Embedded new CI to survivors

demonstration/Articles/Refactoring.ipynb

@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 33,
    "id": "8ad4da3a",
    "metadata": {},
    "outputs": [],
@@ -222,7 +222,7 @@
     "#                     concordant_pairs += 0.5\n",
     "    return concordant_pairs, total_pairs\n",
     "\n",
-    "def concordance_index_self(T, P, E):\n",
+    "def concordance_index_self(event_times, predicted_scores, event_observed=None):\n",
     "    \"\"\"\n",
     "    Calculates the concordance index (C-index) for survival analysis.\n",
     "\n",
@@ -234,11 +234,13 @@
     "    Returns:\n",
     "    The concordance index.\n",
     "    \"\"\"\n",
-    "    order = np.argsort(T)\n",
-    "    P = np.asarray(P)[order]\n",
-    "    T = np.asarray(T)[order]\n",
-    "    E = np.asarray(E)[order]\n",
-    "    concordant_pairs, total_pairs = count_pairs(T, P, E)\n",
+    "    if event_observed is None:\n",
+    "        event_observed = np.ones(len(event_times))\n",
+    "    order = np.argsort(event_times)\n",
+    "    predicted_scores = np.asarray(predicted_scores)[order]\n",
+    "    event_times = np.asarray(event_times)[order]\n",
+    "    event_observed = np.asarray(event_observed)[order]\n",
+    "    concordant_pairs, total_pairs = count_pairs(event_times, predicted_scores, event_observed)\n",
     "    \n",
     "    if total_pairs == 0:\n",
     "        return 0\n",
@@ -247,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 39,
    "id": "051083de",
    "metadata": {},
    "outputs": [],
@@ -264,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 40,
    "id": "7f5b0643",
    "metadata": {},
    "outputs": [],
@@ -274,12 +276,12 @@
     "    b = np.random.rand(100)*100\n",
     "    e = np.round(np.random.rand(100))\n",
     "    \n",
-    "    assert concordance_index(a, b, e) == concordance_index_self(a, b, e), f\"{a}, {b}, {e}\""
+    "    assert concordance_index(a, b) == concordance_index_self(a, b), f\"{a}, {b}, {e}\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 36,
    "id": "d6fdf195",
    "metadata": {},
    "outputs": [],
@@ -296,15 +298,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 37,
    "id": "d7ba9fdc",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "652 ms ± 7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "684 ms ± 4.06 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -314,15 +316,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 38,
    "id": "3efaefab",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "21.9 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "22.7 ms ± 128 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],

survivors/metrics.py

@@ -2,7 +2,7 @@
 import numpy as np
 from numba import njit, jit
 from lifelines import KaplanMeierFitter, NelsonAalenFitter
-from lifelines.utils import concordance_index
+# from lifelines.utils import concordance_index
 
 from .constants import TIME_NAME, CENS_NAME
 
@@ -498,6 +498,45 @@ def bic(k, n, time, cens, sf, cumhf, bins):
     return k*np.log(n) - 2*loglikelihood(time, cens, sf, cumhf, bins)
 
 
+@njit
+def count_pairs(T, P, E):
+    n = len(T)
+    concordant_pairs = 0
+    total_pairs = 0
+    for i in range(n):
+        for j in range(i + 1, n):
+            if E[i] == 1 and T[i] <= T[j]:
+                total_pairs += 1
+                concordant_pairs += P[i] < P[j]
+                concordant_pairs += 0.5 * (P[i] == P[j])
+    return concordant_pairs, total_pairs
+
+
+def concordance_index_self(event_times, predicted_scores, event_observed=None):
+    """
+    Calculates the concordance index (C-index) for survival analysis.
+
+    Args:
+    event_times: Array of true event times.
+    predicted_scores: Array of predicted event times.
+    event_observed: Array of event indicators (1 if event occurred, 0 if censored).
+
+    Returns:
+    The concordance index.
+    """
+    if event_observed is None:
+        event_observed = np.ones(len(event_times))
+    order = np.argsort(event_times)
+    predicted_scores = np.asarray(predicted_scores)[order]
+    event_times = np.asarray(event_times)[order]
+    event_observed = np.asarray(event_observed)[order]
+    concordant_pairs, total_pairs = count_pairs(event_times, predicted_scores, event_observed)
+
+    if total_pairs == 0:
+        return 0
+    return concordant_pairs / total_pairs
+
+
 """ ESTIMATE FUNCTION """