test_numerical_histogram_heights_match_numpy(
- self, sample_donor_receiver_data
- ):
+ def test_numerical_histogram_heights_match_numpy(self, sample_donor_receiver_data):
"""Test that histogram heights match numpy's histogram output."""
donor_data, receiver_data = sample_donor_receiver_data
var_name = "numerical_var1"
n_bins = 15
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -870,9 +804,7 @@ def test_numerical_histogram_heights_match_numpy(
assert data["bin_index"] == i
# Check bin edges
assert np.isclose(data["bin_start"], bin_edges[i], rtol=1e-10)
- assert np.isclose(
- data["bin_end"], bin_edges[i + 1], rtol=1e-10
- )
+ assert np.isclose(data["bin_end"], bin_edges[i + 1], rtol=1e-10)
# Check heights match numpy's output
assert np.isclose(
data["donor_height"], donor_heights_expected[i], rtol=1e-10
@@ -890,16 +822,12 @@ def test_numerical_histogram_heights_match_numpy(
finally:
Path(output_path).unlink()
- def test_categorical_distribution_proportions(
- self, sample_donor_receiver_data
- ):
+ def test_categorical_distribution_proportions(self, sample_donor_receiver_data):
"""Test that categorical distribution proportions are computed correctly."""
donor_data, receiver_data = sample_donor_receiver_data
var_name = "categorical_var"
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -995,9 +923,7 @@ def test_error_when_variable_missing_from_datasets(self):
receiver_data = pd.DataFrame({"var1": [7, 8, 9], "var2": [10, 11, 12]})
imputed_variables = ["var1", "var3"] # var3 doesn't exist
- with pytest.raises(
- ValueError, match="missing from donor_data: \\['var3'\\]"
- ):
+ with pytest.raises(ValueError, match="missing from donor_data: \\['var3'\\]"):
format_csv(
donor_data=donor_data,
receiver_data=receiver_data,
@@ -1023,9 +949,7 @@ class TestEdgeCases:
def test_none_values_handled_correctly(self):
"""Test that None values for optional parameters work."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -1047,9 +971,7 @@ def test_none_values_handled_correctly(self):
def test_empty_dataframes_handled_correctly(self):
"""Test that empty DataFrames are handled gracefully."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -1068,9 +990,7 @@ def test_empty_dataframes_handled_correctly(self):
def test_mixed_quantile_types(self, sample_autoimpute_result):
"""Test that mixed quantile types (numeric and string) are handled."""
- with tempfile.NamedTemporaryFile(
- mode="w", delete=False, suffix=".csv"
- ) as f:
+ with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".csv") as f:
output_path = f.name
try:
@@ -1082,8 +1002,7 @@ def test_mixed_quantile_types(self, sample_autoimpute_result):
# Check that we have both numeric quantiles and 'mean'
quantiles = result["quantile"].unique()
has_numeric = any(
- isinstance(q, (int, float)) and not pd.isna(q)
- for q in quantiles
+ isinstance(q, (int, float)) and not pd.isna(q) for q in quantiles
)
has_mean = "mean" in quantiles
diff --git a/tests/test_data_preprocessing.py b/tests/test_data_preprocessing.py
index 47b3242..5bb339c 100644
--- a/tests/test_data_preprocessing.py
+++ b/tests/test_data_preprocessing.py
@@ -65,9 +65,7 @@ def test_normalize_preserves_column_names(self):
# Categorical columns should have exact same values
pd.testing.assert_series_equal(normalized_data["race"], data["race"])
- pd.testing.assert_series_equal(
- normalized_data["is_female"], data["is_female"]
- )
+ pd.testing.assert_series_equal(normalized_data["is_female"], data["is_female"])
def test_normalize_correctly_normalizes_numeric_columns(self):
"""Test that numeric columns are normalized with mean=0, std=1."""
@@ -112,9 +110,7 @@ def test_normalize_handles_constant_columns(self):
# Constant columns are detected as numeric_categorical and excluded
# So they should remain unchanged
- pd.testing.assert_series_equal(
- normalized_data["constant"], data["constant"]
- )
+ pd.testing.assert_series_equal(normalized_data["constant"], data["constant"])
# Only varying column should have normalization params
assert "constant" not in norm_params
@@ -177,9 +173,7 @@ def test_preprocess_data_excludes_categoricals_from_normalization(self):
}
)
- result, transform_params = preprocess_data(
- data, full_data=True, normalize=True
- )
+ result, transform_params = preprocess_data(data, full_data=True, normalize=True)
# Extract normalization params from nested dict
norm_params = transform_params["normalization"]
@@ -264,9 +258,7 @@ def test_log_transform_excludes_categorical_columns(self):
pd.testing.assert_series_equal(
log_data["categorical_col"], data["categorical_col"]
)
- pd.testing.assert_series_equal(
- log_data["boolean_col"], data["boolean_col"]
- )
+ pd.testing.assert_series_equal(log_data["boolean_col"], data["boolean_col"])
# Numeric column should be log transformed
assert not np.allclose(
@@ -316,12 +308,8 @@ def test_log_transform_correctly_transforms_numeric_columns(self):
expected_value1 = np.log(data["value1"].values)
expected_value2 = np.log(data["value2"].values)
- np.testing.assert_array_almost_equal(
- log_data["value1"].values, expected_value1
- )
- np.testing.assert_array_almost_equal(
- log_data["value2"].values, expected_value2
- )
+ np.testing.assert_array_almost_equal(log_data["value1"].values, expected_value1)
+ np.testing.assert_array_almost_equal(log_data["value2"].values, expected_value2)
# Check log transform params are stored
assert "value1" in log_params
@@ -440,9 +428,7 @@ def test_unlog_transform_raises_error_for_missing_params(self):
# Only have params for value1, not value2
log_params = {"value1": {}}
- with pytest.raises(
- ValueError, match="Missing log transformation parameters"
- ):
+ with pytest.raises(ValueError, match="Missing log transformation parameters"):
unlog_transform_predictions(imputations, log_params)
@@ -529,9 +515,7 @@ def test_preprocess_data_raises_error_for_both_normalize_and_log(
ValueError,
match="Cannot apply multiple transformations",
):
- preprocess_data(
- data, full_data=True, normalize=True, log_transform=True
- )
+ preprocess_data(data, full_data=True, normalize=True, log_transform=True)
def test_preprocess_data_with_log_transform_and_split(self):
"""Test that preprocess_data correctly splits and log transforms data."""
@@ -795,9 +779,7 @@ def test_error_on_nonexistent_column_normalize(self):
)
with pytest.raises(ValueError, match="not found in data"):
- preprocess_data(
- data, full_data=True, normalize=["income", "nonexistent"]
- )
+ preprocess_data(data, full_data=True, normalize=["income", "nonexistent"])
def test_error_on_nonexistent_column_log_transform(self):
"""Test that error is raised when specifying non-existent column."""
@@ -923,9 +905,7 @@ def test_asinh_transform_excludes_categorical_columns(self):
pd.testing.assert_series_equal(
asinh_data["categorical_col"], data["categorical_col"]
)
- pd.testing.assert_series_equal(
- asinh_data["boolean_col"], data["boolean_col"]
- )
+ pd.testing.assert_series_equal(asinh_data["boolean_col"], data["boolean_col"])
# Numeric column should be asinh transformed
assert not np.allclose(
@@ -998,15 +978,9 @@ def test_asinh_transform_handles_negative_and_zero_values(self):
asinh_data, asinh_params = asinh_transform_data(data)
# Check that transformation is symmetric
- assert np.isclose(
- asinh_data["value"].iloc[0], -asinh_data["value"].iloc[6]
- )
- assert np.isclose(
- asinh_data["value"].iloc[1], -asinh_data["value"].iloc[5]
- )
- assert np.isclose(
- asinh_data["value"].iloc[2], -asinh_data["value"].iloc[4]
- )
+ assert np.isclose(asinh_data["value"].iloc[0], -asinh_data["value"].iloc[6])
+ assert np.isclose(asinh_data["value"].iloc[1], -asinh_data["value"].iloc[5])
+ assert np.isclose(asinh_data["value"].iloc[2], -asinh_data["value"].iloc[4])
assert np.isclose(asinh_data["value"].iloc[3], 0.0)
def test_asinh_transform_returns_copy(self):
@@ -1061,9 +1035,7 @@ def test_un_asinh_transform_reverses_asinh_transform(self):
imputations = {0.5: asinh_data}
# Reverse asinh transform
- reversed_data = un_asinh_transform_predictions(
- imputations, asinh_params
- )
+ reversed_data = un_asinh_transform_predictions(imputations, asinh_params)
# Should match original data
pd.testing.assert_frame_equal(
@@ -1084,9 +1056,7 @@ def test_un_asinh_transform_raises_error_for_missing_params(self):
# Only have params for value1, not value2
asinh_params = {"value1": {}}
- with pytest.raises(
- ValueError, match="Missing asinh transformation parameters"
- ):
+ with pytest.raises(ValueError, match="Missing asinh transformation parameters"):
un_asinh_transform_predictions(imputations, asinh_params)
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index ca416c8..f6867e3 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -72,38 +72,26 @@ def test_metric_detection_numerical() -> None:
"""Test that numerical variables are correctly identified."""
# Continuous numerical data
numerical_series = pd.Series(np.random.randn(100))
- assert (
- get_metric_for_variable_type(numerical_series, "num_var")
- == "quantile_loss"
- )
+ assert get_metric_for_variable_type(numerical_series, "num_var") == "quantile_loss"
# Integer numerical data with high cardinality
int_series = pd.Series(np.random.randint(0, 100, size=100))
- assert (
- get_metric_for_variable_type(int_series, "int_var") == "quantile_loss"
- )
+ assert get_metric_for_variable_type(int_series, "int_var") == "quantile_loss"
def test_metric_detection_categorical() -> None:
"""Test that categorical variables are correctly identified."""
# Binary data
binary_series = pd.Series([0, 1, 0, 1, 1, 0, 1, 0])
- assert (
- get_metric_for_variable_type(binary_series, "binary_var") == "log_loss"
- )
+ assert get_metric_for_variable_type(binary_series, "binary_var") == "log_loss"
# String categorical
string_series = pd.Series(["A", "B", "C", "A", "B", "C"])
- assert (
- get_metric_for_variable_type(string_series, "string_var") == "log_loss"
- )
+ assert get_metric_for_variable_type(string_series, "string_var") == "log_loss"
# Low cardinality integer (categorical-like)
low_card_series = pd.Series([0, 1, 2, 0, 1, 2, 0, 1, 2])
- assert (
- get_metric_for_variable_type(low_card_series, "low_card_var")
- == "log_loss"
- )
+ assert get_metric_for_variable_type(low_card_series, "low_card_var") == "log_loss"
# Boolean type
bool_series = pd.Series([True, False, True, False, True])
@@ -207,9 +195,9 @@ def test_compare_metrics_mixed_types(split_mixed_data: tuple) -> None:
assert "log_loss" in metrics_used
# Check correct metric assignment
- num_target_metrics = results_df[
- results_df["Imputed Variable"] == "num_target1"
- ]["Metric"].unique()
+ num_target_metrics = results_df[results_df["Imputed Variable"] == "num_target1"][
+ "Metric"
+ ].unique()
assert len(num_target_metrics) == 1
assert num_target_metrics[0] == "quantile_loss"
@@ -257,9 +245,7 @@ def test_compare_metrics_all_categorical(split_mixed_data: tuple) -> None:
)
Y_test = test_data[categorical_targets]
- results_df = compare_metrics(
- Y_test, method_imputations, categorical_targets
- )
+ results_df = compare_metrics(Y_test, method_imputations, categorical_targets)
# Should only have log loss
assert all(results_df["Metric"].isin(["log_loss"]))
@@ -559,9 +545,7 @@ def test_log_loss_constant_across_quantiles(split_mixed_data: tuple) -> None:
)
Y_test = test_data[categorical_targets]
- results_df = compare_metrics(
- Y_test, method_imputations, categorical_targets
- )
+ results_df = compare_metrics(Y_test, method_imputations, categorical_targets)
# Filter to log loss results for the categorical variable
log_loss_results = results_df[
@@ -572,18 +556,16 @@ def test_log_loss_constant_across_quantiles(split_mixed_data: tuple) -> None:
# Get losses at different quantiles
losses_by_quantile = {}
for q in QUANTILES:
- q_loss = log_loss_results[log_loss_results["Percentile"] == q][
- "Loss"
- ].values
+ q_loss = log_loss_results[log_loss_results["Percentile"] == q]["Loss"].values
if len(q_loss) > 0:
losses_by_quantile[q] = q_loss[0]
# All quantiles should have the same log loss
if len(losses_by_quantile) > 1:
loss_values = list(losses_by_quantile.values())
- assert np.allclose(
- loss_values, loss_values[0], rtol=1e-10
- ), "Log loss should be constant across quantiles"
+ assert np.allclose(loss_values, loss_values[0], rtol=1e-10), (
+ "Log loss should be constant across quantiles"
+ )
def test_empty_variable_lists() -> None:
@@ -596,30 +578,20 @@ def test_empty_variable_lists() -> None:
}
# Should raise an error when no variables to evaluate with 'auto'
- with pytest.raises(
- ValueError, match="No variables compatible with any model"
- ):
+ with pytest.raises(ValueError, match="No variables compatible with any model"):
select_best_model_dual_metrics(method_results, metric_priority="auto")
# Should raise error with 'numerical' priority
with pytest.raises(ValueError, match="No numerical variables found"):
- select_best_model_dual_metrics(
- method_results, metric_priority="numerical"
- )
+ select_best_model_dual_metrics(method_results, metric_priority="numerical")
# Should raise error with 'categorical' priority
with pytest.raises(ValueError, match="No categorical variables found"):
- select_best_model_dual_metrics(
- method_results, metric_priority="categorical"
- )
+ select_best_model_dual_metrics(method_results, metric_priority="categorical")
# Should raise error with 'combined' priority
- with pytest.raises(
- ValueError, match="No variables available for evaluation"
- ):
- select_best_model_dual_metrics(
- method_results, metric_priority="combined"
- )
+ with pytest.raises(ValueError, match="No variables available for evaluation"):
+ select_best_model_dual_metrics(method_results, metric_priority="combined")
def test_quantreg_with_numerical_only(split_mixed_data: tuple) -> None:
@@ -748,9 +720,9 @@ def test_categorical_probabilities_in_cross_validation() -> None:
# This threshold distinguishes between using real probabilities vs dummy ones
# Real probabilities should give lower log loss
- assert (
- test_loss < 2.0
- ), f"Log loss {test_loss} suggests dummy probabilities are being used instead of real ones"
+ assert test_loss < 2.0, (
+ f"Log loss {test_loss} suggests dummy probabilities are being used instead of real ones"
+ )
def test_probability_ordering() -> None:
@@ -785,17 +757,17 @@ def test_probability_ordering() -> None:
)
# Correct alphabetical order: A, B, C
- probs_correct_order, alphabetical_labels = (
- order_probabilities_alphabetically(probs_wrong_order, labels_wrong)
+ probs_correct_order, alphabetical_labels = order_probabilities_alphabetically(
+ probs_wrong_order, labels_wrong
)
_, loss_correct = compute_loss(
y_true, probs_correct_order, "log_loss", labels=alphabetical_labels
)
# The correctly ordered probabilities should give much lower loss
- assert (
- loss_correct < loss_wrong
- ), "Alphabetical ordering of probabilities is not working correctly"
+ assert loss_correct < loss_wrong, (
+ "Alphabetical ordering of probabilities is not working correctly"
+ )
# Check that labels are alphabetically ordered
assert list(alphabetical_labels) == sorted(alphabetical_labels)
@@ -829,18 +801,16 @@ def test_ols_returns_probabilities_for_categorical() -> None:
predictions = fitted.predict(test_data, quantiles=[0.5], return_probs=True)
# Check that probabilities are returned
- assert (
- "probabilities" in predictions
- ), "Model should return probabilities when return_probs=True"
- assert (
- "cat_target" in predictions["probabilities"]
- ), "Probabilities should include categorical variable"
+ assert "probabilities" in predictions, (
+ "Model should return probabilities when return_probs=True"
+ )
+ assert "cat_target" in predictions["probabilities"], (
+ "Probabilities should include categorical variable"
+ )
# Check probability structure
prob_info = predictions["probabilities"]["cat_target"]
- assert isinstance(
- prob_info, dict
- ), "Probability info should be a dictionary"
+ assert isinstance(prob_info, dict), "Probability info should be a dictionary"
assert "probabilities" in prob_info, "Should contain probabilities array"
assert "classes" in prob_info, "Should contain classes array"
@@ -848,15 +818,13 @@ def test_ols_returns_probabilities_for_categorical() -> None:
classes = prob_info["classes"]
# Check shapes
- assert probs.shape[0] == len(
- test_data
- ), "Should have probabilities for each test sample"
- assert probs.shape[1] == len(
- np.unique(df["cat_target"])
- ), "Should have probability for each class"
- assert len(classes) == len(
- np.unique(df["cat_target"])
- ), "Should have all classes"
+ assert probs.shape[0] == len(test_data), (
+ "Should have probabilities for each test sample"
+ )
+ assert probs.shape[1] == len(np.unique(df["cat_target"])), (
+ "Should have probability for each class"
+ )
+ assert len(classes) == len(np.unique(df["cat_target"])), "Should have all classes"
# Check that probabilities sum to 1
prob_sums = probs.sum(axis=1)
@@ -875,9 +843,7 @@ def test_qrf_returns_probabilities_for_categorical() -> None:
{
"x1": np.random.randn(n_samples),
"x2": np.random.randn(n_samples),
- "cat_target": np.random.choice(
- ["Apple", "Banana", "Cherry"], n_samples
- ),
+ "cat_target": np.random.choice(["Apple", "Banana", "Cherry"], n_samples),
}
)
@@ -895,18 +861,16 @@ def test_qrf_returns_probabilities_for_categorical() -> None:
predictions = fitted.predict(test_data, quantiles=[0.5], return_probs=True)
# Check that probabilities are returned
- assert (
- "probabilities" in predictions
- ), "Model should return probabilities when return_probs=True"
- assert (
- "cat_target" in predictions["probabilities"]
- ), "Probabilities should include categorical variable"
+ assert "probabilities" in predictions, (
+ "Model should return probabilities when return_probs=True"
+ )
+ assert "cat_target" in predictions["probabilities"], (
+ "Probabilities should include categorical variable"
+ )
# Check probability structure
prob_info = predictions["probabilities"]["cat_target"]
- assert isinstance(
- prob_info, dict
- ), "Probability info should be a dictionary"
+ assert isinstance(prob_info, dict), "Probability info should be a dictionary"
assert "probabilities" in prob_info, "Should contain probabilities array"
assert "classes" in prob_info, "Should contain classes array"
@@ -914,20 +878,18 @@ def test_qrf_returns_probabilities_for_categorical() -> None:
classes = prob_info["classes"]
# Check that we have the original string labels, not encoded values
- assert all(
- isinstance(c, str) for c in classes
- ), "Classes should be original string labels"
- assert set(classes) == set(
- df["cat_target"].unique()
- ), "Should have all original class labels"
+ assert all(isinstance(c, str) for c in classes), (
+ "Classes should be original string labels"
+ )
+ assert set(classes) == set(df["cat_target"].unique()), (
+ "Should have all original class labels"
+ )
# Check shapes
- assert probs.shape[0] == len(
- test_data
- ), "Should have probabilities for each test sample"
- assert probs.shape[1] == len(
- classes
- ), "Should have probability for each class"
+ assert probs.shape[0] == len(test_data), (
+ "Should have probabilities for each test sample"
+ )
+ assert probs.shape[1] == len(classes), "Should have probability for each class"
# Check that probabilities sum to 1
prob_sums = probs.sum(axis=1)
@@ -962,30 +924,25 @@ def test_probability_ordering_with_real_model() -> None:
# Fit model
model = OLS()
- fitted = model.fit(
- train_df, predictors=["x1", "x2"], imputed_variables=["target"]
- )
+ fitted = model.fit(train_df, predictors=["x1", "x2"], imputed_variables=["target"])
# Get predictions with probabilities
predictions = fitted.predict(test_df, quantiles=[0.5], return_probs=True)
- if (
- "probabilities" in predictions
- and "target" in predictions["probabilities"]
- ):
+ if "probabilities" in predictions and "target" in predictions["probabilities"]:
prob_info = predictions["probabilities"]["target"]
probs = prob_info["probabilities"]
model_classes = prob_info["classes"]
# Test ordering function
- probs_ordered, alphabetical_labels = (
- order_probabilities_alphabetically(probs, model_classes)
+ probs_ordered, alphabetical_labels = order_probabilities_alphabetically(
+ probs, model_classes
)
# Check that labels are alphabetical
- assert list(alphabetical_labels) == sorted(
- alphabetical_labels
- ), "Labels should be alphabetically ordered"
+ assert list(alphabetical_labels) == sorted(alphabetical_labels), (
+ "Labels should be alphabetically ordered"
+ )
# Compute log loss with ordered probabilities
y_test = test_df["target"].values
@@ -1006,9 +963,9 @@ def test_probability_ordering_with_real_model() -> None:
_, loss_dummy = compute_loss(y_test, class_preds, "log_loss")
# Real probabilities should give better (lower) loss than dummy probabilities
- assert (
- loss_ordered < loss_dummy
- ), "Real probabilities should give better loss than dummy probabilities"
+ assert loss_ordered < loss_dummy, (
+ "Real probabilities should give better loss than dummy probabilities"
+ )
# === Distribution Comparison Tests ===
@@ -1018,9 +975,9 @@ def test_kl_divergence_identical() -> None:
"""Test KL divergence between identical distributions."""
values = np.array(["A", "B", "C", "A", "B", "C"])
kl = kl_divergence(values, values)
- assert np.isclose(
- kl, 0.0, atol=1e-10
- ), "KL divergence should be 0 for identical distributions"
+ assert np.isclose(kl, 0.0, atol=1e-10), (
+ "KL divergence should be 0 for identical distributions"
+ )
def test_kl_divergence_disjoint() -> None:
@@ -1029,9 +986,7 @@ def test_kl_divergence_disjoint() -> None:
receiver = np.array(["B", "B", "B", "B"])
kl = kl_divergence(donor, receiver)
# KL divergence should be very large (approaching infinity) for disjoint distributions
- assert (
- kl > 10
- ), "KL divergence should be very large for disjoint distributions"
+ assert kl > 10, "KL divergence should be very large for disjoint distributions"
def test_kl_divergence_partial_overlap() -> None:
@@ -1045,12 +1000,10 @@ def test_kl_divergence_partial_overlap() -> None:
# = 0.75*log(1.5) + 0.25*log(0.5)
# ≈ 0.75*0.405 + 0.25*(-0.693)
# ≈ 0.304 - 0.173 ≈ 0.131
- assert (
- kl > 0
- ), "KL divergence should be positive for different distributions"
- assert (
- kl < 1
- ), f"KL divergence should be reasonable for similar distributions, got {kl}"
+ assert kl > 0, "KL divergence should be positive for different distributions"
+ assert kl < 1, (
+ f"KL divergence should be reasonable for similar distributions, got {kl}"
+ )
def test_kl_divergence_different_categories() -> None:
@@ -1061,9 +1014,9 @@ def test_kl_divergence_different_categories() -> None:
# Donor: A=0.5, B=0.5, C=0.0
# Receiver: A=0.0+ε, B=0.5, C=0.5 (ε added to avoid log(0))
# KL divergence should be large due to A having 0 probability in receiver
- assert (
- kl > 5
- ), f"KL divergence should be large when categories are missing, got {kl}"
+ assert kl > 5, (
+ f"KL divergence should be large when categories are missing, got {kl}"
+ )
def test_kl_divergence_empty_input() -> None:
@@ -1113,9 +1066,7 @@ def test_compare_distributions_categorical_only() -> None:
# Create donor data
donor = pd.DataFrame(
{
- "region": np.random.choice(
- ["North", "South", "East", "West"], 100
- ),
+ "region": np.random.choice(["North", "South", "East", "West"], 100),
"status": np.random.choice(["Active", "Inactive"], 100),
}
)
@@ -1126,9 +1077,7 @@ def test_compare_distributions_categorical_only() -> None:
"region": np.random.choice(
["North", "South", "East", "West"], 100, p=[0.4, 0.3, 0.2, 0.1]
),
- "status": np.random.choice(
- ["Active", "Inactive"], 100, p=[0.7, 0.3]
- ),
+ "status": np.random.choice(["Active", "Inactive"], 100, p=[0.7, 0.3]),
}
)
@@ -1159,9 +1108,7 @@ def test_compare_distributions_mixed_types() -> None:
receiver = pd.DataFrame(
{
"income": np.random.normal(51000, 10000, 100),
- "region": np.random.choice(
- ["A", "B", "C"], 100, p=[0.5, 0.3, 0.2]
- ),
+ "region": np.random.choice(["A", "B", "C"], 100, p=[0.5, 0.3, 0.2]),
"age": np.random.normal(40.5, 10, 100),
"employed": np.random.choice([True, False], 100, p=[0.8, 0.2]),
}
@@ -1305,29 +1252,25 @@ def test_kl_divergence_with_weights() -> None:
# Without weights: both distributions are 50% A, 50% B
kl_unweighted = kl_divergence(donor, receiver)
- assert np.isclose(
- kl_unweighted, 0.0, atol=1e-10
- ), "Unweighted identical distributions should have KL=0"
+ assert np.isclose(kl_unweighted, 0.0, atol=1e-10), (
+ "Unweighted identical distributions should have KL=0"
+ )
# With weights: donor becomes 80% A, 20% B; receiver stays 50% A, 50% B
- donor_weights = np.array(
- [4.0, 4.0, 1.0, 1.0]
- ) # A weighted 8, B weighted 2
- kl_weighted_donor = kl_divergence(
- donor, receiver, donor_weights=donor_weights
+ donor_weights = np.array([4.0, 4.0, 1.0, 1.0]) # A weighted 8, B weighted 2
+ kl_weighted_donor = kl_divergence(donor, receiver, donor_weights=donor_weights)
+ assert kl_weighted_donor > 0, (
+ "Weighted donor with different distribution should have KL > 0"
)
- assert (
- kl_weighted_donor > 0
- ), "Weighted donor with different distribution should have KL > 0"
# With receiver weights: donor 50/50, receiver becomes 80% A, 20% B
receiver_weights = np.array([4.0, 4.0, 1.0, 1.0])
kl_weighted_receiver = kl_divergence(
donor, receiver, receiver_weights=receiver_weights
)
- assert (
- kl_weighted_receiver > 0
- ), "Weighted receiver with different distribution should have KL > 0"
+ assert kl_weighted_receiver > 0, (
+ "Weighted receiver with different distribution should have KL > 0"
+ )
def test_kl_divergence_weights_symmetry() -> None:
@@ -1348,9 +1291,9 @@ def test_kl_divergence_weights_symmetry() -> None:
donor_weights=donor_weights,
receiver_weights=receiver_weights,
)
- assert np.isclose(
- kl, 0.0, atol=1e-10
- ), "Identically weighted distributions should have KL=0"
+ assert np.isclose(kl, 0.0, atol=1e-10), (
+ "Identically weighted distributions should have KL=0"
+ )
def test_compare_distributions_with_weights() -> None:
@@ -1374,9 +1317,7 @@ def test_compare_distributions_with_weights() -> None:
receiver_weights = np.random.uniform(0.5, 2.0, 100)
# Unweighted comparison
- results_unweighted = compare_distributions(
- donor, receiver, ["income", "region"]
- )
+ results_unweighted = compare_distributions(donor, receiver, ["income", "region"])
# Weighted comparison
results_weighted = compare_distributions(
@@ -1393,12 +1334,12 @@ def test_compare_distributions_with_weights() -> None:
# Results should be different (weights should affect the computation)
# Get income distances
- income_unweighted = results_unweighted[
- results_unweighted["Variable"] == "income"
- ]["Distance"].values[0]
- income_weighted = results_weighted[
- results_weighted["Variable"] == "income"
- ]["Distance"].values[0]
+ income_unweighted = results_unweighted[results_unweighted["Variable"] == "income"][
+ "Distance"
+ ].values[0]
+ income_weighted = results_weighted[results_weighted["Variable"] == "income"][
+ "Distance"
+ ].values[0]
# With random weights, results should typically differ
# (though not guaranteed, so we just check they're both valid)
@@ -1424,9 +1365,7 @@ def test_compare_distributions_donor_weight_only() -> None:
)
# Should work with only donor weights
- results = compare_distributions(
- donor, receiver, ["x"], donor_weights=donor_weights
- )
+ results = compare_distributions(donor, receiver, ["x"], donor_weights=donor_weights)
assert len(results) == 1
assert results["Variable"].values[0] == "x"
@@ -1517,9 +1456,9 @@ def test_compare_distributions_weights_affect_wasserstein() -> None:
donor_weights=donor_weights,
receiver_weights=receiver_weights,
)
- assert np.isclose(
- results_identical["Distance"].values[0], 0.0, atol=1e-10
- ), "Identical weighted distributions should have distance=0"
+ assert np.isclose(results_identical["Distance"].values[0], 0.0, atol=1e-10), (
+ "Identical weighted distributions should have distance=0"
+ )
# Now change receiver weights to shift distribution toward higher values
receiver_shifted_weights = np.array(
@@ -1533,9 +1472,9 @@ def test_compare_distributions_weights_affect_wasserstein() -> None:
donor_weights=donor_weights,
receiver_weights=receiver_shifted_weights,
)
- assert (
- results_shifted["Distance"].values[0] > 0
- ), "Different weighted distributions should have distance > 0"
+ assert results_shifted["Distance"].values[0] > 0, (
+ "Different weighted distributions should have distance > 0"
+ )
def test_compare_distributions_weights_affect_kl() -> None:
@@ -1563,16 +1502,14 @@ def test_compare_distributions_weights_affect_kl() -> None:
donor_weights=donor_weights,
receiver_weights=receiver_weights,
)
- assert np.isclose(
- results_identical["Distance"].values[0], 0.0, atol=1e-10
- ), "Identical weighted distributions should have KL=0"
+ assert np.isclose(results_identical["Distance"].values[0], 0.0, atol=1e-10), (
+ "Identical weighted distributions should have KL=0"
+ )
# Now change weights to create different distributions
# Donor: 50% A, 50% B (equal weights)
# Receiver: 90% A, 10% B (by weights)
- receiver_shifted_weights = np.array(
- [4.5, 4.5, 0.5, 0.5]
- ) # 90% weight on A
+ receiver_shifted_weights = np.array([4.5, 4.5, 0.5, 0.5]) # 90% weight on A
results_shifted = compare_distributions(
donor,
@@ -1581,9 +1518,9 @@ def test_compare_distributions_weights_affect_kl() -> None:
donor_weights=donor_weights,
receiver_weights=receiver_shifted_weights,
)
- assert (
- results_shifted["Distance"].values[0] > 0
- ), "Different weighted distributions should have KL > 0"
+ assert results_shifted["Distance"].values[0] > 0, (
+ "Different weighted distributions should have KL > 0"
+ )
def test_compare_distributions_weight_length_mismatch() -> None:
diff --git a/tests/test_models/test_imputers.py b/tests/test_models/test_imputers.py
index 7c50ef0..9e43b49 100644
--- a/tests/test_models/test_imputers.py
+++ b/tests/test_models/test_imputers.py
@@ -57,9 +57,7 @@ def data_with_edge_cases() -> pd.DataFrame:
"constant": np.ones(n_samples), # Constant predictor
"binary": np.random.choice([0, 1], n_samples),
"categorical": np.random.choice(["A", "B", "C"], n_samples),
- "high_correlation": np.random.randn(
- n_samples
- ), # Will be made correlated
+ "high_correlation": np.random.randn(n_samples), # Will be made correlated
"target": np.random.randn(n_samples),
}
)
@@ -95,12 +93,12 @@ def data_with_edge_cases() -> pd.DataFrame:
def test_init_signatures(model_class: Type[Imputer]) -> None:
"""Test that all models can be initialized without required arguments."""
model = model_class()
- assert (
- model.predictors is None
- ), f"{model_class.__name__} should initialize predictors as None"
- assert (
- model.imputed_variables is None
- ), f"{model_class.__name__} should initialize imputed_variables as None"
+ assert model.predictors is None, (
+ f"{model_class.__name__} should initialize predictors as None"
+ )
+ assert model.imputed_variables is None, (
+ f"{model_class.__name__} should initialize imputed_variables as None"
+ )
@pytest.mark.parametrize(
@@ -131,9 +129,9 @@ def test_fit_predict_interface(
predictions = fitted_model.predict(X_test, quantiles)
# Check prediction format
- assert isinstance(
- predictions, dict
- ), f"{model_class.__name__} predict should return a dictionary"
+ assert isinstance(predictions, dict), (
+ f"{model_class.__name__} predict should return a dictionary"
+ )
assert set(predictions.keys()).issubset(set(quantiles))
# Check prediction shape
@@ -279,9 +277,7 @@ def test_imputation_categorical_targets(
# Check that we still get the categorical predictions
assert isinstance(predictions_with_probs[0.5], pd.DataFrame)
- assert pd.api.types.is_string_dtype(
- predictions_with_probs[0.5]["categorical"]
- )
+ assert pd.api.types.is_string_dtype(predictions_with_probs[0.5]["categorical"])
# Check probability format
prob_info = predictions_with_probs["probabilities"]["categorical"]
@@ -402,9 +398,7 @@ def test_multiple_targets(
model = model_class()
if model_class.__name__ == "QuantReg":
- fitted = model.fit(
- X_train, predictors, imputed_variables, quantiles=[0.5]
- )
+ fitted = model.fit(X_train, predictors, imputed_variables, quantiles=[0.5])
else:
fitted = model.fit(X_train, predictors, imputed_variables)
@@ -537,9 +531,7 @@ def test_weighted_training(
quantiles=QUANTILES,
)
else:
- fitted = model.fit(
- X_train, predictors, imputed_variables, weight_col="wgt"
- )
+ fitted = model.fit(X_train, predictors, imputed_variables, weight_col="wgt")
assert fitted is not None
@@ -570,9 +562,7 @@ def test_extreme_quantiles(
model = model_class()
if model_class.__name__ == "QuantReg":
- fitted = model.fit(
- X_train, ["x1", "x2"], ["y"], quantiles=extreme_quantiles
- )
+ fitted = model.fit(X_train, ["x1", "x2"], ["y"], quantiles=extreme_quantiles)
else:
fitted = model.fit(X_train, ["x1", "x2"], ["y"])
@@ -586,9 +576,7 @@ def test_extreme_quantiles(
@pytest.mark.parametrize(
"model_class", ALL_IMPUTER_MODELS, ids=lambda cls: cls.__name__
)
-def test_single_quantile(
- model_class: Type[Imputer], simple_data: pd.DataFrame
-) -> None:
+def test_single_quantile(model_class: Type[Imputer], simple_data: pd.DataFrame) -> None:
"""Test models with a single quantile."""
X_train, X_test = preprocess_data(simple_data)
@@ -612,9 +600,7 @@ def test_single_quantile(
def test_string_column_validation() -> None:
"""Test that the _validate_data method handles string columns appropriately."""
- data = pd.DataFrame(
- {"numeric_col": [1, 2, 3], "string_col": ["a", "b", "c"]}
- )
+ data = pd.DataFrame({"numeric_col": [1, 2, 3], "string_col": ["a", "b", "c"]})
columns = ["numeric_col", "string_col"]
model = OLS()
@@ -663,9 +649,7 @@ def test_missing_predictors_in_test(model_class: Type[Imputer]) -> None:
[OLS, QuantReg, QRF, Matching],
ids=lambda cls: cls.__name__,
)
-def test_reproducibility(
- model_class: Type[Imputer], simple_data: pd.DataFrame
-) -> None:
+def test_reproducibility(model_class: Type[Imputer], simple_data: pd.DataFrame) -> None:
# Note: MDN is excluded because PyTorch MPS (Apple Silicon) doesn't support
# deterministic operations, making reproducibility tests unreliable.
"""Test that models produce reproducible results."""
@@ -701,9 +685,7 @@ def test_large_number_of_predictors(model_class: Type[Imputer]) -> None:
n_predictors = 20
# Create data with many predictors
- data_dict = {
- f"x{i}": np.random.randn(n_samples) for i in range(n_predictors)
- }
+ data_dict = {f"x{i}": np.random.randn(n_samples) for i in range(n_predictors)}
data_dict["y"] = np.random.randn(n_samples)
data = pd.DataFrame(data_dict)
diff --git a/tests/test_models/test_matching.py b/tests/test_models/test_matching.py
index 7c01bff..25b5403 100644
--- a/tests/test_models/test_matching.py
+++ b/tests/test_models/test_matching.py
@@ -103,9 +103,9 @@ def test_matching_quantile_invariance(simple_data: pd.DataFrame) -> None:
val_01 = predictions[0.1]["y"].iloc[i]
val_05 = predictions[0.5]["y"].iloc[i]
val_09 = predictions[0.9]["y"].iloc[i]
- assert (
- val_01 == val_05 == val_09
- ), "Matching should return same value for all quantiles"
+ assert val_01 == val_05 == val_09, (
+ "Matching should return same value for all quantiles"
+ )
def test_matching_donor_preservation(simple_data: pd.DataFrame) -> None:
@@ -119,9 +119,9 @@ def test_matching_donor_preservation(simple_data: pd.DataFrame) -> None:
# The predicted value should be from the training set
predicted_value = predictions[0.5]["y"].iloc[0]
- assert (
- predicted_value in X_train["y"].values
- ), "Matched value should be from donor pool"
+ assert predicted_value in X_train["y"].values, (
+ "Matched value should be from donor pool"
+ )
# === Distance Functions Tests ===
@@ -144,9 +144,7 @@ def test_matching_different_distance_functions() -> None:
for dist_fun in distance_functions:
model = Matching()
- fitted_model = model.fit(
- X_train, ["x1", "x2"], ["y"], dist_fun=dist_fun
- )
+ fitted_model = model.fit(X_train, ["x1", "x2"], ["y"], dist_fun=dist_fun)
predictions = fitted_model.predict(X_test[:5], quantiles=[0.5])
@@ -328,9 +326,7 @@ def test_matching_hyperparameter_tuning(diabetes_data: pd.DataFrame) -> None:
train_idx = np.random.choice(
len(diabetes_data), int(0.7 * len(diabetes_data)), replace=False
)
- valid_idx = np.array(
- [i for i in range(len(diabetes_data)) if i not in train_idx]
- )
+ valid_idx = np.array([i for i in range(len(diabetes_data)) if i not in train_idx])
train_data = diabetes_data.iloc[train_idx].reset_index(drop=True)
valid_data = diabetes_data.iloc[valid_idx].reset_index(drop=True)
@@ -356,22 +352,15 @@ def test_matching_hyperparameter_tuning(diabetes_data: pd.DataFrame) -> None:
tuned_mse = {}
for var in imputed_variables:
- default_mse[var] = mean_squared_error(
- X_valid[var], default_preds[0.5][var]
- )
- tuned_mse[var] = mean_squared_error(
- X_valid[var], tuned_preds[0.5][var]
- )
+ default_mse[var] = mean_squared_error(X_valid[var], default_preds[0.5][var])
+ tuned_mse[var] = mean_squared_error(X_valid[var], tuned_preds[0.5][var])
# Both should produce valid results
assert all(mse < np.inf for mse in default_mse.values())
assert all(mse < np.inf for mse in tuned_mse.values())
# Check hyperparameters if available
- if (
- hasattr(tuned_fitted, "hyperparameters")
- and tuned_fitted.hyperparameters
- ):
+ if hasattr(tuned_fitted, "hyperparameters") and tuned_fitted.hyperparameters:
if "dist_fun" in tuned_fitted.hyperparameters:
assert tuned_fitted.hyperparameters["dist_fun"] in [
"Manhattan",
@@ -441,7 +430,5 @@ def test_matching_preserves_relationships() -> None:
# Each prediction should come from the same donor row
for i in range(len(pred_y1)):
# Find which donor row was matched
- donor_mask = (X_train["y1"] == pred_y1[i]) & (
- X_train["y2"] == pred_y2[i]
- )
+ donor_mask = (X_train["y1"] == pred_y1[i]) & (X_train["y2"] == pred_y2[i])
assert donor_mask.any(), "Predictions should come from same donor row"
diff --git a/tests/test_models/test_mdn.py b/tests/test_models/test_mdn.py
index 886ab57..3b2b080 100644
--- a/tests/test_models/test_mdn.py
+++ b/tests/test_models/test_mdn.py
@@ -496,9 +496,7 @@ def test_mdn_return_probs(mixed_type_data: pd.DataFrame) -> None:
fitted_model = model.fit(X_train, predictors, imputed_variables)
# Predict with return_probs=True
- predictions = fitted_model.predict(
- X_test, quantiles=[0.5], return_probs=True
- )
+ predictions = fitted_model.predict(X_test, quantiles=[0.5], return_probs=True)
assert "probabilities" in predictions
assert "y_categorical" in predictions["probabilities"]
diff --git a/tests/test_models/test_ols.py b/tests/test_models/test_ols.py
index 4db9686..2083ffb 100644
--- a/tests/test_models/test_ols.py
+++ b/tests/test_models/test_ols.py
@@ -54,9 +54,7 @@ def test_ols_basic_fit_predict(diabetes_data: pd.DataFrame) -> None:
fitted_model = model.fit(X_train, predictors, imputed_variables)
# Predict at multiple quantiles
- predictions = fitted_model.predict(
- X_test, QUANTILES, random_quantile_sample=False
- )
+ predictions = fitted_model.predict(X_test, QUANTILES, random_quantile_sample=False)
# Validate predictions
assert isinstance(predictions, dict)
@@ -231,20 +229,14 @@ def test_ols_prediction_quality(diabetes_data: pd.DataFrame) -> None:
# Split data
np.random.seed(42)
- train_idx = np.random.choice(
- len(data), int(0.8 * len(data)), replace=False
- )
+ train_idx = np.random.choice(len(data), int(0.8 * len(data)), replace=False)
test_idx = np.array([i for i in range(len(data)) if i not in train_idx])
train_data = data.iloc[train_idx].reset_index(drop=True)
test_data = data.iloc[test_idx].reset_index(drop=True)
- X_train = preprocess_data(
- train_data, full_data=True, train_size=1.0, test_size=0.0
- )
- X_test = preprocess_data(
- test_data, full_data=True, train_size=1.0, test_size=0.0
- )
+ X_train = preprocess_data(train_data, full_data=True, train_size=1.0, test_size=0.0)
+ X_test = preprocess_data(test_data, full_data=True, train_size=1.0, test_size=0.0)
# Fit model
model = OLS()
diff --git a/tests/test_models/test_qrf.py b/tests/test_models/test_qrf.py
index 04d740a..695f8ad 100644
--- a/tests/test_models/test_qrf.py
+++ b/tests/test_models/test_qrf.py
@@ -107,9 +107,7 @@ def test_qrf_sequential_imputation(diabetes_data: pd.DataFrame) -> None:
# Fit model with sequential imputation
model = QRF()
- fitted_model = model.fit(
- X_train, predictors, imputed_variables, n_estimators=30
- )
+ fitted_model = model.fit(X_train, predictors, imputed_variables, n_estimators=30)
# Get predictions
small_test = X_test.head(5).copy()
@@ -119,9 +117,7 @@ def test_qrf_sequential_imputation(diabetes_data: pd.DataFrame) -> None:
parallel_predictions = {}
for var in imputed_variables:
single_model = QRF()
- single_fitted = single_model.fit(
- X_train, predictors, [var], n_estimators=30
- )
+ single_fitted = single_model.fit(X_train, predictors, [var], n_estimators=30)
single_pred = single_fitted.predict(small_test, quantiles=[0.5])
parallel_predictions[var] = single_pred[0.5][var]
@@ -134,9 +130,7 @@ def test_qrf_sequential_imputation(diabetes_data: pd.DataFrame) -> None:
differences_found = True
break
- assert (
- differences_found
- ), "Sequential imputation should differ from parallel"
+ assert differences_found, "Sequential imputation should differ from parallel"
# Test that order matters
reversed_model = QRF()
@@ -214,9 +208,7 @@ def test_qrf_missing_categorical_levels_in_test(
)
# Should handle missing category gracefully
- predictions = fitted_model.predict(
- test_data[["numeric1", "numeric2", "category"]]
- )
+ predictions = fitted_model.predict(test_data[["numeric1", "numeric2", "category"]])
# Default returns DataFrame directly
assert not predictions["target"].isna().any()
@@ -613,9 +605,7 @@ def test_qrf_cross_validation(diabetes_data: pd.DataFrame) -> None:
imputed_variables = ["s1", "s4"]
data = diabetes_data[predictors + imputed_variables]
- qrf_results = cross_validate_model(
- QRF, data, predictors, imputed_variables
- )
+ qrf_results = cross_validate_model(QRF, data, predictors, imputed_variables)
# Validate cross-validation results - now a dict with dual metrics
assert isinstance(qrf_results, dict)
@@ -688,26 +678,18 @@ def test_qrf_performance_characteristics(diabetes_data: pd.DataFrame) -> None:
# Create train/test split
np.random.seed(42)
- train_idx = np.random.choice(
- len(data), int(0.8 * len(data)), replace=False
- )
+ train_idx = np.random.choice(len(data), int(0.8 * len(data)), replace=False)
test_idx = np.array([i for i in range(len(data)) if i not in train_idx])
train_data = data.iloc[train_idx].reset_index(drop=True)
test_data = data.iloc[test_idx].reset_index(drop=True)
- X_train = preprocess_data(
- train_data, full_data=True, train_size=1.0, test_size=0.0
- )
- X_test = preprocess_data(
- test_data, full_data=True, train_size=1.0, test_size=0.0
- )
+ X_train = preprocess_data(train_data, full_data=True, train_size=1.0, test_size=0.0)
+ X_test = preprocess_data(test_data, full_data=True, train_size=1.0, test_size=0.0)
# Fit model
model = QRF()
- fitted_model = model.fit(
- X_train, predictors, imputed_variables, n_estimators=50
- )
+ fitted_model = model.fit(X_train, predictors, imputed_variables, n_estimators=50)
# Get predictions
predictions = fitted_model.predict(X_test, quantiles=[0.5])
@@ -720,9 +702,7 @@ def test_qrf_performance_characteristics(diabetes_data: pd.DataFrame) -> None:
correlation = np.corrcoef(true_values, pred_values)[0, 1]
# QRF should achieve positive correlation
- assert (
- correlation > 0.0
- ), f"QRF correlation should be positive: {correlation}"
+ assert correlation > 0.0, f"QRF correlation should be positive: {correlation}"
# Calculate MSE
mse = mean_squared_error(true_values, pred_values)
@@ -1079,12 +1059,12 @@ def test_qrf_not_numeric_categorical_override() -> None:
{
"predictor1": np.random.randn(n_samples),
"predictor2": np.random.randn(n_samples),
- "discrete_var1": np.random.choice(
- [0, 1, 2, 3, 4, 5], n_samples
- ).astype(float),
- "discrete_var2": np.random.choice(
- [0, 1, 2, 3, 4, 5], n_samples
- ).astype(float),
+ "discrete_var1": np.random.choice([0, 1, 2, 3, 4, 5], n_samples).astype(
+ float
+ ),
+ "discrete_var2": np.random.choice([0, 1, 2, 3, 4, 5], n_samples).astype(
+ float
+ ),
"continuous_var": np.random.randn(n_samples) * 5 + 10,
}
)
@@ -1105,15 +1085,15 @@ def test_qrf_not_numeric_categorical_override() -> None:
)
# Check that discrete vars were treated as categorical
- assert (
- "discrete_var1" in model_default.categorical_targets
- ), "discrete_var1 should be categorical by default"
- assert (
- "discrete_var2" in model_default.categorical_targets
- ), "discrete_var2 should be categorical by default"
- assert (
- "continuous_var" in model_default.numeric_targets
- ), "continuous_var should be numeric"
+ assert "discrete_var1" in model_default.categorical_targets, (
+ "discrete_var1 should be categorical by default"
+ )
+ assert "discrete_var2" in model_default.categorical_targets, (
+ "discrete_var2 should be categorical by default"
+ )
+ assert "continuous_var" in model_default.numeric_targets, (
+ "continuous_var should be numeric"
+ )
# Test 2: Override discrete_var1 to be numeric, keep discrete_var2 as categorical
model_override = QRF(log_level="WARNING")
@@ -1121,26 +1101,24 @@ def test_qrf_not_numeric_categorical_override() -> None:
X_train=donor_df,
predictors=["predictor1", "predictor2"],
imputed_variables=["discrete_var1", "discrete_var2", "continuous_var"],
- not_numeric_categorical=[
- "discrete_var1"
- ], # Force discrete_var1 to be numeric
+ not_numeric_categorical=["discrete_var1"], # Force discrete_var1 to be numeric
n_estimators=20,
random_state=42,
)
# Check that discrete_var1 is now numeric, but discrete_var2 is still categorical
- assert (
- "discrete_var1" not in model_override.categorical_targets
- ), "discrete_var1 should NOT be categorical with override"
- assert (
- "discrete_var1" in model_override.numeric_targets
- ), "discrete_var1 should be numeric with override"
- assert (
- "discrete_var2" in model_override.categorical_targets
- ), "discrete_var2 should still be categorical"
- assert (
- "continuous_var" in model_override.numeric_targets
- ), "continuous_var should still be numeric"
+ assert "discrete_var1" not in model_override.categorical_targets, (
+ "discrete_var1 should NOT be categorical with override"
+ )
+ assert "discrete_var1" in model_override.numeric_targets, (
+ "discrete_var1 should be numeric with override"
+ )
+ assert "discrete_var2" in model_override.categorical_targets, (
+ "discrete_var2 should still be categorical"
+ )
+ assert "continuous_var" in model_override.numeric_targets, (
+ "continuous_var should still be numeric"
+ )
# Test 3: Predictions should work with both models
predictions_default = fitted_default.predict(receiver_df)
@@ -1155,15 +1133,17 @@ def test_qrf_not_numeric_categorical_override() -> None:
predictions_default["discrete_var1"].values,
predictions_override["discrete_var1"].values,
rtol=1e-5,
- ), "discrete_var1 predictions should differ between categorical and numeric treatment"
+ ), (
+ "discrete_var1 predictions should differ between categorical and numeric treatment"
+ )
# Test 4: Override for predictors as well
# Create data where a predictor has discrete values
donor_df_pred = pd.DataFrame(
{
- "discrete_predictor": np.random.choice(
- [10, 20, 30, 40], n_samples
- ).astype(float),
+ "discrete_predictor": np.random.choice([10, 20, 30, 40], n_samples).astype(
+ float
+ ),
"normal_predictor": np.random.randn(n_samples),
"target": np.random.randn(n_samples) * 2 + 5,
}
@@ -1171,9 +1151,7 @@ def test_qrf_not_numeric_categorical_override() -> None:
receiver_df_pred = pd.DataFrame(
{
- "discrete_predictor": np.random.choice(
- [10, 20, 30, 40], 30
- ).astype(float),
+ "discrete_predictor": np.random.choice([10, 20, 30, 40], 30).astype(float),
"normal_predictor": np.random.randn(30),
}
)
@@ -1276,6 +1254,6 @@ def test_qrf_hyperparameter_tuning_improves_performance() -> None:
# Tuned model should perform at least as well as untuned, or within 20% margin
# (allowing for random variation in small datasets)
margin = 0.2
- assert tuned_loss <= untuned_loss * (
- 1 + margin
- ), f"Tuned loss ({tuned_loss:.4f}) should be ≤ {(1+margin)*100}% of untuned loss ({untuned_loss:.4f} * {1+margin} = {untuned_loss * (1+margin):.4f})"
+ assert tuned_loss <= untuned_loss * (1 + margin), (
+ f"Tuned loss ({tuned_loss:.4f}) should be ≤ {(1 + margin) * 100}% of untuned loss ({untuned_loss:.4f} * {1 + margin} = {untuned_loss * (1 + margin):.4f})"
+ )
diff --git a/tests/test_models/test_quantreg.py b/tests/test_models/test_quantreg.py
index 74e241f..826cc78 100644
--- a/tests/test_models/test_quantreg.py
+++ b/tests/test_models/test_quantreg.py
@@ -90,9 +90,7 @@ def test_quantreg_specific_quantiles(simple_data: pd.DataFrame) -> None:
specific_quantiles = [0.1, 0.5, 0.9]
model = QuantReg()
- fitted_model = model.fit(
- X_train, ["x1", "x2"], ["y"], quantiles=specific_quantiles
- )
+ fitted_model = model.fit(X_train, ["x1", "x2"], ["y"], quantiles=specific_quantiles)
# Predict at the same quantiles
predictions = fitted_model.predict(X_test, quantiles=specific_quantiles)
@@ -152,9 +150,7 @@ def test_quantreg_skewed_distribution(skewed_data: pd.DataFrame) -> None:
# For skewed distributions, quantiles should be different from symmetric
# Just check they're not exactly equal (some asymmetry is captured)
- assert (
- abs(upper_diff - lower_diff) > 0.01
- ), "QuantReg should capture some asymmetry"
+ assert abs(upper_diff - lower_diff) > 0.01, "QuantReg should capture some asymmetry"
# === Cross-Validation Test ===
@@ -244,18 +240,16 @@ def test_quantreg_heteroscedasticity() -> None:
high_x_idx = X_test_sorted.index[-10:]
low_x_spread = (
- predictions[0.9].loc[low_x_idx, "y"]
- - predictions[0.1].loc[low_x_idx, "y"]
+ predictions[0.9].loc[low_x_idx, "y"] - predictions[0.1].loc[low_x_idx, "y"]
).mean()
high_x_spread = (
- predictions[0.9].loc[high_x_idx, "y"]
- - predictions[0.1].loc[high_x_idx, "y"]
+ predictions[0.9].loc[high_x_idx, "y"] - predictions[0.1].loc[high_x_idx, "y"]
).mean()
# Check that there's some difference in spread (heteroscedasticity is partially captured)
- assert (
- abs(high_x_spread - low_x_spread) > 0.01
- ), "QuantReg should show some heteroscedasticity effect"
+ assert abs(high_x_spread - low_x_spread) > 0.01, (
+ "QuantReg should show some heteroscedasticity effect"
+ )
# === Comparison with OLS ===
@@ -269,17 +263,13 @@ def test_quantreg_vs_ols_median(simple_data: pd.DataFrame) -> None:
# Fit QuantReg for median
quantreg = QuantReg()
- quantreg_fitted = quantreg.fit(
- X_train, ["x1", "x2"], ["y"], quantiles=[0.5]
- )
+ quantreg_fitted = quantreg.fit(X_train, ["x1", "x2"], ["y"], quantiles=[0.5])
quantreg_pred = quantreg_fitted.predict(X_test, quantiles=[0.5])
# Fit OLS
ols = OLS()
ols_fitted = ols.fit(X_train, ["x1", "x2"], ["y"])
- ols_pred = ols_fitted.predict(
- X_test, quantiles=[0.5], random_quantile_sample=False
- )
+ ols_pred = ols_fitted.predict(X_test, quantiles=[0.5], random_quantile_sample=False)
# For normally distributed errors, median and mean should be similar
# QuantReg returns DataFrame for single quantile, OLS returns dict
@@ -287,9 +277,9 @@ def test_quantreg_vs_ols_median(simple_data: pd.DataFrame) -> None:
ols_mean = ols_pred[0.5]["y"].values
correlation = np.corrcoef(quantreg_median, ols_mean)[0, 1]
- assert (
- correlation > 0.85
- ), "QuantReg median and OLS mean should be reasonably similar for normal data"
+ assert correlation > 0.85, (
+ "QuantReg median and OLS mean should be reasonably similar for normal data"
+ )
# === Performance Tests ===
@@ -304,26 +294,18 @@ def test_quantreg_prediction_quality(diabetes_data: pd.DataFrame) -> None:
# Split data
np.random.seed(42)
- train_idx = np.random.choice(
- len(data), int(0.8 * len(data)), replace=False
- )
+ train_idx = np.random.choice(len(data), int(0.8 * len(data)), replace=False)
test_idx = np.array([i for i in range(len(data)) if i not in train_idx])
train_data = data.iloc[train_idx].reset_index(drop=True)
test_data = data.iloc[test_idx].reset_index(drop=True)
- X_train = preprocess_data(
- train_data, full_data=True, train_size=1.0, test_size=0.0
- )
- X_test = preprocess_data(
- test_data, full_data=True, train_size=1.0, test_size=0.0
- )
+ X_train = preprocess_data(train_data, full_data=True, train_size=1.0, test_size=0.0)
+ X_test = preprocess_data(test_data, full_data=True, train_size=1.0, test_size=0.0)
# Fit model
model = QuantReg()
- fitted_model = model.fit(
- X_train, predictors, imputed_variables, quantiles=[0.5]
- )
+ fitted_model = model.fit(X_train, predictors, imputed_variables, quantiles=[0.5])
# Get predictions
predictions = fitted_model.predict(X_test, quantiles=[0.5])
diff --git a/tests/test_predictor_analysis.py b/tests/test_predictor_analysis.py
index 2c167da..9fd32ce 100644
--- a/tests/test_predictor_analysis.py
+++ b/tests/test_predictor_analysis.py
@@ -67,23 +67,17 @@ def sample_mixed_data():
"numeric_1": np.random.randn(n_samples),
"numeric_2": np.random.randn(n_samples) * 2 + 5,
"numeric_3": np.random.exponential(2, n_samples),
- "categorical_1": np.random.choice(
- ["cat_A", "cat_B", "cat_C"], n_samples
- ),
+ "categorical_1": np.random.choice(["cat_A", "cat_B", "cat_C"], n_samples),
"categorical_2": np.random.choice(["X", "Y", "Z", "W"], n_samples),
"binary_1": np.random.choice([True, False], n_samples),
"binary_2": np.random.choice([0, 1], n_samples),
"target_numeric": np.random.randn(n_samples) * 3,
- "target_categorical": np.random.choice(
- ["class_1", "class_2"], n_samples
- ),
+ "target_categorical": np.random.choice(["class_1", "class_2"], n_samples),
}
)
# Create some correlation
- data["numeric_2"] = (
- data["numeric_1"] * 0.7 + np.random.randn(n_samples) * 0.5
- )
+ data["numeric_2"] = data["numeric_1"] * 0.7 + np.random.randn(n_samples) * 0.5
data["target_numeric"] = (
data["numeric_1"] * 0.5
+ data["numeric_2"] * 0.3
@@ -216,9 +210,7 @@ def test_predictor_target_mutual_information(self, sample_mixed_data):
len(imputed_variables),
)
assert list(results["predictor_target_mi"].index) == predictors
- assert (
- list(results["predictor_target_mi"].columns) == imputed_variables
- )
+ assert list(results["predictor_target_mi"].columns) == imputed_variables
# Check values are in valid range [0, 1] (normalized MI)
mi_values = results["predictor_target_mi"].values
@@ -226,25 +218,14 @@ def test_predictor_target_mutual_information(self, sample_mixed_data):
# Check that numeric_1 and numeric_2 have non-zero MI with target_numeric
# (since we created correlation in the fixture)
- assert (
- results["predictor_target_mi"].loc["numeric_1", "target_numeric"]
- > 0
- )
- assert (
- results["predictor_target_mi"].loc["numeric_2", "target_numeric"]
- > 0
- )
+ assert results["predictor_target_mi"].loc["numeric_1", "target_numeric"] > 0
+ assert results["predictor_target_mi"].loc["numeric_2", "target_numeric"] > 0
# Check that binary_1 has non-zero MI with target_numeric
# (it's part of the target calculation)
- assert (
- results["predictor_target_mi"].loc["binary_1", "target_numeric"]
- > 0
- )
+ assert results["predictor_target_mi"].loc["binary_1", "target_numeric"] > 0
- def test_predictor_target_mi_only_with_mutual_info_method(
- self, sample_mixed_data
- ):
+ def test_predictor_target_mi_only_with_mutual_info_method(self, sample_mixed_data):
"""Test that predictor-target MI is only computed when mutual_info is requested."""
predictors = ["numeric_1", "numeric_2"]
imputed_variables = ["target_numeric"]
@@ -414,9 +395,7 @@ def test_basic_progressive_inclusion(self, sample_regression_data):
# Check inclusion order from DataFrame
assert len(results_df) <= 3
- assert all(
- pred in predictors for pred in results_df["predictor_added"]
- )
+ assert all(pred in predictors for pred in results_df["predictor_added"])
# Check step numbering is correct
assert list(results_df["step"]) == list(range(1, len(results_df) + 1))
@@ -431,9 +410,7 @@ def test_basic_progressive_inclusion(self, sample_regression_data):
assert len(results["optimal_subset"]) <= 3
assert results["optimal_loss"] > 0 # Should be a positive loss value
- def test_progressive_inclusion_all_predictors(
- self, sample_regression_data
- ):
+ def test_progressive_inclusion_all_predictors(self, sample_regression_data):
"""Test progressive inclusion using all predictors."""
predictors = ["feature_1", "feature_2", "feature_3"]
imputed_variables = ["target"]
@@ -479,9 +456,7 @@ def test_progressive_inclusion_with_mixed_types(self, sample_mixed_data):
# Check that values are valid
assert (results_df["avg_quantile_loss"] >= 0).all()
assert (results_df["avg_log_loss"] >= 0).all()
- assert results_df["step"].tolist() == list(
- range(1, len(results_df) + 1)
- )
+ assert results_df["step"].tolist() == list(range(1, len(results_df) + 1))
class TestIntegration:
@@ -497,8 +472,7 @@ def test_full_predictor_analysis_workflow(self, sample_mixed_data):
data=sample_mixed_data, predictors=predictors, method="all"
)
assert all(
- key in correlations
- for key in ["pearson", "spearman", "mutual_info"]
+ key in correlations for key in ["pearson", "spearman", "mutual_info"]
)
# 2. Leave-one-out analysis
diff --git a/tests/test_quantile_comparison.py b/tests/test_quantile_comparison.py
index 9814d21..9eb9319 100644
--- a/tests/test_quantile_comparison.py
+++ b/tests/test_quantile_comparison.py
@@ -119,9 +119,7 @@ def test_compare_metrics_basic(split_data: tuple) -> None:
)
# Compare quantile loss
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Check structure - returns long format DataFrame
assert isinstance(loss_comparison_df, pd.DataFrame)
@@ -156,9 +154,7 @@ def test_single_imputed_variable(split_data: tuple) -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Check results contain data for single variable
variables = loss_comparison_df["Imputed Variable"].unique()
@@ -180,9 +176,7 @@ def test_multiple_imputed_variables(split_data: tuple) -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Check results contain data for all variables
variables = loss_comparison_df["Imputed Variable"].unique()
@@ -221,9 +215,7 @@ def test_quantile_loss_symmetry() -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
assert not loss_comparison_df.empty
@@ -256,14 +248,10 @@ def test_perfect_predictions() -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Loss should be relatively low for perfect predictions
- ols_loss = loss_comparison_df[loss_comparison_df["Method"] == "OLS"][
- "Loss"
- ]
+ ols_loss = loss_comparison_df[loss_comparison_df["Method"] == "OLS"]["Loss"]
assert ols_loss.min() <= 1.01 # Allow for small floating point errors
@@ -288,9 +276,7 @@ def test_model_ranking(diabetes_data: pd.DataFrame) -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Check we can compute mean loss per model
mean_losses = loss_comparison_df.groupby("Method")["Loss"].mean()
@@ -317,9 +303,7 @@ def test_wide_format_visualization(split_data: tuple) -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Convert to wide format for plotting
# Filter to mean_loss only
@@ -336,9 +320,7 @@ def test_wide_format_visualization(split_data: tuple) -> None:
assert isinstance(wide_df, pd.DataFrame)
assert wide_df.shape[0] <= len(model_classes) # One row per model
# Columns should be quantiles
- assert all(
- col in QUANTILES for col in wide_df.columns if col in QUANTILES
- )
+ assert all(col in QUANTILES for col in wide_df.columns if col in QUANTILES)
def test_long_format_visualization(split_data: tuple) -> None:
@@ -354,9 +336,7 @@ def test_long_format_visualization(split_data: tuple) -> None:
model_classes, X_train, X_test, predictors, imputed_variables
)
- loss_comparison_df = compare_metrics(
- Y_test, method_imputations, imputed_variables
- )
+ loss_comparison_df = compare_metrics(Y_test, method_imputations, imputed_variables)
# Long format is directly suitable for seaborn/plotly
assert "Method" in loss_comparison_df.columns
@@ -364,9 +344,7 @@ def test_long_format_visualization(split_data: tuple) -> None:
assert "Loss" in loss_comparison_df.columns
# Can group by method and percentile
- grouped = loss_comparison_df.groupby(["Method", "Percentile"])[
- "Loss"
- ].mean()
+ grouped = loss_comparison_df.groupby(["Method", "Percentile"])["Loss"].mean()
assert not grouped.empty
@@ -422,9 +400,7 @@ def test_log_loss_for_categorical_variables() -> None:
model_classes, X_train, X_test, predictors, binary_imputed
)
- loss_df = compare_metrics(
- Y_test_binary, method_imputations, binary_imputed
- )
+ loss_df = compare_metrics(Y_test_binary, method_imputations, binary_imputed)
# Check that log loss is used for binary categorical
binary_metrics = loss_df[loss_df["Imputed Variable"] == "binary_cat"][
@@ -458,9 +434,7 @@ def test_log_loss_for_categorical_variables() -> None:
model_classes, X_train, X_test, predictors, string_imputed
)
- loss_df = compare_metrics(
- Y_test_string, method_imputations, string_imputed
- )
+ loss_df = compare_metrics(Y_test_string, method_imputations, string_imputed)
# Check that log loss is used for string categorical
string_metrics = loss_df[loss_df["Imputed Variable"] == "string_cat"][
@@ -480,9 +454,7 @@ def test_log_loss_for_categorical_variables() -> None:
loss_df = compare_metrics(Y_test_num, method_imputations, num_imputed)
# Check that quantile loss is used for numerical
- num_metrics = loss_df[loss_df["Imputed Variable"] == "numerical"][
- "Metric"
- ].unique()
+ num_metrics = loss_df[loss_df["Imputed Variable"] == "numerical"]["Metric"].unique()
assert "quantile_loss" in num_metrics
assert "log_loss" not in num_metrics
@@ -512,9 +484,7 @@ def test_mixed_variable_types() -> None:
}
)
- predictors = [
- "predictor1"
- ] # Using only numerical predictor for simplicity
+ predictors = ["predictor1"] # Using only numerical predictor for simplicity
imputed_variables = ["numerical_target", "categorical_target"]
Y_test = X_test[imputed_variables]
@@ -559,9 +529,7 @@ def test_log_loss_value_properties() -> None:
X_train = pd.DataFrame(
{
"x": range(100),
- "y_perfect": [
- i % 2 for i in range(100)
- ], # Perfectly predictable from x
+ "y_perfect": [i % 2 for i in range(100)], # Perfectly predictable from x
"y_random": np.random.choice([0, 1], size=100), # Random
}
)
@@ -585,14 +553,10 @@ def test_log_loss_value_properties() -> None:
model_classes, X_train, X_test, predictors, perfect_imputed
)
- loss_df = compare_metrics(
- Y_test_perfect, method_imputations, perfect_imputed
- )
+ loss_df = compare_metrics(Y_test_perfect, method_imputations, perfect_imputed)
# Log loss for perfect predictions should be low
- perfect_loss = loss_df[loss_df["Imputed Variable"] == "y_perfect"][
- "Loss"
- ].mean()
+ perfect_loss = loss_df[loss_df["Imputed Variable"] == "y_perfect"]["Loss"].mean()
assert perfect_loss < 5.0 # Log loss should be relatively low
# Test with random variable (should have higher loss)
@@ -603,14 +567,10 @@ def test_log_loss_value_properties() -> None:
model_classes, X_train, X_test, predictors, random_imputed
)
- loss_df = compare_metrics(
- Y_test_random, method_imputations, random_imputed
- )
+ loss_df = compare_metrics(Y_test_random, method_imputations, random_imputed)
# Log loss for random predictions should be higher
- random_loss = loss_df[loss_df["Imputed Variable"] == "y_random"][
- "Loss"
- ].mean()
+ random_loss = loss_df[loss_df["Imputed Variable"] == "y_random"]["Loss"].mean()
# Random loss should typically be higher than perfect predictions
# (though not guaranteed due to randomness)
diff --git a/tests/test_smoke_qrf.py b/tests/test_smoke_qrf.py
index b6be02b..9c47fa6 100644
--- a/tests/test_smoke_qrf.py
+++ b/tests/test_smoke_qrf.py
@@ -21,9 +21,7 @@ def test_qrf_basic_usage():
"household_size": np.random.randint(1, 6, n_samples),
}
)
- X_train["benefits"] = X_train["income"] * 0.1 + np.random.normal(
- 0, 1000, n_samples
- )
+ X_train["benefits"] = X_train["income"] * 0.1 + np.random.normal(0, 1000, n_samples)
predictors = ["age", "income", "household_size"]
imputed_variables = ["benefits"]
@@ -50,12 +48,10 @@ def test_qrf_basic_usage():
# Basic assertions
assert "benefits" in predictions, "Should have predictions for 'benefits'"
- assert len(predictions["benefits"]) == len(
- X_test
- ), "Should have predictions for all test samples"
- assert (
- not predictions["benefits"].isna().any()
- ), "Should not have NaN predictions"
+ assert len(predictions["benefits"]) == len(X_test), (
+ "Should have predictions for all test samples"
+ )
+ assert not predictions["benefits"].isna().any(), "Should not have NaN predictions"
print("✓ QRF smoke test passed")
diff --git a/tests/test_visualizations.py b/tests/test_visualizations.py
index ad1475d..2e7d091 100644
--- a/tests/test_visualizations.py
+++ b/tests/test_visualizations.py
@@ -61,9 +61,7 @@ def sample_log_loss_results():
@pytest.fixture
-def sample_combined_results(
- sample_quantile_loss_results, sample_log_loss_results
-):
+def sample_combined_results(sample_quantile_loss_results, sample_log_loss_results):
"""Create sample combined metric results."""
return {
"quantile_loss": sample_quantile_loss_results,
@@ -91,9 +89,7 @@ def sample_probability_distribution():
# Generate random probabilities that sum to 1
probs = np.random.dirichlet(np.ones(n_classes), n_samples)
- return pd.DataFrame(
- probs, columns=[f"Class {i}" for i in range(n_classes)]
- )
+ return pd.DataFrame(probs, columns=[f"Class {i}" for i in range(n_classes)])
@pytest.fixture
@@ -225,9 +221,7 @@ def test_confusion_matrix_plot(self, sample_confusion_matrix):
assert hasattr(fig, "data")
assert len(fig.data) > 0 # Should have at least one trace
- def test_probability_distribution_plot(
- self, sample_probability_distribution
- ):
+ def test_probability_distribution_plot(self, sample_probability_distribution):
"""Test probability distribution visualization."""
with warnings.catch_warnings():
warnings.simplefilter("ignore")
@@ -260,9 +254,7 @@ def test_probability_distribution_plot(
# Verify the figure contains data
assert hasattr(fig, "data")
- assert (
- len(fig.data) > 0
- ) # Should have histogram or distribution data
+ assert len(fig.data) > 0 # Should have histogram or distribution data
def test_df_compatibility(self, sample_quantile_loss_results):
"""Test compatibility with DataFrame input."""
@@ -460,9 +452,7 @@ def test_ols_visualization(self, diabetes_data):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- results = cross_validate_model(
- OLS, data, predictors, imputed_variables
- )
+ results = cross_validate_model(OLS, data, predictors, imputed_variables)
# Test visualization
viz = PerformanceResults(
@@ -517,9 +507,7 @@ def test_model_comparison_integration(self, diabetes_data):
comparison_results = {}
# OLS
- ols_results = cross_validate_model(
- OLS, data, predictors, imputed_variables
- )
+ ols_results = cross_validate_model(OLS, data, predictors, imputed_variables)
comparison_results["OLS"] = ols_results
# QuantReg
@@ -529,9 +517,7 @@ def test_model_comparison_integration(self, diabetes_data):
comparison_results["QuantReg"] = qr_results
# Test comparison visualization
- viz = MethodComparisonResults(
- comparison_results, metric="quantile_loss"
- )
+ viz = MethodComparisonResults(comparison_results, metric="quantile_loss")
fig = viz.plot()
assert fig is not None
@@ -592,9 +578,7 @@ def test_comparison_visualization_from_autoimpute(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- viz = MethodComparisonResults(
- model_results, metric="quantile_loss"
- )
+ viz = MethodComparisonResults(model_results, metric="quantile_loss")
fig = viz.plot()
assert fig is not None
@@ -638,9 +622,7 @@ def test_missing_required_keys(self):
assert fig is not None
except (ValueError, KeyError) as e:
# Should have informative error message
- assert (
- "results" in str(e).lower() or "missing" in str(e).lower()
- )
+ assert "results" in str(e).lower() or "missing" in str(e).lower()
def test_nan_handling(self):
"""Test handling of NaN values in results."""
@@ -749,9 +731,7 @@ def test_comparison_results_with_error_bars(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- viz = MethodComparisonResults(
- comparison_results, metric="quantile_loss"
- )
+ viz = MethodComparisonResults(comparison_results, metric="quantile_loss")
fig = viz.plot(show_error_bars=True)
assert fig is not None
diff --git a/uv.lock b/uv.lock
index a9bc50d..6eb8066 100644
--- a/uv.lock
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