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final touches
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@apoorvalal
apoorvalal committed Aug 28, 2024
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8 changes: 8 additions & 0 deletions CHANGELOG.rst
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Changelog
=========

0.11.0 (2024-09-xx)
-------------------

**New features**

* Add support for using ``scipy.sparse.csr_matrix`` as datastructure for covariates ``X``.


0.10.0 (2024-08-13)
-------------------

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100 changes: 20 additions & 80 deletions docs/examples/example_estimating_ates.ipynb
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"(2.083595103597918, 0.06526671583747883)"
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"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",
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"covaradjust_lm = smf.ols(f\"{outcome_column} ~ {treatment_column}+{'+'.join(feature_columns)}\",\n",
" df) .fit(cov_type=\"HC1\")\n",
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},
{
"cell_type": "code",
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"metadata": {},
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{
"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"
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"source": [
"from metalearners import DRLearner\n",
"from lightgbm import LGBMRegressor, LGBMClassifier\n",
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"source": [
"metalearners_dr = DRLearner(\n",
" nuisance_model_factory=LGBMRegressor,\n",
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}
],
"metadata": {
"kernelspec": {
"display_name": "py311",
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"name": "python",
"version": "3.11.7"
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"mystnb": {
"execution_timeout": 120
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93 changes: 16 additions & 77 deletions docs/examples/example_sparse_inputs.ipynb
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},
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".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",
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"source": [
"import time, psutil, os, gc\n",
"import numpy as np\n",
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"\n",
"Sparse data memory: 0.76MB\n",
"Dense data memory: 41.28MB\n"
]
}
],
"outputs": [],
"source": [
"print(f\"\\nSparse data memory: {X_csr.data.nbytes / 1024 / 1024:.2f}MB\")\n",
"print(f\"Dense data memory: {X_np.nbytes / 1024 / 1024:.2f}MB\")"
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},
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"source": [
"def fit_drlearner_wrapper(X):\n",
"def fit_drlearner_wrapper(X, name):\n",
" start_memory = get_memory_usage()\n",
" start_time = time.time()\n",
" metalearners_dr = DRLearner(\n",
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" end_memory = get_memory_usage()\n",
" runtime = end_time - start_time\n",
" memory_used = end_memory - start_memory\n",
" print(f\"Sparse data - Runtime: {runtime:.2f}s, Memory used: {memory_used:.2f}MB\")\n",
" print(f\"{name} data - Runtime: {runtime:.2f}s, Memory used: {memory_used:.2f}MB\")\n",
" print(metalearners_est)"
]
},
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},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Sparse data - Runtime: 3.06s, Memory used: 115.93MB\n",
"(array([1.0161235]), array([0.06374022]))\n"
]
}
],
"outputs": [],
"source": [
"fit_drlearner_wrapper(X_csr)"
"fit_drlearner_wrapper(X_csr, \"Sparse\")"
]
},
{
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},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Sparse data - Runtime: 6.91s, Memory used: 131.66MB\n",
"(array([1.01609547]), array([0.06384197]))\n"
]
}
],
"outputs": [],
"source": [
"fit_drlearner_wrapper(X_np)"
"fit_drlearner_wrapper(X_np, \"Dense\")"
]
}
],
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2 changes: 1 addition & 1 deletion metalearners/_utils.py
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default_rng = np.random.default_rng()


def safe_len(X):
def safe_len(X: Matrix) -> int:
if scipy.sparse.issparse(X):
return X.shape[0]
return len(X)
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