From f9ca9ba8cfdfe0e07e6be8db59e6b6689c3e9965 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Czy=C5=BC?= <pczyz@protonmail.com>
Date: Thu, 7 Dec 2023 13:16:22 +0100
Subject: [PATCH] Add example

---
 TreeMHN-JAX-demo.py | 223 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 223 insertions(+)
 create mode 100644 TreeMHN-JAX-demo.py

diff --git a/TreeMHN-JAX-demo.py b/TreeMHN-JAX-demo.py
new file mode 100644
index 0000000..0c1b0e9
--- /dev/null
+++ b/TreeMHN-JAX-demo.py
@@ -0,0 +1,223 @@
+from collections import Counter
+
+import jax
+import jax.numpy as jnp
+import joblib
+import numpy as np
+import pmhn._trees._backend_jax as bj
+import pmhn._trees._simulate as sim
+
+dim = 30
+theta = -3 * np.eye(dim)
+n_trees: int = 100
+min_size: int = 10
+max_size: int = 15
+
+dim = 3
+theta = -3 * np.eye(dim)
+n_trees: int = 100
+min_size: int = 3
+max_size: int = 5
+
+
+def sample_trees():
+    trees = []
+    rng = np.random.default_rng(42)
+    for i in range(n_trees):
+        tree, _ = sim.generate_valid_tree(
+            rng,
+            theta=theta,
+            sampling_time=1,
+            mean_sampling_time=1,
+            min_tree_size=min_size,
+            max_tree_size=max_size,
+        )
+        trees.append(tree)
+    return trees
+
+
+trees = sample_trees()
+
+print(Counter([a.size for a in trees]))
+
+
+def _wrap(tree):
+    return bj.wrap_tree(tree, n_genes=dim)[0]
+
+
+import time
+
+t0 = time.time()
+wrapped_trees = joblib.Parallel(n_jobs=-1)(
+    joblib.delayed(_wrap)(tree) for tree in trees
+)
+t1 = time.time()
+
+print(f"Wrapping time: {t1-t0:.2f}")
+
+
+def logp(theta, tree):
+    return bj.loglikelihood(theta, omega=jnp.zeros(dim), tree=tree)
+
+
+jit_grad = jax.jit(jax.grad(logp))
+jit_logp = jax.jit(logp)
+
+
+def logp_total(theta):
+    return jnp.sum(jnp.asarray([jit_logp(theta, wrap) for wrap in wrapped_trees]))
+
+
+def grad_total(theta):
+    return jnp.sum(jnp.asarray([jit_grad(theta, wrap) for wrap in wrapped_trees]))
+
+
+logp_total = jax.jit(logp_total)
+
+
+# Call for the first time for JIT compilation
+t0 = time.time()
+
+_ = logp_total(jnp.eye(dim))
+_ = grad_total(jnp.eye(dim))
+
+t1 = time.time()
+
+print(f"Compilation time: {t1-t0:.2f}")
+
+# See how quickly JAX finds likelihood
+n_reps: int = 3
+
+t0 = time.time()
+
+ps = []
+for i in range(n_reps):
+    theta = jnp.eye(dim) + i / n_reps
+    ps.append(logp_total(theta))
+
+repr(ps)
+
+t1 = time.time()
+
+average_time = (t1 - t0) / n_reps
+print(f"JAX logp evaluation time: {average_time:.2f}")
+
+t0 = time.time()
+gs = []
+for i in range(n_reps):
+    theta = jnp.eye(dim) + i / n_reps
+    gs.append(grad_total(theta))
+
+repr(gs)
+t1 = time.time()
+print(f"JAX grad evaluation time: {average_time:.2f}")
+
+# Compare with original implementation
+
+import pmhn._trees._backend_code as bc
+
+wrappers = [bc.TreeWrapperCode(t) for t in trees]
+backend = bc.TreeMHNBackendCode()
+
+
+def get_loglike(theta):
+    return np.sum(
+        [
+            backend.loglikelihood(
+                w, theta, sampling_rate=1.0, all_mut=set(range(1, dim + 1))
+            )
+            for w in wrappers
+        ]
+    )
+
+
+n_reps: int = 3
+
+t0 = time.time()
+
+ps = []
+for i in range(n_reps):
+    theta = np.eye(dim) + i / n_reps
+    ps.append(get_loglike(theta))
+
+
+t1 = time.time()
+
+average_time = (t1 - t0) / n_reps
+print(f"Python evaluation time: {average_time:.2f}")
+
+# See if both versions agree
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+fig, axs = plt.subplots(1, 2, figsize=(8, 3), dpi=250)
+
+rng = np.random.default_rng(21)
+
+thetas = [
+    -np.eye(dim, dtype=np.float64),
+    rng.normal(size=(dim, dim)),
+    3 * rng.normal(size=(dim, dim)),
+]
+
+
+backend = bc.TreeMHNBackendCode(jitter=1e-20)
+backend2 = bc.TreeMHNBackendCode(jitter=1e-10)
+
+for i, theta in enumerate(thetas):
+    jax_vals = np.asarray([jit_logp(theta, t) for t in wrapped_trees])
+
+    pyt_vals = np.asarray(
+        [
+            backend.loglikelihood(
+                t, theta, sampling_rate=1.0, all_mut=set(range(1, dim + 1))
+            )
+            for t in wrappers
+        ]
+    )
+
+    pyt2_vals = np.asarray(
+        [
+            backend2.loglikelihood(
+                t, theta, sampling_rate=1.0, all_mut=set(range(1, dim + 1))
+            )
+            for t in wrappers
+        ]
+    )
+
+    ax = axs[0]
+    ax.plot(
+        pyt_vals,
+        pyt_vals,
+        c="k",
+        alpha=1.0,
+        rasterized=True,
+        linestyle="--",
+        linewidth=0.1,
+    )
+    ax.scatter(pyt_vals, jax_vals, s=2, rasterized=True, c=f"C{i}")
+
+    ax = axs[1]
+    ax.plot(
+        pyt_vals,
+        pyt_vals,
+        c="k",
+        alpha=1.0,
+        rasterized=True,
+        linestyle="--",
+        linewidth=0.1,
+    )
+    ax.scatter(pyt_vals, pyt2_vals, s=2, rasterized=True, c=f"C{i}")
+
+ax = axs[0]
+ax.set_xlabel("$\\log P_\\text{Python}$\njitter=$10^{-20}$")
+ax.set_ylabel("$\\log P_\\text{JAX}$")
+
+ax = axs[1]
+ax.set_xlabel("$\\log P_\\text{Python}$\njitter=$10^{-20}$")
+ax.set_ylabel("$\\log P_\\text{Python}$\njitter=$10^{-10}$")
+
+# ax.set_aspect('equal', 'box')
+fig.tight_layout()
+fig.savefig("jax_vs_python.pdf")