Added demography.py module, added jointplots

demography.py

@@ -0,0 +1,115 @@
+import msprime
+import math
+
+
+def onepop_constant(args):
+    """Single population model with pop size Ne and constant growth"""
+
+    genob, params, randomize, i, proposals, seed = args
+    necessary_params = ["mu", "r", "Ne"]
+    assert sorted(necessary_params) == sorted(
+        list(params.keys())
+    ), "Invalid combination of parameters. Needed: mu | r | Ne"
+
+    if proposals:
+        mu, r, Ne = [
+            params[p].prop(i) if params[p].inferable else params[p].val
+            for p in necessary_params
+        ]
+    else:
+        mu, r, Ne = [
+            params[p].rand() if randomize else params[p].val for p in necessary_params
+        ]
+
+    ts = msprime.simulate(
+        sample_size=genob.num_samples,
+        Ne=Ne,
+        length=genob.seq_len,
+        mutation_rate=mu,
+        recombination_rate=r,
+        random_seed=seed,
+    )
+
+    return ts
+
+
+def onepop_exp(args):
+    """Single population model with sudden population size increase from N1 to N2
+    at time T1 and exponential growth at time T2"""
+
+    genob, params, randomize, i, proposals, seed = args
+    necessary_params = ["mu", "r", "T1", "N1", "T2", "N2", "growth"]
+    assert sorted(necessary_params) == sorted(
+        list(params.keys())
+    ), "Invalid combination of parameters. Needed: mu | r | T1 | N1 | T2 | N2 | growth"
+
+    if proposals:
+        mu, r, T1, N1, T2, N2, growth = [
+            params[p].prop(i) if params[p].inferable else params[p].val
+            for p in necessary_params
+        ]
+    else:
+        mu, r, T1, N1, T2, N2, growth = [
+            params[p].rand() if randomize else params[p].val for p in necessary_params
+        ]
+
+    N0 = N2 / math.exp(growth * T2)
+
+    # Time is given in generations unit (t/25)
+    demographic_events = [
+        msprime.PopulationParametersChange(time=0, initial_size=N0, growth_rate=growth),
+        msprime.PopulationParametersChange(time=T2, initial_size=N2, growth_rate=0),
+        msprime.PopulationParametersChange(time=T1, initial_size=N1),
+    ]
+
+    ts = msprime.simulate(
+        sample_size=genob.num_samples,
+        demographic_events=demographic_events,
+        length=genob.seq_len,
+        mutation_rate=mu,
+        recombination_rate=r,
+        random_seed=seed,
+    )
+
+    return ts
+
+
+def onepop_migration(args):
+    """Mass migration at time T1 from population 1 with pop size N2 to population
+    0 with pop size N1. Samples are collected only from population 0."""
+
+    genob, params, randomize, i, proposals, seed = args
+    necessary_params = ["mu", "r", "T1", "N1", "N2", "mig"]
+    assert sorted(necessary_params) == sorted(list(params.keys())), (
+        "Invalid combination of parameters. Needed: mu | r | T1 | N1 | N2 | mig \n"
+        f"Obtained: {list(params.keys())}"
+    )
+
+    if proposals:
+        mu, r, T1, N1, N2, mig = [
+            params[p].prop(i) if params[p].inferable else params[p].val
+            for p in necessary_params
+        ]
+    else:
+        mu, r, T1, N1, N2, mig = [
+            params[p].rand() if randomize else params[p].val for p in necessary_params
+        ]
+
+    population_configurations = [
+        msprime.PopulationConfiguration(sample_size=genob.num_samples, initial_size=N1),
+        msprime.PopulationConfiguration(sample_size=0, initial_size=N2),
+    ]
+
+    # migration from pop 1 into pop 0 (back in time)
+    mig_event = msprime.MassMigration(time=T1, source=1, destination=0, proportion=mig)
+
+    ts = msprime.simulate(
+        population_configurations=population_configurations,
+        demographic_events=[mig_event],
+        length=genob.seq_len,
+        mutation_rate=mu,
+        recombination_rate=r,
+        random_seed=seed,
+    )
+
+    return ts

genobuilder.py

@@ -1,7 +1,6 @@
 import os
 from collections import OrderedDict
 import concurrent.futures
-import msprime
 import pickle
 import argparse
 import stdpopsim
@@ -11,6 +10,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
+import demography as dm
 from parameter import Parameter
 
 _ex = None
@@ -24,27 +24,11 @@ def executor(p):
 
 
 def do_sim(args):
-    genob, params, randomize, i, proposals, seed = args
-    rng = random.Random(seed)
 
-    if proposals:
-        Ne, mu, r = [
-            params[p].prop(i) if params[p].inferable else params[p].val
-            for p in ("Ne", "mu", "r")
-        ]
-    else:
-        Ne, mu, r = [
-            params[p].rand() if randomize else params[p].val for p in ("Ne", "mu", "r")
-        ]
-
-    ts = msprime.simulate(
-        sample_size=genob.num_samples,
-        Ne=Ne,
-        length=genob.seq_len,
-        mutation_rate=mu,
-        recombination_rate=r,
-        random_seed=seed,
-    )
+    seed = args[5]
+    genob = args[0]
+    rng = random.Random(seed)
+    ts = dm.onepop_exp(args)
 
     return genob._resize_from_ts(ts, rng)
 
@@ -765,9 +749,24 @@ def vcf2zarr(vcf_files, pop_file, zarr_path):
     args = parser.parse_args()
     params_dict = OrderedDict()
 
-    params_dict["r"] = Parameter("r", 1.25e-9, 1e-10, (1e-11, 1e-7), inferable=True)
-    params_dict["mu"] = Parameter("mu", 1.25e-8, 1e-9, (1e-10, 1e-7), inferable=False)
-    params_dict["Ne"] = Parameter("Ne", 10000, 14000, (5000, 15000), inferable=True)
+    params_dict["r"] = Parameter("r", 2e-8, 1e-10, (1e-11, 1e-7), inferable=False)
+    params_dict["mu"] = Parameter("mu", 1.29e-8, 1e-9, (1e-10, 1e-7), inferable=False)
+    # params_dict["Ne"] = Parameter("Ne", 10000, 14000, (5000, 15000), inferable=True)
+
+    # For onepop_exp model:
+    params_dict["T1"] = Parameter("T1", 3000, 4000, (1500, 5000), inferable=True)
+    params_dict["N1"] = Parameter("N1", 10000, 20000, (1000, 30000), inferable=True)
+    params_dict["T2"] = Parameter("T2", 500, 1000, (100, 1500), inferable=False)
+    params_dict["N2"] = Parameter("N2", 5000, 20000, (1000, 20000), inferable=True)
+    params_dict["growth"] = Parameter("growth", 0.01, 0.02, (0, 0.05), inferable=True)
+
+    # For onepop_migration model:
+    """
+    params_dict["T1"] = Parameter("T1", 1000, 4000, (500, 5000), inferable=False)
+    params_dict["N1"] = Parameter("N1", 5000, 18000, (1000, 20000), inferable=False)
+    params_dict["N2"] = Parameter("N2", 8000, 15000, (1000, 20000), inferable=False)
+    params_dict["mig"] = Parameter("mig", 0.9, 0.2, (0, 0.3), inferable=True)
+    """
 
     genob = Genobuilder(
         source=args.source,

genomcmcgan.py

@@ -69,12 +69,7 @@ def run_genomcmcgan(
     """
     xtest = genob.generate_fakedata(num_reps=1000)
     test_data = tf.data.Dataset.from_tensor_slices((xtest.astype("float32")))
-    test_data = (
-          test_data
-          .cache()
-          .batch(batch_size)
-          .prefetch(2)
-    )
+    test_data = test_data.cache().batch(batch_size).prefetch(2)
     """
 
     print("Data simulation finished")
@@ -125,7 +120,7 @@ def run_genomcmcgan(
 
     while not convergence and max_num_iters != it:
 
-        print("Starting the MCMC sampling chain")
+        print(f"Starting the MCMC sampling chain for iteration {it}")
         start_t = time.time()
 
         is_accepted, log_acc_rate = mcmcgan.run_chain()
@@ -134,7 +129,9 @@ def run_genomcmcgan(
         # Draw traceplot and histogram of collected samples
         mcmcgan.traceplot_samples(inferable_params, it)
         mcmcgan.hist_samples(inferable_params, it)
-        mcmcgan.jointplot(it)
+        print(mcmcgan.samples.shape)
+        if mcmcgan.samples.shape[1] == 2:
+            mcmcgan.jointplot_samples(inferable_params, it)
 
         for i, p in enumerate(inferable_params):
             p.proposals = mcmcgan.samples[:, i].numpy()
@@ -176,7 +173,7 @@ def run_genomcmcgan(
         print(f"A single iteration of the MCMC-GAN took {t} seconds")
 
     print(f"The estimates obtained after {it} iterations are:")
-    print(means)
+    print(float(means))
 
 
 if __name__ == "__main__":

mcmcgan.py

@@ -219,7 +219,9 @@ def D(self, x, num_reps=64):
         self.genob.num_reps = num_reps
 
         return tf.reduce_mean(
-            self.discriminator.predict(self.genob.simulate_msprime(x).astype("float32"))
+            self.discriminator.predict_on_batch(
+                self.genob.simulate_msprime(x).astype("float32")
+            )
         )
 
     # Where `D(x)` is the average discriminator output from n independent
@@ -300,7 +302,7 @@ def setup_mcmc(
             self.mcmc_kernel = tfp.mcmc.DualAveragingStepSizeAdaptation(
                 mcmc,
                 num_adaptation_steps=int(self.num_burnin_steps * 0.8),
-                target_accept_prob=0.75,
+                target_accept_prob=0.3,
                 step_size_setter_fn=lambda pkr, new_step_size: pkr._replace(
                     step_size=new_step_size
                 ),
@@ -356,7 +358,7 @@ def run_chain(self):
 
     def hist_samples(self, params, it, bins=10):
 
-        colors = ["b", "g", "r"]
+        colors = ["red", "blue", "green", "black", "gold", "chocolate", "teal"]
         for i, p in enumerate(params):
             sns.distplot(self.samples[:, i], color=colors[i])
             ymax = plt.ylim()[1]
@@ -375,8 +377,10 @@ def hist_samples(self, params, it, bins=10):
     def traceplot_samples(self, params, it):
 
         # EXPAND COLORS FOR MORE PARAMETERS
-        colors = ["b", "g", "r"]
+        colors = ["red", "blue", "green", "black", "gold", "chocolate", "teal"]
         sns.set_style("darkgrid")
+        print(len(params))
+        print(self.samples.shape)
         for i, p in enumerate(params):
             plt.plot(self.samples[:, i], c=colors[i], alpha=0.3)
             plt.hlines(
@@ -400,13 +404,18 @@ def traceplot_samples(self, params, it):
             )
             plt.clf()
 
-    def jointplot(self, it):
+    def jointplot_samples(self, params, it):
+
+        p1 = list(params.values())[0]
+        p2 = list(params.values())[1]
 
         g = sns.jointplot(self.samples[:, 0], self.samples[:, 1], kind="kde")
         g.plot_joint(sns.kdeplot, color="b", zorder=0, levels=6)
         g.plot_marginals(sns.rugplot, color="r", height=-0.15, clip_on=False)
-        plt.xlabel("P1")
-        plt.ylabel("P2")
+        plt.xlim(p1.bounds)
+        plt.ylim(p2.bounds)
+        plt.xlabel(p1.name)
+        plt.ylabel(p2.name)
         plt.title(f"Jointplot at iteration {it}")
         plt.savefig(f"./results/jointplot_{it}.png")
         plt.clf()