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add support for sparse X #86

Merged
merged 11 commits into from
Aug 28, 2024
Merged

add support for sparse X #86

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c1351d0
add support for csr matrix
apoorvalal Aug 16, 2024
ff81034
Appease mypy about notebook.
Aug 27, 2024
47dac10
Test against csr in test_cross_fit_estimator.py
Aug 27, 2024
3d2d84e
Test against csr in test_utils.py
Aug 27, 2024
7e892f2
Adapt S-Learner to work with csr matrix.
Aug 27, 2024
43da989
Test against csr matrix in test_learner and test_metalearner.
kklein Aug 27, 2024
0c0923f
Merge pull request #3 from kklein/sparseX
apoorvalal Aug 27, 2024
9a6ed73
fix notebook metadata
apoorvalal Aug 27, 2024
6a77952
Merge branch 'main' into sparseX
apoorvalal Aug 27, 2024
8282381
reduce sparse problem size for docs
apoorvalal Aug 27, 2024
e4501b4
final touches
apoorvalal Aug 28, 2024
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119 changes: 90 additions & 29 deletions docs/examples/example_estimating_ates.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
Expand All @@ -40,7 +40,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -99,9 +99,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2.083595103597918, 0.06526671583747883)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"naive_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}\", df) .fit(cov_type=\"HC1\")\n",
"naive_est = naive_lm.params.iloc[1], naive_lm.bse.iloc[1]\n",
Expand All @@ -110,9 +121,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2.1433722387308025, 0.06345124983351998)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covaradjust_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\",\n",
" df) .fit(cov_type=\"HC1\")\n",
Expand All @@ -138,9 +160,42 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style type='text/css'>\n",
".datatable table.frame { margin-bottom: 0; }\n",
".datatable table.frame thead { border-bottom: none; }\n",
".datatable table.frame tr.coltypes td { color: #FFFFFF; line-height: 6px; padding: 0 0.5em;}\n",
".datatable .bool { background: #DDDD99; }\n",
".datatable .object { background: #565656; }\n",
".datatable .int { background: #5D9E5D; }\n",
".datatable .float { background: #4040CC; }\n",
".datatable .str { background: #CC4040; }\n",
".datatable .time { background: #40CC40; }\n",
".datatable .row_index { background: var(--jp-border-color3); border-right: 1px solid var(--jp-border-color0); color: var(--jp-ui-font-color3); font-size: 9px;}\n",
".datatable .frame tbody td { text-align: left; }\n",
".datatable .frame tr.coltypes .row_index { background: var(--jp-border-color0);}\n",
".datatable th:nth-child(2) { padding-left: 12px; }\n",
".datatable .hellipsis { color: var(--jp-cell-editor-border-color);}\n",
".datatable .vellipsis { background: var(--jp-layout-color0); color: var(--jp-cell-editor-border-color);}\n",
".datatable .na { color: var(--jp-cell-editor-border-color); font-size: 80%;}\n",
".datatable .sp { opacity: 0.25;}\n",
".datatable .footer { font-size: 9px; }\n",
".datatable .frame_dimensions { background: var(--jp-border-color3); border-top: 1px solid var(--jp-border-color0); color: var(--jp-ui-font-color3); display: inline-block; opacity: 0.6; padding: 1px 10px 1px 5px;}\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from metalearners import DRLearner\n",
"from lightgbm import LGBMRegressor, LGBMClassifier\n",
Expand All @@ -149,9 +204,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"execution_count": 6,
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(array([1.02931589]), array([0.06679633]))"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metalearners_dr = DRLearner(\n",
" nuisance_model_factory=LGBMRegressor,\n",
Expand Down Expand Up @@ -557,22 +629,11 @@
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
"name": "python"
},
"mystnb": {
"execution_timeout": 120
}
},
"nbformat": 4,
Expand Down
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