fix nofeatures model (remove coef)

pyrocov/mutrans.py

@@ -491,7 +491,8 @@ def model(dataset, model_type, *, forecast_steps=None):
     # Configure reparametrization (which does not affect model density).
     reparam = {}
     if "reparam" in model_type:
-        reparam["coef"] = LocScaleReparam()
+        if "nofeatures" not in model_type:
+            reparam["coef"] = LocScaleReparam()
         if "localrate" in model_type or "nofeatures" in model_type:
             reparam["rate_loc"] = LocScaleReparam()
         if "localinit" in model_type:
@@ -501,22 +502,22 @@ def model(dataset, model_type, *, forecast_steps=None):
     with poutine.reparam(config=reparam):
 
         # Sample global random variables.
-        coef_scale = pyro.sample("coef_scale", dist.LogNormal(-4, 2))
+        if "nofeatures" not in model_type:
+            coef_scale = pyro.sample("coef_scale", dist.LogNormal(-4, 2))
         rate_scale = pyro.sample("rate_scale", dist.LogNormal(-4, 2))
         init_scale = pyro.sample("init_scale", dist.LogNormal(0, 2))
-        if "localrate" in model_type:
-            rate_loc_scale = pyro.sample("rate_loc_scale", dist.LogNormal(-4, 2))
-        if "nofeatures" in model_type:
+        if "localrate" or "nofeatures" in model_type:
             rate_loc_scale = pyro.sample("rate_loc_scale", dist.LogNormal(-4, 2))
         if "localinit" in model_type:
             init_loc_scale = pyro.sample("init_loc_scale", dist.LogNormal(0, 2))
 
         # Assume relative growth rate depends strongly on mutations and weakly
         # on clade and place. Assume initial infections depend strongly on
         # clade and place.
-        coef = pyro.sample(
-            "coef", dist.Laplace(torch.zeros(F), coef_scale).to_event(1)
-        )  # [F]
+        if "nofeatures" not in model_type:
+            coef = pyro.sample(
+                "coef", dist.Laplace(torch.zeros(F), coef_scale).to_event(1)
+            )  # [F]
         with clade_plate:
             if "localrate" in model_type:
                 rate_loc = pyro.sample(
@@ -959,33 +960,35 @@ def log_stats(dataset: dict, result: dict) -> dict:
     stats = {k: float(v) for k, v in result["median"].items() if v.numel() == 1}
     stats["loss"] = float(np.median(result["losses"][-100:]))
     mutations = dataset["mutations"]
-    mean = result["mean"]["coef"].cpu()
-    if not mean.shape:
-        return stats  # Work around error in map estimation.
-    logger.info(
-        "Dense data has shape {} totaling {} sequences".format(
-            " x ".join(map(str, dataset["weekly_clades"].shape)),
-            int(dataset["weekly_clades"].sum()),
+
+    if "coef" in result["mean"]:
+        mean = result["mean"]["coef"].cpu()
+        if not mean.shape:
+            return stats  # Work around error in map estimation.
+        logger.info(
+            "Dense data has shape {} totaling {} sequences".format(
+                " x ".join(map(str, dataset["weekly_clades"].shape)),
+                int(dataset["weekly_clades"].sum()),
+            )
         )
-    )
 
-    # Statistical significance.
-    std = result["std"]["coef"].cpu()
-    sig = mean.abs() / std
-    logger.info(f"|μ|/σ [median,max] = [{sig.median():0.3g},{sig.max():0.3g}]")
-    stats["|μ|/σ median"] = sig.median()
-    stats["|μ|/σ max"] = sig.max()
+        # Statistical significance.
+        std = result["std"]["coef"].cpu()
+        sig = mean.abs() / std
+        logger.info(f"|μ|/σ [median,max] = [{sig.median():0.3g},{sig.max():0.3g}]")
+        stats["|μ|/σ median"] = sig.median()
+        stats["|μ|/σ max"] = sig.max()
 
-    # Effects of individual mutations.
-    for name in ["S:D614G", "S:N501Y", "S:E484K", "S:L452R"]:
-        if name not in mutations:
-            continue
-        i = mutations.index(name)
-        m = mean[i] * 0.01
-        s = std[i] * 0.01
-        logger.info(f"ΔlogR({name}) = {m:0.3g} ± {s:0.2f}")
-        stats[f"ΔlogR({name}) mean"] = m
-        stats[f"ΔlogR({name}) std"] = s
+        # Effects of individual mutations.
+        for name in ["S:D614G", "S:N501Y", "S:E484K", "S:L452R"]:
+            if name not in mutations:
+                continue
+            i = mutations.index(name)
+            m = mean[i] * 0.01
+            s = std[i] * 0.01
+            logger.info(f"ΔlogR({name}) = {m:0.3g} ± {s:0.2f}")
+            stats[f"ΔlogR({name}) mean"] = m
+            stats[f"ΔlogR({name}) std"] = s
 
     # Growth rates of individual clades.
     rate = quotient_central_moments(