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Add pre-validation predictions to cv_grpnet #150

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9 changes: 9 additions & 0 deletions adelie/cv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,10 @@ class CVGrpnetResult:
"""
Argmin of ``avg_losses``.
"""
preval_preds: np.ndarray
"""
``preval_preds[i]`` is the prevalidated prediction from CV for the ``i`` th training datapoint. Only supported when `glm` is a Gaussian family.
"""

def plot_loss(self):
"""Plots the average K-fold CV loss.
Expand Down Expand Up @@ -236,6 +240,8 @@ def cv_grpnet(
full_lmdas = state.lmda_max * np.logspace(0, np.log10(min_ratio), lmda_path_size)

cv_losses = np.empty((n_folds, full_lmdas.shape[0]))
preval_preds = np.empty((full_lmdas.shape[0], n))

for fold in range(n_folds):
# current validation fold range
begin = (
Expand Down Expand Up @@ -301,6 +307,8 @@ def cv_grpnet(
offsets=state._offsets,
n_threads=n_threads,
)

preval_preds[:,order[begin:begin+curr_fold_size]] = etas[:,order[begin:begin+curr_fold_size]]

# compute loss on full data
full_data_losses = np.array([glm.loss(eta) for eta in etas])
Expand All @@ -322,4 +330,5 @@ def cv_grpnet(
losses=cv_losses,
avg_losses=avg_losses,
best_idx=best_idx,
preval_preds=preval_preds[best_idx,:]
)
161 changes: 161 additions & 0 deletions tests/test_preval.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,161 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import adelie as ad\n",
"import numpy as np\n",
"\n",
"from adelie.diagnostic import coefficient, predict\n",
"from adelie.solver import grpnet"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"n = 10000 # number of samples\n",
"p = 100 # number of features\n",
"n_h1 = p // 2 # number of features with signal\n",
"rho = 0.3 # equi-correlation\n",
"seed = 0 # random seed\n",
"\n",
"np.random.seed(seed)\n",
"W = np.random.normal(0, 1, n)\n",
"Z = np.random.normal(0, 1, (n, p))\n",
"X = np.sqrt(rho) * W[:, None] + np.sqrt(1-rho) * Z\n",
"y = X[:, :n_h1] @ np.random.normal(0, 1, n_h1) + np.sqrt(n_h1) * np.random.normal(0, 1, n)\n",
"X = np.asfortranarray(X)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 850.54it/s] [dev:44.0%]\n",
"100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 959.19it/s] [dev:44.6%]\n",
"100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 101/101 [00:00:00<00:00:00, 996.62it/s] [dev:44.3%] \n",
"100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 100/100 [00:00:00<00:00:00, 974.54it/s] [dev:44.8%]\n",
"100%|\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m\u001b[1;32m█\u001b[0m| 101/101 [00:00:00<00:00:00, 929.90it/s] [dev:45.1%]\n"
]
},
{
"data": {
"text/plain": [
"(10000,)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv_res = ad.cv_grpnet(\n",
" X=X,\n",
" glm=ad.glm.gaussian(y),\n",
" min_ratio=1e-3,\n",
" seed=seed,\n",
" intercept=True,\n",
")\n",
"\n",
"cv_res.preval_preds.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"manual_preval_preds = np.empty(n)\n",
"manual_preval_preds.fill(np.nan)\n",
"\n",
"# same folds as what cv_grpnet used \n",
"np.random.seed(seed)\n",
"order = np.random.choice(n, n, replace=False)\n",
"n_folds = cv_res.losses.shape[0]\n",
"fold_size = n // n_folds\n",
"remaining = n % n_folds\n",
"\n",
"best_idx = cv_res.best_idx\n",
"lmdas = cv_res.lmdas\n",
"\n",
"for fold in range(n_folds):\n",
" begin = (fold_size + 1) * min(fold, remaining) + max(fold - remaining, 0) * fold_size\n",
" size = fold_size + (fold < remaining)\n",
" test_idx = order[begin:begin+size]\n",
" train_idx = np.setdiff1d(order, test_idx)\n",
"\n",
" state = grpnet(\n",
" X=X[train_idx],\n",
" glm=ad.glm.gaussian(y[train_idx]),\n",
" intercept=True,\n",
" ddev_tol=0,\n",
" progress_bar=False,\n",
" lmda_path=lmdas\n",
" )\n",
"\n",
" assert np.allclose(state.lmdas,state.lmda_path)\n",
" beta_best, intercept_best = coefficient(\n",
" lmda=lmdas[best_idx],\n",
" betas=state.betas,\n",
" intercepts=state.intercepts,\n",
" lmdas=state.lmda_path,\n",
" )\n",
" manual_preval_preds[test_idx] = predict(X=X[test_idx], betas=beta_best, intercepts=intercept_best)\n",
"\n",
"np.allclose(cv_res.preval_preds, manual_preval_preds)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "adelie_venv",
"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": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}