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inference-train-adni-test-macc.py
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inference-train-adni-test-macc.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Tests a model on NUS data that had been trained on ADNI data
"""
import glob
import os
import string
import numpy as np
import pandas as pd
import scipy.stats as sp_stats
from framework import marginalizable_mixture_model as mixmodel
from util import util_adni as data_adni
from util import util_macc as data_macc
from util import util_post_hoc as util_ph
from util import util_state_space as util
pd.options.display.width = 79
pd.options.display.max_columns = 1000
pd.options.display.max_colwidth = 79
pd.options.display.float_format = "{:,.3f}".format
home_dir = os.path.dirname(os.path.abspath(__file__))
n_clusters = 3
hex_hash = "49cb9120d3a1713763a0bdbdb97779bb"
def main():
ztrain_orig, xtrain, *_ = data_adni.get_trajectories()
ztrain, std_param = util.standardize(ztrain_orig, return_params=True)
(
ztest_orig,
xtest,
dtest,
mmse_cdrsum_cdrglobal_test,
lengthtest,
idstest,
agestest,
) = data_macc.get_data()
ztest = util.standardize(ztest_orig, params=std_param)
final_dx = data_macc.get_final_diagnoses(dtest)
demog = data_macc.get_demographics(idstest)
pfile = sorted(
glob.glob(f"./tmp/mmm-{hex_hash}*"),
key=os.path.getmtime,
).pop()
best_mdl = mixmodel.MMLinGaussSS_marginalizable.from_pickle(
pfile, training_data={"states": ztrain, "observations": xtrain}
)
ctest_all = np.array(
[
best_mdl.correspondence[c]
for c in best_mdl.mle_cluster_assignment(
states=ztest, observations=xtest
)
]
)
indextest_all = best_mdl.cluster_assignment_index(
states=ztest, observations=xtest
)
df_mmse = (
pd.DataFrame(
data={"cluster": ctest_all.ravel(), "health_index": indextest_all},
index=idstest.ravel(),
)
.join(
pd.DataFrame(
index=np.tile(idstest, (max(lengthtest), 1)).ravel(),
data=dict(
zip(
"mmse_cdrsum_cdrglobal".split("_"),
map(
np.ravel,
np.split(mmse_cdrsum_cdrglobal_test, 3, axis=-1),
),
)
)
| {
"age": agestest.ravel(),
"time_in_years": 2
* np.repeat(np.arange(ztest.shape[0]), ztest.shape[1]),
"age_diff": np.concatenate(
[
np.expand_dims(np.zeros_like(agestest[0]), axis=0),
np.diff(agestest, axis=0),
],
axis=0,
).ravel(),
},
)
)
.loc[lambda df: ~df.mmse.isna()]
.reset_index()
)
ctest_snap = np.array(
[
best_mdl.correspondence[c]
for c in best_mdl.mle_cluster_assignment(
states=util.mask_all_but_time_i_vect(ztest, lengthtest - 1),
observations=util.mask_all_but_time_i_vect(
xtest, lengthtest - 1
),
)
]
)
ctest_moca = np.array(
[
best_mdl.correspondence[c]
for c in best_mdl.mle_cluster_assignment(
states=np.nan * ztest, observations=xtest
)
]
)
pd.concat(
[
pd.crosstab(
pd.Series(
cs,
name="cluster",
),
pd.Series(final_dx.ravel(), name="diagnosis"),
normalize="index",
)[data_macc.diagnosis_list].stack()
for cs in [ctest_all, ctest_moca, ctest_snap]
],
axis=1,
).rename(
columns={
0: "trajectories",
1: "cognitive_only",
2: "single_assessment",
}
).to_csv(
os.path.join(
home_dir,
"figure-metadata-macc",
"counts_by_cluster_and_outcome_macc.csv",
)
)
print(
pd.concat(
[
pd.Series(cs).value_counts(normalize=True)[
list(string.ascii_uppercase[:n_clusters])
]
for cs in [ctest_all, ctest_moca, ctest_snap]
],
axis=1,
).rename(
columns={
0: "trajectories",
1: "cognitive_only",
2: "single_assessment",
}
)
)
for ns, cs in {
"all": ctest_all,
"moca_only": ctest_moca,
"snapshot": ctest_snap,
}.items():
χ2 = sp_stats.chi2_contingency(
pd.crosstab(
pd.Series(
cs,
name="cluster",
),
pd.Series(final_dx.ravel(), name="diagnosis"),
)[data_macc.diagnosis_list].values
)
print(
"Pearson's χ^2 contingency test for {}: ".format(ns)
+ "stat={stat:.2f}, pval={pval:.2E}, dof={dof}".format(
stat=χ2[0], pval=χ2[1], dof=χ2[2]
)
)
util.pie(
assignments=cs,
savename=os.path.join(
"figures", f"MACC_{n_clusters}_overall_pie_{ns}.pdf"
),
colors=data_macc.cluster_colors,
)
util.pie(
assignments=final_dx.ravel(),
savename=os.path.join(
"figures", f"MACC_{n_clusters}_overall_pie_{ns}_dx.pdf"
),
colors=data_macc.diagnosis_colors,
cluster_ordering=data_macc.diagnosis_list,
)
util.pies_by_cluster(
savename=os.path.join(
"figures",
f"MACC_{n_clusters}_pie_charts_{ns}.pdf",
),
categories=final_dx.ravel(),
category_ordering=data_macc.diagnosis_list,
category_legend_names={
"NCI": "CN",
"CIND": "mild MCI",
"VCIND": "moderate MCI",
"AD": "AD",
},
clusters=cs,
legend_bbox_to_anchor=(2.25 - 0.4 * int(n_clusters == 3), 1),
fig_length=6.0,
fig_width=4.0 + 0.5 * int(n_clusters == 3),
halo_colors=data_macc.cluster_colors,
slice_colors=data_macc.diagnosis_colors,
)
util.pies_by_cluster(
savename=os.path.join(
"figures",
f"MACC_{n_clusters}_pie_charts_{ns}_dx.pdf",
),
categories=cs,
clusters=final_dx,
cluster_ordering=data_macc.diagnosis_list,
halo_colors=data_macc.diagnosis_colors,
slice_colors=data_macc.cluster_colors,
legend_bbox_to_anchor=(2.25 - 0.4 * int(n_clusters == 3), 1),
fig_length=6.0,
fig_width=4.0 + 0.5 * int(n_clusters == 3),
)
for name, met in dict(
zip(
["MMSE", "CDR (sum)", "CDR (global)"],
np.split(mmse_cdrsum_cdrglobal_test, 3, axis=-1),
)
).items():
util.plot_metric_vs_clusters_over_time(
metric=met.squeeze(),
assignments=cs,
metric_name=name,
savename=f"figures/"
f"MACC_trajectories_{util.make_str_nice(name)}"
f"_all{n_clusters}c_{ns}.pdf",
title="",
xticks=np.array([0, 2, 4]),
xlabel="Years from baseline",
legend_loc="upper right",
)
util.histograms_by_cluster(
metrics=indextest_all.reshape(-1, 1),
clusters=ctest_all,
savename=os.path.join(
"figures",
f"MACC_{n_clusters}c_index_by_cluster.pdf",
),
nbins=30,
nrows=1,
ncols=1,
metric_names=[""],
mean_overlay=False,
density=True,
title="",
tighten=False,
)
df_mmse.assign(
mmse_age_adjusted=lambda df: util_ph.regressed_out_effect_cv(
df.mmse.values.reshape(-1, 1), df.age.values.reshape(-1, 1)
)
).to_csv(
os.path.join(
"posthoc", "results", "mmse_by_cluster_over_time_macc.csv"
),
index=None,
)
contingency = pd.crosstab(
pd.Series(ctest_all, name="cluster"),
pd.Series(final_dx.ravel(), name="diagnosis"),
normalize="index",
)[data_macc.diagnosis_list]
print(contingency.div(contingency.sum(axis=0), axis=1))
print(
demog.assign(cluster=ctest_all, age=agestest[0])
.groupby("cluster")
.agg("mean")[["age", "is_female", "edu_yrs", "apoe4_pos"]]
)
if __name__ == "__main__":
main()
"""
trajectories cognitive_only single_assessment
A 0.203 0.392 0.361
B 0.120 0.297 0.215
C 0.677 0.310 0.424
Pearson's χ^2 contingency test for all: stat=39.48, pval=5.77E-07, dof=6
Pearson's χ^2 contingency test for moca_only: stat=97.18, pval=9.72E-19, dof=6
Pearson's χ^2 contingency test for snapshot: stat=54.30, pval=6.41E-10, dof=6
diagnosis NCI CIND VCIND AD
cluster
A 0.600 0.207 0.581 0.098
B 0.144 0.568 0.245 0.166
C 0.256 0.225 0.174 0.736
age is_female edu_yrs apoe4_pos
cluster
A 73.641 0.344 8.812 0.219
B 74.973 0.211 8.684 0.158
C 72.390 0.673 7.421 0.355
"""