From 3dc1df990782294b4ede4b57d7bf889993ed0e45 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 2 Apr 2024 15:01:08 -0600 Subject: [PATCH 01/24] Adding a testing dataset from the WW repo --- model/pyproject.toml | 3 ++ model/src/pyrenew/datasets/__init__.py | 3 ++ model/src/pyrenew/datasets/example_df.R | 11 +++++ model/src/pyrenew/datasets/example_df.rda | Bin 0 -> 4533 bytes model/src/pyrenew/datasets/wastewater.py | 47 ++++++++++++++++++++++ model/src/test/test_datasets.py | 12 ++++++ 6 files changed, 76 insertions(+) create mode 100644 model/src/pyrenew/datasets/__init__.py create mode 100644 model/src/pyrenew/datasets/example_df.R create mode 100644 model/src/pyrenew/datasets/example_df.rda create mode 100644 model/src/pyrenew/datasets/wastewater.py create mode 100644 model/src/test/test_datasets.py diff --git a/model/pyproject.toml b/model/pyproject.toml index ee2f5aa4..a7d658a7 100755 --- a/model/pyproject.toml +++ b/model/pyproject.toml @@ -5,6 +5,9 @@ description = "Pyrenew: a package for Bayesian renewal modeling with JAX and Num authors = ["Dylan H. Morris "] license = "Apache 2.0" readme = "README.md" +include = [{path = "datasets/*.tsv"}] +exclude = [{path = "datasets/*.rds"}] + [tool.poetry.dependencies] python = "^3.10" diff --git a/model/src/pyrenew/datasets/__init__.py b/model/src/pyrenew/datasets/__init__.py new file mode 100644 index 00000000..ac4eee49 --- /dev/null +++ b/model/src/pyrenew/datasets/__init__.py @@ -0,0 +1,3 @@ +from pyrenew.datasets.wastewater import load_wastewater + +__all__ = ["load_wastewater"] diff --git a/model/src/pyrenew/datasets/example_df.R b/model/src/pyrenew/datasets/example_df.R new file mode 100644 index 00000000..81d970dd --- /dev/null +++ b/model/src/pyrenew/datasets/example_df.R @@ -0,0 +1,11 @@ +load("src/pyrenew/datasets/example_df.rda") + +# Saving as TSV +write.table( + x = example_df, + file = "src/pyrenew/datasets/wastewater.tsv", + sep = "\t", + quote = FALSE, + row.names = FALSE, + na = "" + ) diff --git a/model/src/pyrenew/datasets/example_df.rda b/model/src/pyrenew/datasets/example_df.rda new file mode 100644 index 0000000000000000000000000000000000000000..553640ea6ed1c7cee87671600e6240b1f95fd343 GIT binary patch literal 4533 zcmb_dX;jh)*9SrqG*i$@dnz#&F)?M-+)^U+VOK#+eXY#M#3@sw%{CV-F`e)aF5qq~ zqD^XPhGmwc7L^-li;ZQDqiJKA*vXBf8e42 zz=L3(^q|d~nI+QTLDTdEQ<4YBKH;vBWORsI2C@h72SBFz9{f&w5D}~{Vah>ENXFpw z!730a0m^eA@9`HIkplw>_eycf1(w@gJ8Xiq*CIdoR9$QwxGIlDbSv57mUzrV;C~)% z#LC@62AM-6%zPkLeu2whMoeJk?q9}QMog=y%5}678PZV1w7mEBFty?Um#g<;5i6)^ zdpw>5zqb-d)?OIm#|hMDmA5L?_VU{O%rjX-clqEm>Ma)xKe&_Ge9po*F$|#?_zuIT;-L=AS0^Cf4dz;&7R!ao z#92(o!?4d>cnM#{^o>IzWO{aqvRf6KTfIN2v;KhXwxtKHmOvB)Z~{2GFbmzEIbxzJ zHkIaMZ*i)gUFAI8gT08@aA&3X5OiggNwf707Tlzl59wq)+gIxaS|K&*?OOR4^wyHh z2YwTaPnTF-lQBRpu1@?Cb5IJ%6@S(Q^qVQ;rK-m|{hF2!<~m3m0E0*ZA)!u#x!LZJ z+XR2PpwkxY3UX)ASE;RISKc!3c^NukLA(uvyd{&aExlW4Zn?^wlzcA7#R*#GwyG*F zhn8}}oSC$Y7axDP)}=q}kptW%JU!e-_$WP7#M|$k-e_&&U7ui8*9On2wanGcW%JtV z>Qi>$Gk7^pp(vA@y60vwNtU(`+&gX!yKA+NL*itN8P*6%zHig&T z{_NquEL{}(d22>oO8-yo+~dDlP(y>Y@XT`;O(A9?2XOc@I0+)uf-;wo(n$_ovX>&i zg@PdP1B~7=XXm+^-IU7UP*C3+yt7l31?iab0WMKyS%5O>o1j*$wLeagss~g@*1@|* zrL*YLd#8sMfB%zPF{MM_sjaG+#vq&zN6wMfD-AT~i+}IzyX@fV=^?hd$;Z}+E4~ZT z&fjM_9@RF#;dDJR6Br34!?VUYO&&7p+Z%NAPjNYA3T|si9pE2kne&Ra zT%5=TAAD{;HfXASd}`t;cJJK@Z0|;A*fBHcX3;1nF4^E47Q}`G%>pU@Uw35xs2s)V7DYXPB{5{GL~pt!?XiiTr?`H6Zdmwrqid3GvkF8P%`u_&=zVTk@?kKxpZi z(uo%3%+H8&d=L*{%trw#Ok%9h&Rm*h?|S+HWR61lit8Q*BCyomo4R_&kWc4eqRJ^! 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zZ9}RMcfxR>_em6VjUwhW`lhnArVw_cmahC0V_bgk)eT{`b5q3(pBq;y964IFeV%hd zd$=!lk*3#Seek?(P2cf2N{P+w5e)KTNVX5`8FmnDe8#9DN%2?87>=}7W#(ewAUo pl.DataFrame: + """Load the wastewater dataset + + This dataset contains simulated entries of COVID-19 wastewater concentration + data. The dataset is used to demonstrate the use of the wastewater-informed + COVID-19 forecasting model. + + Returns + ------- + pl.DataFrame + The wastewater dataset + + Notes + ----- + + This dataset was downloaded directly from: + https://github.com/CDCgov/wastewater-informed-covid-forecasting/blob/292526383ece582f10823fc939c7e590ca349c6d/cfaforecastrenewalww/data/example_df.rda + + The dataset contains the following columns: + - `lab_wwtp_unique_id` + - `log_conc` + - `date` + - `lod_sewage` + - `below_LOD` + - `daily_hosp_admits` + - `daily_hosp_admits_for_eval` + - `pop` + - `forecast_date` + - `hosp_calibration_time` + - `site` + - `ww_pop` + - `inf_per_capita` + """ + + # Load the dataset + return pl.read_csv( + source=path("pyrenew.datasets", "wastewater.tsv"), + separator="\t", + ) diff --git a/model/src/test/test_datasets.py b/model/src/test/test_datasets.py new file mode 100644 index 00000000..05747566 --- /dev/null +++ b/model/src/test/test_datasets.py @@ -0,0 +1,12 @@ +import numpy.testing as testing +from pyrenew.datasets import load_wastewater + + +def test_loading_wastewater(): + """Test that the wastewater dataset can be properly loaded""" + df = load_wastewater() + assert len(df) > 0 + assert df.shape == (635, 14) + + testing.assert_approx_equal(df["daily_hosp_admits"].mean(), 12.8888, 3) + testing.assert_approx_equal(df["lod_sewage"].mean(), 3.841025, 3) From 44c837b9ba112885078cff52a1b9e52b7738050e Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 2 Apr 2024 15:14:03 -0600 Subject: [PATCH 02/24] Correcting how path to wastewater is retrieved --- model/src/pyrenew/datasets/wastewater.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model/src/pyrenew/datasets/wastewater.py b/model/src/pyrenew/datasets/wastewater.py index 504525ca..d7e09c6d 100644 --- a/model/src/pyrenew/datasets/wastewater.py +++ b/model/src/pyrenew/datasets/wastewater.py @@ -1,7 +1,7 @@ """This module loads the package dataset named 'wastewater' and provides functions to manipulate the data. It uses the 'polars' library""" -from importlib.resources import path +from importlib.resources import files import polars as pl @@ -42,6 +42,6 @@ def load_wastewater() -> pl.DataFrame: # Load the dataset return pl.read_csv( - source=path("pyrenew.datasets", "wastewater.tsv"), + source=files("pyrenew.datasets") / "wastewater.tsv", separator="\t", ) From 40f111980f7880c6af03b5283f529df502357406 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 2 Apr 2024 15:18:17 -0600 Subject: [PATCH 03/24] Adding missing file (TSV wastewater) --- model/src/pyrenew/datasets/wastewater.tsv | 636 ++++++++++++++++++++++ 1 file changed, 636 insertions(+) create mode 100644 model/src/pyrenew/datasets/wastewater.tsv diff --git a/model/src/pyrenew/datasets/wastewater.tsv b/model/src/pyrenew/datasets/wastewater.tsv new file mode 100644 index 00000000..3bc6ffb6 --- /dev/null +++ b/model/src/pyrenew/datasets/wastewater.tsv @@ -0,0 +1,636 @@ +t lab_wwtp_unique_id log_conc date lod_sewage below_LOD daily_hosp_admits daily_hosp_admits_for_eval pop forecast_date hosp_calibration_time site ww_pop inf_per_capita +1 1 2023-10-30 6 6 1e+06 2024-02-05 90 1 4e+05 0.000663426572737469 +1 2 2023-10-30 6 6 1e+06 2024-02-05 90 1 4e+05 0.000663426572737469 +1 3 2023-10-30 6 6 1e+06 2024-02-05 90 2 2e+05 0.000663426572737469 +1 4 2023-10-30 6 6 1e+06 2024-02-05 90 3 1e+05 0.000663426572737469 +1 5 2023-10-30 6 6 1e+06 2024-02-05 90 4 50000 0.000663426572737469 +2 1 2023-10-31 8 8 1e+06 2024-02-05 90 1 4e+05 0.00068300916315395 +2 2 2023-10-31 8 8 1e+06 2024-02-05 90 1 4e+05 0.00068300916315395 +2 3 8.36481035293498 2023-10-31 4.01075017260572 0 8 8 1e+06 2024-02-05 90 2 2e+05 0.00068300916315395 +2 4 2023-10-31 8 8 1e+06 2024-02-05 90 3 1e+05 0.00068300916315395 +2 5 2023-10-31 8 8 1e+06 2024-02-05 90 4 50000 0.00068300916315395 +3 1 2023-11-01 4 4 1e+06 2024-02-05 90 1 4e+05 0.000720865160318388 +3 2 2023-11-01 4 4 1e+06 2024-02-05 90 1 4e+05 0.000720865160318388 +3 3 2023-11-01 4 4 1e+06 2024-02-05 90 2 2e+05 0.000720865160318388 +3 4 2023-11-01 4 4 1e+06 2024-02-05 90 3 1e+05 0.000720865160318388 +3 5 2023-11-01 4 4 1e+06 2024-02-05 90 4 50000 0.000720865160318388 +4 1 8.06994711561617 2023-11-02 3.80549506716936 0 8 8 1e+06 2024-02-05 90 1 4e+05 0.00076104521535863 +4 2 2023-11-02 8 8 1e+06 2024-02-05 90 1 4e+05 0.00076104521535863 +4 3 2023-11-02 8 8 1e+06 2024-02-05 90 2 2e+05 0.00076104521535863 +4 4 2023-11-02 8 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9 1e+06 2024-02-05 90 1 4e+05 0.00115746811066289 +117 3 2024-02-23 9 1e+06 2024-02-05 90 2 2e+05 0.00115746811066289 +117 4 2024-02-23 9 1e+06 2024-02-05 90 3 1e+05 0.00115746811066289 +117 5 2024-02-23 9 1e+06 2024-02-05 90 4 50000 0.00115746811066289 +118 1 2024-02-24 7 1e+06 2024-02-05 90 1 4e+05 0.00119582408925606 +118 2 2024-02-24 7 1e+06 2024-02-05 90 1 4e+05 0.00119582408925606 +118 3 2024-02-24 7 1e+06 2024-02-05 90 2 2e+05 0.00119582408925606 +118 4 2024-02-24 7 1e+06 2024-02-05 90 3 1e+05 0.00119582408925606 +118 5 2024-02-24 7 1e+06 2024-02-05 90 4 50000 0.00119582408925606 +119 1 2024-02-25 7 1e+06 2024-02-05 90 1 4e+05 0.00123545884649584 +119 2 2024-02-25 7 1e+06 2024-02-05 90 1 4e+05 0.00123545884649584 +119 3 2024-02-25 7 1e+06 2024-02-05 90 2 2e+05 0.00123545884649584 +119 4 2024-02-25 7 1e+06 2024-02-05 90 3 1e+05 0.00123545884649584 +119 5 2024-02-25 7 1e+06 2024-02-05 90 4 50000 0.00123545884649584 +120 1 2024-02-26 6 1e+06 2024-02-05 90 1 4e+05 0.00131953405788595 +120 2 2024-02-26 6 1e+06 2024-02-05 90 1 4e+05 0.00131953405788595 +120 3 2024-02-26 6 1e+06 2024-02-05 90 2 2e+05 0.00131953405788595 +120 4 2024-02-26 6 1e+06 2024-02-05 90 3 1e+05 0.00131953405788595 +120 5 2024-02-26 6 1e+06 2024-02-05 90 4 50000 0.00131953405788595 +121 1 2024-02-27 7 1e+06 2024-02-05 90 1 4e+05 0.00137118304332127 +121 2 2024-02-27 7 1e+06 2024-02-05 90 1 4e+05 0.00137118304332127 +121 3 2024-02-27 7 1e+06 2024-02-05 90 2 2e+05 0.00137118304332127 +121 4 2024-02-27 7 1e+06 2024-02-05 90 3 1e+05 0.00137118304332127 +121 5 2024-02-27 7 1e+06 2024-02-05 90 4 50000 0.00137118304332127 +122 1 2024-02-28 14 1e+06 2024-02-05 90 1 4e+05 0.00143382583178741 +122 2 2024-02-28 14 1e+06 2024-02-05 90 1 4e+05 0.00143382583178741 +122 3 2024-02-28 14 1e+06 2024-02-05 90 2 2e+05 0.00143382583178741 +122 4 2024-02-28 14 1e+06 2024-02-05 90 3 1e+05 0.00143382583178741 +122 5 2024-02-28 14 1e+06 2024-02-05 90 4 50000 0.00143382583178741 +123 1 2024-02-29 13 1e+06 2024-02-05 90 1 4e+05 0.00149928295317067 +123 2 2024-02-29 13 1e+06 2024-02-05 90 1 4e+05 0.00149928295317067 +123 3 2024-02-29 13 1e+06 2024-02-05 90 2 2e+05 0.00149928295317067 +123 4 2024-02-29 13 1e+06 2024-02-05 90 3 1e+05 0.00149928295317067 +123 5 2024-02-29 13 1e+06 2024-02-05 90 4 50000 0.00149928295317067 +124 1 2024-03-01 9 1e+06 2024-02-05 90 1 4e+05 0.00156736770858743 +124 2 2024-03-01 9 1e+06 2024-02-05 90 1 4e+05 0.00156736770858743 +124 3 2024-03-01 9 1e+06 2024-02-05 90 2 2e+05 0.00156736770858743 +124 4 2024-03-01 9 1e+06 2024-02-05 90 3 1e+05 0.00156736770858743 +124 5 2024-03-01 9 1e+06 2024-02-05 90 4 50000 0.00156736770858743 +125 1 2024-03-02 15 1e+06 2024-02-05 90 1 4e+05 0.00163860318776472 +125 2 2024-03-02 15 1e+06 2024-02-05 90 1 4e+05 0.00163860318776472 +125 3 2024-03-02 15 1e+06 2024-02-05 90 2 2e+05 0.00163860318776472 +125 4 2024-03-02 15 1e+06 2024-02-05 90 3 1e+05 0.00163860318776472 +125 5 2024-03-02 15 1e+06 2024-02-05 90 4 50000 0.00163860318776472 +126 1 2024-03-03 10 1e+06 2024-02-05 90 1 4e+05 0.00171309115137499 +126 2 2024-03-03 10 1e+06 2024-02-05 90 1 4e+05 0.00171309115137499 +126 3 2024-03-03 10 1e+06 2024-02-05 90 2 2e+05 0.00171309115137499 +126 4 2024-03-03 10 1e+06 2024-02-05 90 3 1e+05 0.00171309115137499 +126 5 2024-03-03 10 1e+06 2024-02-05 90 4 50000 0.00171309115137499 +127 1 2024-03-04 10 1e+06 2024-02-05 90 1 4e+05 0.00163994741751005 +127 2 2024-03-04 10 1e+06 2024-02-05 90 1 4e+05 0.00163994741751005 +127 3 2024-03-04 10 1e+06 2024-02-05 90 2 2e+05 0.00163994741751005 +127 4 2024-03-04 10 1e+06 2024-02-05 90 3 1e+05 0.00163994741751005 +127 5 2024-03-04 10 1e+06 2024-02-05 90 4 50000 0.00163994741751005 From 729ae574ac67522d5a35af5915d8f88c1ad9ee99 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 4 Apr 2024 15:50:27 -0600 Subject: [PATCH 04/24] Adding interval datasets and starting example with ww data --- model/docs/example-with-datasets.md | 210 ++++++++++++++++++ model/docs/example-with-datasets.qmd | 167 ++++++++++++++ .../figure-commonmark/basic-fig-output-1.png | Bin 0 -> 33670 bytes .../plot-hospitalizations-output-1.png | Bin 0 -> 41781 bytes .../plot-hospitalizations-output-2.png | Bin 0 -> 72688 bytes model/src/pyrenew/datasets/__init__.py | 10 +- .../pyrenew/datasets/generation_interval.R | 13 ++ .../pyrenew/datasets/generation_interval.py | 31 +++ .../pyrenew/datasets/generation_interval.tsv | 16 ++ .../datasets/infection_admission_interval.R | 13 ++ .../datasets/infection_admission_interval.py | 32 +++ .../datasets/infection_admission_interval.tsv | 56 +++++ model/src/test/test_datasets.py | 26 ++- 13 files changed, 572 insertions(+), 2 deletions(-) create mode 100644 model/docs/example-with-datasets.md create mode 100644 model/docs/example-with-datasets.qmd create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/plot-hospitalizations-output-1.png create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/plot-hospitalizations-output-2.png create mode 100644 model/src/pyrenew/datasets/generation_interval.R create mode 100644 model/src/pyrenew/datasets/generation_interval.py create mode 100644 model/src/pyrenew/datasets/generation_interval.tsv create mode 100644 model/src/pyrenew/datasets/infection_admission_interval.R create mode 100644 model/src/pyrenew/datasets/infection_admission_interval.py create mode 100644 model/src/pyrenew/datasets/infection_admission_interval.tsv diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md new file mode 100644 index 00000000..adc8b1ad --- /dev/null +++ b/model/docs/example-with-datasets.md @@ -0,0 +1,210 @@ +# Fitting an hospitalizations only model + + +This document illustrates how a hospitalization-only model can be fitted +using data from the Pyrenew package, particularly the wastewater +dataset. The CFA wastewater team created this dataset, which contains +simulated data. + +``` python +import polars as pl +from pyrenew.datasets import load_wastewater +from pyrenew.model import HospitalizationsModel +``` + +## Data processing + +We start by loading the data and inspecting the first five rows. + +``` python +dat = load_wastewater() +dat.head(5) +``` + +
+shape: (5, 14) + +| t | lab_wwtp_unique_id | log_conc | date | lod_sewage | below_LOD | daily_hosp_admits | daily_hosp_admits_for_eval | pop | forecast_date | hosp_calibration_time | site | ww_pop | inf_per_capita | +|-----|--------------------|----------|--------------|------------|-----------|-------------------|----------------------------|-----|---------------|-----------------------|------|----------|----------------| +| i64 | i64 | f64 | str | f64 | i64 | i64 | i64 | f64 | str | i64 | i64 | f64 | f64 | +| 1 | 1 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 1 | 400000.0 | 0.000663 | +| 1 | 2 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 1 | 400000.0 | 0.000663 | +| 1 | 3 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 2 | 200000.0 | 0.000663 | +| 1 | 4 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 3 | 100000.0 | 0.000663 | +| 1 | 5 | null | "2023-10-30" | null | null | 6 | 6 | 1e6 | "2024-02-05" | 90 | 4 | 50000.0 | 0.000663 | + +
+ +The data shows one entry per site. We will take the aggregated data for +all sites by date. + +``` python +dat = dat.group_by("date").agg( + hospitalizations = pl.sum("daily_hosp_admits"), + population = pl.sum("pop"), +) + +# Now, sorting by date +dat = dat.sort("date") + +dat.head(5) +``` + +
+shape: (5, 3) + +| date | hospitalizations | population | +|--------------|------------------|------------| +| str | i64 | f64 | +| "2023-10-30" | 30 | 5e6 | +| "2023-10-31" | 40 | 5e6 | +| "2023-11-01" | 20 | 5e6 | +| "2023-11-02" | 40 | 5e6 | +| "2023-11-03" | 20 | 5e6 | + +
+ +Let’s take a look at the daily prevalence of hospitalizations. + +``` python +import matplotlib.pyplot as plt + +# Rotating the x-axis labels, and only showing ~10 labels +ax = plt.gca() +ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) +ax.xaxis.set_tick_params(rotation=45) +plt.plot(dat["date"].to_numpy(), dat["hospitalizations"].to_numpy()) +plt.xlabel("Date") +plt.ylabel("Hospitalizations") +plt.show() +``` + + + +## Building the model + +First, we will extract two datasets that we will use as deterministic +quantities, the generation interval and the infection to hospitalization +interval. + +``` python +from pyrenew.datasets import ( + load_generation_interval, + load_infection_admission_interval, +) + +gen_int = load_generation_interval() +inf_hosp_int = load_infection_admission_interval() + +# We only need the probability_mass column of each dataset +gen_int = gen_int["probability_mass"].to_numpy() +inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() + +# Taking a pick at the first 5 elements of each +gen_int[:5], inf_hosp_int[:5] +``` + + (array([0.16174201, 0.32062589, 0.24228335, 0.13465256, 0.06892184]), + array([0. , 0.00469385, 0.01452001, 0.02786277, 0.04236565])) + +With these two in hand, we can start building the model. First, we will +define the latent hospitalizations. + +``` python +from pyrenew.latent import HospitalAdmissions, InfectHospRate +from pyrenew.deterministic import DeterministicPMF +import jax.numpy as jnp +import numpyro.distributions as dist + +inf_hosp_int = DeterministicPMF((inf_hosp_int,)) + +hosp_rate = InfectHospRate( + dist=dist.LogNormal(jnp.log(0.05), 0.05) +) + +latent_hosp = HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist=hosp_rate, + ) +``` + +Now we can define the rest of the other model components: + +``` python +from pyrenew.model import HospitalizationsModel +from pyrenew.latent import Infections, Infections0 +from pyrenew.process import RtRandomWalkProcess +from pyrenew.observation import NegativeBinomialObservation + +# Infection process +latent_inf = Infections() +I0 = Infections0() + +# Generation interval and Rt +gen_int = DeterministicPMF((gen_int,)) +process = RtRandomWalkProcess() + +# The observation model +obs = NegativeBinomialObservation(concentration_prior=0.1) +``` + +Notice all the components are `RandomVariable` instances. We can now +build the model: + +``` python +model = HospitalizationsModel( + latent_infections=latent_inf, + latent_hospitalizations=latent_hosp, + I0=I0, + gen_int=gen_int, + Rt_process=process, + observed_hospitalizations=obs, +) +``` + +Let’s run a simulation to check if the model is working: + +``` python +import numpyro as npro +import numpy as np + +np.random.seed(223) +with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): + sim_data = model.sample(n_timepoints=60) +``` + +``` python +import matplotlib.pyplot as plt + +fig, axs = plt.subplots(1, 2) + +# Rt plot +axs[0].plot(range(0, 61), sim_data[0]) +axs[0].set_ylabel('Rt') + +# Infections plot +axs[1].plot(range(0, 61), sim_data[1]) +axs[1].set_ylabel('Infections') + +fig.suptitle('Basic renewal model') +fig.supxlabel('Time') +plt.tight_layout() +plt.show() +``` + +![Rt and +Infections](example-with-datasets_files/figure-commonmark/basic-fig-output-1.png) diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd new file mode 100644 index 00000000..12c0aa46 --- /dev/null +++ b/model/docs/example-with-datasets.qmd @@ -0,0 +1,167 @@ +--- +title: Fitting an hospitalizations only model +format: gfm +engine: jupyter +--- + +This document illustrates how a hospitalization-only model can be fitted using data from the Pyrenew package, particularly the wastewater dataset. The CFA wastewater team created this dataset, which contains simulated data. + +```{python} +#| label: setup +import polars as pl +from pyrenew.datasets import load_wastewater +from pyrenew.model import HospitalizationsModel +``` + +## Data processing + +We start by loading the data and inspecting the first five rows. + +```{python} +#| label: data-inspect +dat = load_wastewater() +dat.head(5) +``` + +The data shows one entry per site. We will take the aggregated data for all sites by date. + +```{python} +#| label: aggregation +dat = dat.group_by("date").agg( + hospitalizations = pl.sum("daily_hosp_admits"), + population = pl.sum("pop"), +) + +# Now, sorting by date +dat = dat.sort("date") + +dat.head(5) +``` + +Let's take a look at the daily prevalence of hospitalizations. + +```{python} +#| label: plot-hospitalizations +import matplotlib.pyplot as plt + +# Rotating the x-axis labels, and only showing ~10 labels +ax = plt.gca() +ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) +ax.xaxis.set_tick_params(rotation=45) +plt.plot(dat["date"].to_numpy(), dat["hospitalizations"].to_numpy()) +plt.xlabel("Date") +plt.ylabel("Hospitalizations") +plt.show() +``` + +## Building the model + +First, we will extract two datasets that we will use as deterministic quantities, the generation interval and the infection to hospitalization interval. + +```{python} +#| label: data-extract +from pyrenew.datasets import ( + load_generation_interval, + load_infection_admission_interval, +) + +gen_int = load_generation_interval() +inf_hosp_int = load_infection_admission_interval() + +# We only need the probability_mass column of each dataset +gen_int = gen_int["probability_mass"].to_numpy() +inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() + +# Taking a pick at the first 5 elements of each +gen_int[:5], inf_hosp_int[:5] +``` + +With these two in hand, we can start building the model. First, we will define the latent hospitalizations. + +```{python} +#| label: latent-hosp +from pyrenew.latent import HospitalAdmissions, InfectHospRate +from pyrenew.deterministic import DeterministicPMF +import jax.numpy as jnp +import numpyro.distributions as dist + +inf_hosp_int = DeterministicPMF((inf_hosp_int,)) + +hosp_rate = InfectHospRate( + dist=dist.LogNormal(jnp.log(0.05), 0.05) +) + +latent_hosp = HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist=hosp_rate, + ) +``` + +Now we can define the rest of the other model components: + +```{python} +#| label: initializing-rest-of-model +from pyrenew.model import HospitalizationsModel +from pyrenew.latent import Infections, Infections0 +from pyrenew.process import RtRandomWalkProcess +from pyrenew.observation import NegativeBinomialObservation + +# Infection process +latent_inf = Infections() +I0 = Infections0() + +# Generation interval and Rt +gen_int = DeterministicPMF((gen_int,)) +process = RtRandomWalkProcess() + +# The observation model +obs = NegativeBinomialObservation(concentration_prior=0.1) +``` + +Notice all the components are `RandomVariable` instances. We can now build the model: + +```{python} +#| label: init-model +model = HospitalizationsModel( + latent_infections=latent_inf, + latent_hospitalizations=latent_hosp, + I0=I0, + gen_int=gen_int, + Rt_process=process, + observed_hospitalizations=obs, +) +``` + +Let's run a simulation to check if the model is working: + +```{python} +#| label: simulation +import numpyro as npro +import numpy as np + +np.random.seed(223) +with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): + sim_data = model.sample(n_timepoints=60) +``` + +```{python} +#| label: basic-fig +#| fig-cap: Rt and Infections +#| fig-cap-location: bottom +import matplotlib.pyplot as plt + +fig, axs = plt.subplots(1, 2) + +# Rt plot +axs[0].plot(range(0, 61), sim_data[0]) +axs[0].set_ylabel('Rt') + +# Infections plot +axs[1].plot(range(0, 61), sim_data[1]) +axs[1].set_ylabel('Infections') + +fig.suptitle('Basic renewal model') +fig.supxlabel('Time') +plt.tight_layout() +plt.show() +``` diff --git 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z%nUF&6zjBO95*v73yarQ5P*nmbpI8KTWn;6|GtW!7??t+krYaCi@I7SMlk@UDFuQ! zl_&oUUh9{*$gcoIMB`d#+8p^%3eJbW|V zOuD=2+yijd4Cufz@I^q0Nq?wS5riq9{yH{ADbg=qtn(_tSEM@YaWRj!e(|4YB{nqF z@3ft-(pVOkCb9UyBS(m8lw{kQDz-1RYz?>!1%!{Yre@qf%vmnYs>Ks^V8dArkOn_d zSsZ78TgiJ2xa$U_(U57y{_G*rwXR!8>lHiR)E*v2@h0K`?davX>~ttw$WN27mI&I7a_gM|`DEbdrI2 z5Flg#Q*>R literal 0 HcmV?d00001 diff --git a/model/src/pyrenew/datasets/__init__.py b/model/src/pyrenew/datasets/__init__.py index ac4eee49..bf31218e 100644 --- a/model/src/pyrenew/datasets/__init__.py +++ b/model/src/pyrenew/datasets/__init__.py @@ -1,3 +1,11 @@ +from pyrenew.datasets.generation_interval import load_generation_interval +from pyrenew.datasets.infection_admission_interval import ( + load_infection_admission_interval, +) from pyrenew.datasets.wastewater import load_wastewater -__all__ = ["load_wastewater"] +__all__ = [ + "load_wastewater", + "load_infection_admission_interval", + "load_generation_interval", +] diff --git a/model/src/pyrenew/datasets/generation_interval.R b/model/src/pyrenew/datasets/generation_interval.R new file mode 100644 index 00000000..e25d91ba --- /dev/null +++ b/model/src/pyrenew/datasets/generation_interval.R @@ -0,0 +1,13 @@ +# Dataset from: +# https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv +gen_int <- read.csv("https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv") + +# Saving as TSV +write.table( + x = gen_int, + file = "src/pyrenew/datasets/gen_int.tsv", + sep = "\t", + quote = FALSE, + row.names = FALSE, + na = "" + ) diff --git a/model/src/pyrenew/datasets/generation_interval.py b/model/src/pyrenew/datasets/generation_interval.py new file mode 100644 index 00000000..11c8f9ea --- /dev/null +++ b/model/src/pyrenew/datasets/generation_interval.py @@ -0,0 +1,31 @@ +from importlib.resources import files + +import polars as pl + + +def load_generation_interval() -> pl.DataFrame: + """Load the generation interval dataset + + This dataset contains the generation interval distribution for COVID-19. + + Returns + ------- + pl.DataFrame + The generation interval dataset + + Notes + ----- + + This dataset was downloaded directly from: + https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv + + The dataset contains the following columns: + - `timepoint` + - `probability_mass` + """ + + # Load the dataset + return pl.read_csv( + source=files("pyrenew.datasets") / "generation_interval.tsv", + separator="\t", + ) diff --git a/model/src/pyrenew/datasets/generation_interval.tsv b/model/src/pyrenew/datasets/generation_interval.tsv new file mode 100644 index 00000000..f74292b5 --- /dev/null +++ b/model/src/pyrenew/datasets/generation_interval.tsv @@ -0,0 +1,16 @@ +timepoint probability_mass +1 0.16174201341157 +2 0.320625889390807 +3 0.242283351248733 +4 0.134652559431048 +5 0.0689218448080527 +6 0.0345873307236478 +7 0.0175305676468834 +8 0.00900188393721926 +9 0.0048814763363626 +10 0.00258678762113042 +11 0.00144458017530763 +12 0.000825330494803486 +13 0.000478236415587845 +14 0.000270544021118098 +15 0.000167604337729198 diff --git a/model/src/pyrenew/datasets/infection_admission_interval.R b/model/src/pyrenew/datasets/infection_admission_interval.R new file mode 100644 index 00000000..74b55a34 --- /dev/null +++ b/model/src/pyrenew/datasets/infection_admission_interval.R @@ -0,0 +1,13 @@ +# Dataset from: +# https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv +infection_admission_interval <- read.csv("https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv") + +# Saving as TSV +write.table( + x = infection_admission_interval, + file = "src/pyrenew/datasets/infection_admission_interval.tsv", + sep = "\t", + quote = FALSE, + row.names = FALSE, + na = "" + ) diff --git a/model/src/pyrenew/datasets/infection_admission_interval.py b/model/src/pyrenew/datasets/infection_admission_interval.py new file mode 100644 index 00000000..e04e4c37 --- /dev/null +++ b/model/src/pyrenew/datasets/infection_admission_interval.py @@ -0,0 +1,32 @@ +from importlib.resources import files + +import polars as pl + + +def load_infection_admission_interval() -> pl.DataFrame: + """Load the infection to admission interval + + This dataset contains the infection to admission interval distribution for + COVID-19. + + Returns + ------- + pl.DataFrame + The infection to admission interval dataset + + Notes + ----- + + This dataset was downloaded directly from: + https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv + + The dataset contains the following columns: + - `timepoint` + - `probability_mass` + """ + + # Load the dataset + return pl.read_csv( + source=files("pyrenew.datasets") / "infection_admission_interval.tsv", + separator="\t", + ) diff --git a/model/src/pyrenew/datasets/infection_admission_interval.tsv b/model/src/pyrenew/datasets/infection_admission_interval.tsv new file mode 100644 index 00000000..e1e6c2bf --- /dev/null +++ b/model/src/pyrenew/datasets/infection_admission_interval.tsv @@ -0,0 +1,56 @@ +timepoint probability_mass +0 0 +1 0.00469384736487552 +2 0.0145200073436112 +3 0.0278627741704387 +4 0.0423656492135518 +5 0.0558071445014868 +6 0.0665713169684116 +7 0.0737925805176124 +8 0.0772854627892072 +9 0.0773666390616176 +10 0.0746515449009949 +11 0.0698761436052596 +12 0.0637663813017696 +13 0.0569581929821651 +14 0.0499600186601535 +15 0.0431457477049282 +16 0.0367662806214045 +17 0.0309702535668237 +18 0.0258273785539499 +19 0.0213504646948306 +20 0.0175141661880584 +21 0.0142698211023571 +22 0.0115565159519833 +23 0.00930888979824423 +24 0.00746229206759215 +25 0.00595605679409682 +26 0.00473519993107751 +27 0.00375117728281841 +28 0.00296198928038098 +29 0.00233187862772459 +30 0.00183079868293457 +31 0.00143377454057296 +32 0.00107076258525208 +33 0.000773006742366448 +34 0.000539573690886396 +35 0.000364177599116743 +36 0.000237727628685579 +37 0.000150157714457011 +38 9.18283319498657e-05 +39 5.44079947589854e-05 +40 3.12548818921465e-05 +41 1.74202619730274e-05 +42 9.42698047424713e-06 +43 4.95614149002087e-06 +44 2.53275674485913e-06 +45 1.25854819834554e-06 +46 6.08116579596933e-07 +47 2.85572858589747e-07 +48 1.30129404249734e-07 +49 5.73280599448306e-08 +50 2.4219376577964e-08 +51 9.6316861194457e-09 +52 3.43804936850951e-09 +53 9.34806280366888e-10 +54 0 diff --git a/model/src/test/test_datasets.py b/model/src/test/test_datasets.py index 05747566..a8a3875a 100644 --- a/model/src/test/test_datasets.py +++ b/model/src/test/test_datasets.py @@ -1,5 +1,9 @@ import numpy.testing as testing -from pyrenew.datasets import load_wastewater +from pyrenew.datasets import ( + load_generation_interval, + load_infection_admission_interval, + load_wastewater, +) def test_loading_wastewater(): @@ -10,3 +14,23 @@ def test_loading_wastewater(): testing.assert_approx_equal(df["daily_hosp_admits"].mean(), 12.8888, 3) testing.assert_approx_equal(df["lod_sewage"].mean(), 3.841025, 3) + + +def test_gen_int(): + """Test that the generation interval dataset can be properly loaded""" + df = load_generation_interval() + assert len(df) > 0 + assert df.shape == (15, 2) + + testing.assert_approx_equal(df["probability_mass"].mean(), 0.0666666, 3) + + +def test_infection_admission_interval(): + """Test that the infection to admission interval dataset can be properly loaded""" + df = load_infection_admission_interval() + assert len(df) > 0 + assert df.shape == (55, 2) + + testing.assert_approx_equal( + df["probability_mass"].mean(), 0.01818181818, 3 + ) From c865c377fa79bb78942126714514381bb0133e2b Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 5 Apr 2024 15:13:38 -0600 Subject: [PATCH 05/24] Model starts to fit but fails b/c of too big mean for negbinom [skip ci] (will fail) --- model/docs/example-with-datasets.qmd | 18 ++++++++++++++++++ .../pyrenew/observation/negativebinomial.py | 4 +++- 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 12c0aa46..5e5cbaaa 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -165,3 +165,21 @@ fig.supxlabel('Time') plt.tight_layout() plt.show() ``` + +## Fitting the model + +Now we can fit the model to the data. We will use the `run` method of the model object. The two inputs this model requires are `n_timepoints` and `observed_hospitalizations` + + +```{python} +import jax + +model.run( + num_samples=2000, + num_warmup=1000, + n_timepoints=dat.shape[0] - 1, + observed_hospitalizations=dat["hospitalizations"].to_numpy(), + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), +) +``` diff --git a/model/src/pyrenew/observation/negativebinomial.py b/model/src/pyrenew/observation/negativebinomial.py index 04e76fa8..331ae828 100644 --- a/model/src/pyrenew/observation/negativebinomial.py +++ b/model/src/pyrenew/observation/negativebinomial.py @@ -73,12 +73,14 @@ def sample( ------- tuple """ + concentration = self.sample_prior() + return ( numpyro.sample( self.parameter_name, dist.NegativeBinomial2( mean=predicted, - concentration=self.sample_prior(), + concentration=concentration, ), obs=obs, ), From c501a0d2c2c494a3cef075f736a6e419b643bb35 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Mon, 8 Apr 2024 16:02:46 -0600 Subject: [PATCH 06/24] Adding math to the example-with-datasets plus clarifications in code --- model/Makefile | 15 +++- model/docs/example-with-datasets.md | 85 ++++++++++++++++-- model/docs/example-with-datasets.qmd | 47 +++++++++- .../figure-commonmark/basic-fig-output-1.png | Bin 33670 -> 32559 bytes .../data-extract-output-2.png | Bin 0 -> 31824 bytes model/docs/pyrenew_demo.md | 2 + .../src/pyrenew/latent/infection_functions.py | 2 +- model/src/pyrenew/latent/infections.py | 24 ++++- model/src/pyrenew/process/rtrandomwalk.py | 13 ++- 9 files changed, 170 insertions(+), 18 deletions(-) create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/data-extract-output-2.png diff --git a/model/Makefile b/model/Makefile index a6560b69..31b2f95a 100644 --- a/model/Makefile +++ b/model/Makefile @@ -4,7 +4,8 @@ install: test: poetry run pytest -docs: docs/pyrenew_demo.md docs/getting-started.md +docs: docs/pyrenew_demo.md docs/getting-started.md \ + docs/example-with-datasets.md docs/pyrenew_demo.md: docs/pyrenew_demo.qmd poetry run quarto render docs/pyrenew_demo.qmd @@ -12,9 +13,19 @@ docs/pyrenew_demo.md: docs/pyrenew_demo.qmd docs/getting-started.md: docs/getting-started.qmd poetry run quarto render docs/getting-started.qmd +docs/example-with-datasets.md: docs/example-with-datasets.qmd + poetry run quarto render docs/example-with-datasets.qmd + +notebooks: + quarto convert docs/pyrenew_demo.qmd --output docs/pyrenew_demo.ipynb + quarto convert docs/getting-started.qmd --output docs/getting-started.ipynb + jupyter nbconvert --to python docs/pyrenew_demo.ipynb + jupyter nbconvert --to python docs/getting-started.ipynb + rm -f docs/pyrenew_demo.ipynb docs/getting-started.ipynb + clean: rm -rf docs/*_files/ rm -f docs/getting-started.ipynb rm -f docs/pyrenew_demo.ipynb -.PHONY: install test docs clean +.PHONY: install test docs clean notebooks diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index adc8b1ad..334db618 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -12,6 +12,47 @@ from pyrenew.datasets import load_wastewater from pyrenew.model import HospitalizationsModel ``` + /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html + from .autonotebook import tqdm as notebook_tqdm + An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. + +## Model definition + +In this section we provide the formal definition of the model. The +hospitalization model is a semi-mechanistic model that describes the +number of observed hospitalizations as a function of a set of latent +variables. Particularly, the observed number of hospitalizations is +discretily distributed with location at the number of latent +hospitalizations: + +$$ +h(t) \sim \text{HospDist}\left(H(t)\right) +$$ + +Where $h(t)$ is the observed number of hospitalizations at time $t$, and +$H(t)$ is the number of latent hospitalizations at time $t$. The +distribution $\text{HospDist}$ can be any discrete distribution, but in +this case we will use a negative binomial distribution. + +The number of latent hospitalizations at time $t$ is a function of the +number of latent infections at time $t$ and the infection to +hospitalization rate. The latent infections are modeled as a renewal +process: + +$$ +I(t) = R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) +$$ + +The reproductive number $R(t)$ is modeled as a random walk process: + +$$ +\begin{align*} +R(t) & = R(t-1) + \epsilon\\ +\log{\epsilon} & \sim \text{Normal}(0, \sigma) \\ +R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) +\end{align*} +$$ + ## Data processing We start by loading the data and inspecting the first five rows. @@ -116,10 +157,21 @@ inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() # Taking a pick at the first 5 elements of each gen_int[:5], inf_hosp_int[:5] + +# Visualizing both quantities side by side +fig, axs = plt.subplots(1, 2) + +axs[0].plot(gen_int) +axs[0].set_title("Generation interval") +axs[1].plot(inf_hosp_int) +axs[1].set_title("Infection to hospitalization interval") ``` - (array([0.16174201, 0.32062589, 0.24228335, 0.13465256, 0.06892184]), - array([0. , 0.00469385, 0.01452001, 0.02786277, 0.04236565])) + Text(0.5, 1.0, 'Infection to hospitalization interval') + + With these two in hand, we can start building the model. First, we will define the latent hospitalizations. @@ -182,9 +234,11 @@ Let’s run a simulation to check if the model is working: import numpyro as npro import numpy as np +timeframe = 120 + np.random.seed(223) -with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): - sim_data = model.sample(n_timepoints=60) +with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): + sim_data = model.sample(n_timepoints=timeframe) ``` ``` python @@ -193,11 +247,11 @@ import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 2) # Rt plot -axs[0].plot(range(0, 61), sim_data[0]) +axs[0].plot(range(0, timeframe + 1), sim_data[0]) axs[0].set_ylabel('Rt') # Infections plot -axs[1].plot(range(0, 61), sim_data[1]) +axs[1].plot(range(0, timeframe + 1), sim_data[1]) axs[1].set_ylabel('Infections') fig.suptitle('Basic renewal model') @@ -208,3 +262,22 @@ plt.show() ![Rt and Infections](example-with-datasets_files/figure-commonmark/basic-fig-output-1.png) + +## Fitting the model + +Now we can fit the model to the data. We will use the `run` method of +the model object. The two inputs this model requires are `n_timepoints` +and `observed_hospitalizations` + +``` python +import jax + +model.run( + num_samples=2000, + num_warmup=1000, + n_timepoints=dat.shape[0] - 1, + observed_hospitalizations=dat["hospitalizations"].to_numpy(), + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), +) +``` diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 5e5cbaaa..caf50efd 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -13,6 +13,34 @@ from pyrenew.datasets import load_wastewater from pyrenew.model import HospitalizationsModel ``` +## Model definition + +In this section we provide the formal definition of the model. The hospitalization model is a semi-mechanistic model that describes the number of observed hospitalizations as a function of a set of latent variables. Particularly, the observed number of hospitalizations is discretily distributed with location at the number of latent hospitalizations: + +$$ +h(t) \sim \text{HospDist}\left(H(t)\right) +$$ + +Where $h(t)$ is the observed number of hospitalizations at time $t$, and $H(t)$ is the number of latent hospitalizations at time $t$. The distribution $\text{HospDist}$ can be any discrete distribution, but in this case we will use a negative binomial distribution. + +The number of latent hospitalizations at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: + +$$ +I(t) = R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) +$$ + +The reproductive number $R(t)$ is modeled as a random walk process: + +$$ +\begin{align*} +R(t) & = R(t-1) + \epsilon\\ +\log{\epsilon} & \sim \text{Normal}(0, \sigma) \\ +R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) +\end{align*} +$$ + + + ## Data processing We start by loading the data and inspecting the first five rows. @@ -74,6 +102,14 @@ inf_hosp_int = inf_hosp_int["probability_mass"].to_numpy() # Taking a pick at the first 5 elements of each gen_int[:5], inf_hosp_int[:5] + +# Visualizing both quantities side by side +fig, axs = plt.subplots(1, 2) + +axs[0].plot(gen_int) +axs[0].set_title("Generation interval") +axs[1].plot(inf_hosp_int) +axs[1].set_title("Infection to hospitalization interval") ``` With these two in hand, we can start building the model. First, we will define the latent hospitalizations. @@ -139,9 +175,11 @@ Let's run a simulation to check if the model is working: import numpyro as npro import numpy as np +timeframe = 120 + np.random.seed(223) -with npro.handlers.seed(rng_seed = np.random.randint(1, 60)): - sim_data = model.sample(n_timepoints=60) +with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): + sim_data = model.sample(n_timepoints=timeframe) ``` ```{python} @@ -153,11 +191,11 @@ import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 2) # Rt plot -axs[0].plot(range(0, 61), sim_data[0]) +axs[0].plot(range(0, timeframe + 1), sim_data[0]) axs[0].set_ylabel('Rt') # Infections plot -axs[1].plot(range(0, 61), sim_data[1]) +axs[1].plot(range(0, timeframe + 1), sim_data[1]) axs[1].set_ylabel('Infections') fig.suptitle('Basic renewal model') @@ -172,6 +210,7 @@ Now we can fit the model to the data. We will use the `run` method of the model ```{python} +#| eval: false import jax model.run( diff --git a/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png b/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png index f5a3e21dd09c90fcaba06db7d02661f1ab0e0725..db6ef442f05c6e7c99ffdd4f1812b176d278e39d 100644 GIT binary patch literal 32559 zcmcG$1yq$?6gGJ2ZUjZTLFtf=3yL5qNP~2DcgRIhN=mvVBt;r&q@}wA1f;t|nDc)B zKQsSYvu4(sHEUdAa4+wB-gEYT_TJBa_Whu$EQg0pfenMf@Z?|0zJ|e&b6_x}HB5By z9sH$pO7NGk+jDI*~Ph%Hzn3A!ZlbxfRowW(Ahq;TZwW9+crw}LKQ(7xGHz!vS zE-w53;|-jSE|y$9f3?-YMX;P+>bQbS{)E1e5})8dhQR{TbG+DC zd8ov3#0qdY8P=?5G?mKi)aT44nla%xJMUK_a>p0028|}KB{<>2KR~anJVX!^5fQ0%@L)30)6?f7ObHdBH(>S9CO|(* z94*ZXUMFIr3{eHI(<=yp3$Qti=05+#DBrMo=97?^XgpP_GgE0Qr=R6|fA`PX`&L81 z_uNiHL!(GMlJkv*hSB-%Y;()f?bQjL{Oer|Sy}mk>~mN+E6?3gOrX)>a{J+x-%OPy z!G7y~fMS{;-jd%5-uJ31(`$Wl12>|U%Re{?2?;+>oj-)Lx^rVPQP46gCI`hb$RWdq 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literal 0 HcmV?d00001 diff --git a/model/docs/pyrenew_demo.md b/model/docs/pyrenew_demo.md index 410e192e..5fc9d402 100644 --- a/model/docs/pyrenew_demo.md +++ b/model/docs/pyrenew_demo.md @@ -21,6 +21,8 @@ import numpyro.distributions as dist from pyrenew.process import SimpleRandomWalkProcess ``` + An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. + ``` python np.random.seed(3312) q = SimpleRandomWalkProcess(dist.Normal(0, 0.001)) diff --git a/model/src/pyrenew/latent/infection_functions.py b/model/src/pyrenew/latent/infection_functions.py index 542311b4..b4e8b76d 100755 --- a/model/src/pyrenew/latent/infection_functions.py +++ b/model/src/pyrenew/latent/infection_functions.py @@ -45,7 +45,7 @@ def sample_infections_rt( """ incidence_func = new_convolve_scanner(reversed_generation_interval_pmf) - latest, all_infections = jax.lax.scan(incidence_func, I0, Rt) + latest, all_infections = jax.lax.scan(f=incidence_func, init=I0, xs=Rt) return all_infections diff --git a/model/src/pyrenew/latent/infections.py b/model/src/pyrenew/latent/infections.py index b6d9fed1..e079c27d 100644 --- a/model/src/pyrenew/latent/infections.py +++ b/model/src/pyrenew/latent/infections.py @@ -16,7 +16,24 @@ class Infections(RandomVariable): - """Latent infections""" + r"""Latent infections + + This class samples infections given Rt, initial infections, and generation + interval. + + Notes + ----- + The mathematical model is given by: + + .. math:: + + I(t) = R(t) \times \sum_{\tau < t} I(\tau) g(t-\tau) + + where :math:`I(t)` is the number of infections at time :math:`t`, + :math:`R(t)` is the reproduction number at time :math:`t`, and + :math:`g(t-\tau)` is the generation interval. + + """ def __init__( self, @@ -49,9 +66,8 @@ def sample( gen_int: ArrayLike, **kwargs, ) -> tuple: - """Samples infections given Rt - - A random variable representing the pmf of the generation interval. + """Samples infections given Rt, initial infections, and generation + interval. Parameters ---------- diff --git a/model/src/pyrenew/process/rtrandomwalk.py b/model/src/pyrenew/process/rtrandomwalk.py index bff8a460..f92f58e4 100644 --- a/model/src/pyrenew/process/rtrandomwalk.py +++ b/model/src/pyrenew/process/rtrandomwalk.py @@ -8,7 +8,18 @@ class RtRandomWalkProcess(RandomVariable): - """Rt Randomwalk Process""" + r"""Rt Randomwalk Process + + Notes + ----- + + The process is defined as follows: + + .. math:: + + Rt(0) &\sim \text{Rt0_dist} \\ + Rt(t) &\sim \text{Rt_transform}(\text{Rt_transformed_rw}(t)) + """ def __init__( self, From aeda67390cb75fa17142cc105e044395a33b4756 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 9 Apr 2024 15:30:53 -0600 Subject: [PATCH 07/24] Model fit runs OK (yupi) --- model/Makefile | 15 ++++--- model/docs/example-with-datasets.md | 35 ++++++++++++++-- model/docs/example-with-datasets.qmd | 38 ++++++++++++++++-- .../output-hospitalizations-output-1.png | Bin 0 -> 64304 bytes .../plot-hospitalizations-output-1.png | Bin 41781 -> 44459 bytes .../plot-hospitalizations-output-2.png | Bin 72688 -> 0 bytes model/docs/getting-started.md | 4 +- model/docs/getting-started.qmd | 4 +- .../figure-commonmark/fig-hosp-output-1.png | Bin 14325 -> 0 bytes .../fig-randwalk-output-1.png | Bin 29905 -> 0 bytes .../fig-sampled-rt-output-1.png | Bin 38249 -> 0 bytes model/src/pyrenew/deterministic/__init__.py | 3 +- .../pyrenew/deterministic/deterministic.py | 15 +++---- .../pyrenew/deterministic/deterministicpmf.py | 20 +++++---- model/src/pyrenew/deterministic/process.py | 37 +++++++++++++++++ .../src/pyrenew/latent/hospitaladmissions.py | 4 +- model/src/pyrenew/model/hospitalizations.py | 2 +- .../pyrenew/observation/negativebinomial.py | 7 +++- model/src/test/test_model_basic_renewal.py | 2 +- model/src/test/test_model_hospitalizations.py | 8 ++-- 20 files changed, 147 insertions(+), 47 deletions(-) create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png delete mode 100644 model/docs/example-with-datasets_files/figure-commonmark/plot-hospitalizations-output-2.png delete mode 100644 model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png delete mode 100644 model/docs/pyrenew_demo_files/figure-commonmark/fig-randwalk-output-1.png delete mode 100644 model/docs/pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png create mode 100644 model/src/pyrenew/deterministic/process.py diff --git a/model/Makefile b/model/Makefile index 31b2f95a..243726ec 100644 --- a/model/Makefile +++ b/model/Makefile @@ -16,16 +16,21 @@ docs/getting-started.md: docs/getting-started.qmd docs/example-with-datasets.md: docs/example-with-datasets.qmd poetry run quarto render docs/example-with-datasets.qmd -notebooks: - quarto convert docs/pyrenew_demo.qmd --output docs/pyrenew_demo.ipynb - quarto convert docs/getting-started.qmd --output docs/getting-started.ipynb +docs/py: docs/notebooks jupyter nbconvert --to python docs/pyrenew_demo.ipynb jupyter nbconvert --to python docs/getting-started.ipynb - rm -f docs/pyrenew_demo.ipynb docs/getting-started.ipynb + jupyter nbconvert --to python docs/example-with-datasets.ipynb + +docs/notebooks: + quarto convert docs/pyrenew_demo.qmd --output docs/pyrenew_demo.ipynb + quarto convert docs/getting-started.qmd --output docs/getting-started.ipynb + quarto convert docs/example-with-datasets.qmd --output \ + docs/example-with-datasets.ipynb clean: rm -rf docs/*_files/ rm -f docs/getting-started.ipynb rm -f docs/pyrenew_demo.ipynb + rm -f docs/example-with-datasets.ipynb -.PHONY: install test docs clean notebooks +.PHONY: install test docs clean docs/notebooks docs/py diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index 334db618..ccf10a29 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -94,6 +94,9 @@ dat = dat.group_by("date").agg( # Now, sorting by date dat = dat.sort("date") +# Keeping the first 90 days +dat = dat.head(90) + dat.head(5) ``` @@ -182,7 +185,7 @@ from pyrenew.deterministic import DeterministicPMF import jax.numpy as jnp import numpyro.distributions as dist -inf_hosp_int = DeterministicPMF((inf_hosp_int,)) +inf_hosp_int = DeterministicPMF(inf_hosp_int) hosp_rate = InfectHospRate( dist=dist.LogNormal(jnp.log(0.05), 0.05) @@ -207,11 +210,11 @@ latent_inf = Infections() I0 = Infections0() # Generation interval and Rt -gen_int = DeterministicPMF((gen_int,)) +gen_int = DeterministicPMF(gen_int) process = RtRandomWalkProcess() # The observation model -obs = NegativeBinomialObservation(concentration_prior=0.1) +obs = NegativeBinomialObservation(concentration_prior=1.0) ``` Notice all the components are `RandomVariable` instances. We can now @@ -281,3 +284,29 @@ model.run( mcmc_args=dict(progress_bar=False), ) ``` + +``` python +import polars as pl +samps = model.spread_draws([('predicted_hospitalizations', 'time')]) + +fig, ax = plt.subplots(figsize=[4, 5]) + +ax.plot(dat["hospitalizations"].to_numpy(), color="black") +samp_ids = np.random.randint(size=25, low=0, high=999) +for samp_id in samp_ids: + sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) + ax.plot(sub_samps.select("time").to_numpy(), + sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) + +# Some labels +ax.set_xlabel("Time") +ax.set_ylabel("Hospitalizations") + +# Adding a legend +ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") +ax.plot([], [], color="black", label="Observed hospitalizations") +ax.legend() +``` + +![Hospitalizations posterior +distribution](example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png) diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index caf50efd..946d9445 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -63,6 +63,9 @@ dat = dat.group_by("date").agg( # Now, sorting by date dat = dat.sort("date") +# Keeping the first 90 days +dat = dat.head(90) + dat.head(5) ``` @@ -121,7 +124,7 @@ from pyrenew.deterministic import DeterministicPMF import jax.numpy as jnp import numpyro.distributions as dist -inf_hosp_int = DeterministicPMF((inf_hosp_int,)) +inf_hosp_int = DeterministicPMF(inf_hosp_int) hosp_rate = InfectHospRate( dist=dist.LogNormal(jnp.log(0.05), 0.05) @@ -147,11 +150,11 @@ latent_inf = Infections() I0 = Infections0() # Generation interval and Rt -gen_int = DeterministicPMF((gen_int,)) +gen_int = DeterministicPMF(gen_int) process = RtRandomWalkProcess() # The observation model -obs = NegativeBinomialObservation(concentration_prior=0.1) +obs = NegativeBinomialObservation(concentration_prior=1.0) ``` Notice all the components are `RandomVariable` instances. We can now build the model: @@ -210,7 +213,7 @@ Now we can fit the model to the data. We will use the `run` method of the model ```{python} -#| eval: false +#| label: model-fit import jax model.run( @@ -222,3 +225,30 @@ model.run( mcmc_args=dict(progress_bar=False), ) ``` + + +```{python} +#| label: output-hospitalizations +#| fig-cap: Hospitalizations posterior distribution +#| fig-cap-location: bottom +import polars as pl +samps = model.spread_draws([('predicted_hospitalizations', 'time')]) + +fig, ax = plt.subplots(figsize=[4, 5]) + +ax.plot(dat["hospitalizations"].to_numpy(), color="black") +samp_ids = np.random.randint(size=25, low=0, high=999) +for samp_id in samp_ids: + sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) + ax.plot(sub_samps.select("time").to_numpy(), + sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) + +# Some labels +ax.set_xlabel("Time") +ax.set_ylabel("Hospitalizations") + +# Adding a legend +ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") +ax.plot([], [], color="black", label="Observed hospitalizations") +ax.legend() +``` diff --git a/model/docs/example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png b/model/docs/example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png new file mode 100644 index 0000000000000000000000000000000000000000..69bc2e0713856b8cfb1d705e743e746598d8b500 GIT binary patch literal 64304 zcmaI8by$>N)HRGqcMRPyC^2*+-Q8W1(j5ZQEzKZEOQ*EbqI9^uNJzNKN^&|#NRPb0Pb~%-cm`YT;T-%W z;v;Y5qwD^{=cSdG9g@10kB5u9kBg% z@{--oI8&OQ00YI8G@zMF3h^)d7?v&MaH)m?!e}$_{}$omRBROF9#Tb!SGW}Y;zpbd zC*u9o+QX>G;g$&{Zhui-NF8&E=bd`^)s^5MmfkXf*O_dM(N-_DtMur{o*uWix-z(b zG^w0-$_=If*Y?oK%*^B#6!e=Na#@yf%XxzIkHI+eRlO_HIu_m>H&hsOSEzx;N zy09@K#Es;&=8f(dQu|^3KkkJIs~rr%#Z!)f3 z;LwogW|GiGnj$Slz<5cDMEqg>XWHaV%iYO*|I5zRq;c4NTCsmrkSakO%vDe-|f-hIDE-ECG+=CBID;Eyies>%qK3TkQ=sMU61rv}v zSPnU}7e6$vG^jzUY&L+PVq;@F@KYj%aRu+ID#1}+uK1F{#(FahEnc^NzB{_wQ|iq^X&}X9bw4X 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b/model/docs/getting-started.qmd @@ -53,9 +53,7 @@ The basic renewal model defines five components: generation interval, initial in ```{python} #| label: creating-elements # (1) The generation interval (deterministic) -gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), -) +gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) # (2) Initial infections (inferred with a prior) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) diff --git a/model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png b/model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png deleted file mode 100644 index 73cb803fc8ea1b9b1f3d5ea5312ebcc9cbf3ecb1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 14325 zcmd6OXINBQwrvSU;z5Fdf)Yd|OO%Y{C`e8PD3X)p98`i51w$PX_m0;hHdmtzG zoSvSZDz1BCeEhMOC>USv@Fa-(%9Xf=1%5sK8nTEEH?fSLM<-Qt80%zUa(;Q%%ke{N zI+~hs*T}kGbX%P$kAytcvGzh{Xn|#h-<`tJvl)XMUqHX!c=?yFc0{Iu!td`t;7<`m 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zli#W=sX1Dg*2hk%@h|I^7GWz+Cp;TToOJs=PrZra!ej5EPPUuW)Pb9z3n8>knn+c|Cgt3Lb|K9N%BD_gwma0E0^8TmS$7 diff --git a/model/src/pyrenew/deterministic/__init__.py b/model/src/pyrenew/deterministic/__init__.py index 87a504c0..66bbf5ad 100644 --- a/model/src/pyrenew/deterministic/__init__.py +++ b/model/src/pyrenew/deterministic/__init__.py @@ -2,5 +2,6 @@ from pyrenew.deterministic.deterministic import DeterministicVariable from pyrenew.deterministic.deterministicpmf import DeterministicPMF +from pyrenew.deterministic.process import DeterministicProcess -__all__ = ["DeterministicVariable", "DeterministicPMF"] +__all__ = ["DeterministicVariable", "DeterministicPMF", "DeterministicProcess"] diff --git a/model/src/pyrenew/deterministic/deterministic.py b/model/src/pyrenew/deterministic/deterministic.py index e6bdbd88..84cc6ff7 100644 --- a/model/src/pyrenew/deterministic/deterministic.py +++ b/model/src/pyrenew/deterministic/deterministic.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- +from jax.typing import ArrayLike from pyrenew.metaclass import RandomVariable @@ -9,15 +10,15 @@ class DeterministicVariable(RandomVariable): def __init__( self, - vars: tuple, + vars: ArrayLike, label: str = "a_random_variable", ) -> None: """Default constructor Parameters ---------- - vars : tuple - A tuple with arraylike objects. + vars : ArrayLike + An array with the fixed quantity. label : str A label to assign to the process. @@ -33,9 +34,9 @@ def __init__( return None @staticmethod - def validate(vars: tuple) -> None: - if not isinstance(vars, tuple): - raise Exception("vars is not a tuple") + def validate(vars: ArrayLike) -> None: + if not isinstance(vars, ArrayLike): + raise Exception("vars is not an array-like object.") return None @@ -56,4 +57,4 @@ def sample( Containing the stored values during construction. """ - return self.vars + return (self.vars,) diff --git a/model/src/pyrenew/deterministic/deterministicpmf.py b/model/src/pyrenew/deterministic/deterministicpmf.py index 585c6695..00d1b4d5 100644 --- a/model/src/pyrenew/deterministic/deterministicpmf.py +++ b/model/src/pyrenew/deterministic/deterministicpmf.py @@ -1,5 +1,6 @@ # -*- coding: utf-8 -*- +from jax.typing import ArrayLike from pyrenew.deterministic.deterministic import DeterministicVariable from pyrenew.distutil import validate_discrete_dist_vector from pyrenew.metaclass import RandomVariable @@ -10,7 +11,7 @@ class DeterministicPMF(RandomVariable): def __init__( self, - vars: tuple, + vars: ArrayLike, label: str = "a_random_variable", tol: float = 1e-20, ) -> None: @@ -23,8 +24,8 @@ def __init__( Parameters ---------- - vars : tuple - A tuple with arraylike objects. + vars : ArrayLike + An array with the fixed quantity. label : str A label to assign to the process. tol : float @@ -34,15 +35,12 @@ def __init__( ------- None """ + vars = validate_discrete_dist_vector( + discrete_dist=vars, + tol=tol, + ) - vars2 = list(vars) - for i in range(0, len(vars2)): - vars2[i] = validate_discrete_dist_vector( - discrete_dist=vars2[i], - tol=tol, - ) - - self.basevar = DeterministicVariable(tuple(vars2), label) + self.basevar = DeterministicVariable(vars, label) return None diff --git a/model/src/pyrenew/deterministic/process.py b/model/src/pyrenew/deterministic/process.py new file mode 100644 index 00000000..c46206a6 --- /dev/null +++ b/model/src/pyrenew/deterministic/process.py @@ -0,0 +1,37 @@ +import jax.numpy as jnp +from pyrenew.deterministic.deterministic import DeterministicVariable + + +class DeterministicProcess(DeterministicVariable): + """A deterministic process (degenerate) random variable. + Useful to pass fixed quantities over time.""" + + __init__ = DeterministicVariable.__init__ + + def sample( + self, + n_timepoints: int, + **kwargs, + ) -> tuple: + """Retrieve the value of the deterministic Rv + Parameters + ---------- + n_timepoints : int + Number of timepoints to sample. + **kwargs : dict, optional + Ignored. + + Returns + ------- + tuple + Containing the stored values during construction. + """ + + res, *_ = super().sample(**kwargs) + + dif = n_timepoints - res.shape[0] + + if dif > 0: + return (jnp.hstack([res, jnp.repeat(res[-1], dif)]),) + + return (res[:n_timepoints],) diff --git a/model/src/pyrenew/latent/hospitaladmissions.py b/model/src/pyrenew/latent/hospitaladmissions.py index 0f90b601..5a8b9f5f 100644 --- a/model/src/pyrenew/latent/hospitaladmissions.py +++ b/model/src/pyrenew/latent/hospitaladmissions.py @@ -129,9 +129,9 @@ def __init__( """ if weekday_effect_dist is None: - weekday_effect_dist = DeterministicVariable((1,)) + weekday_effect_dist = DeterministicVariable(1) if hosp_report_prob_dist is None: - hosp_report_prob_dist = DeterministicVariable((1,)) + hosp_report_prob_dist = DeterministicVariable(1) HospitalAdmissions.validate( infect_hosp_rate_dist, diff --git a/model/src/pyrenew/model/hospitalizations.py b/model/src/pyrenew/model/hospitalizations.py index 5000802a..ed9017e5 100644 --- a/model/src/pyrenew/model/hospitalizations.py +++ b/model/src/pyrenew/model/hospitalizations.py @@ -64,7 +64,7 @@ def __init__( gen_int=gen_int, I0=I0, latent_infections=latent_infections, - observed_infections=DeterministicVariable((0,)), + observed_infections=DeterministicVariable(0), Rt_process=Rt_process, ) diff --git a/model/src/pyrenew/observation/negativebinomial.py b/model/src/pyrenew/observation/negativebinomial.py index 331ae828..d072c95b 100644 --- a/model/src/pyrenew/observation/negativebinomial.py +++ b/model/src/pyrenew/observation/negativebinomial.py @@ -16,6 +16,7 @@ def __init__( concentration_prior: dist.Distribution | ArrayLike, concentration_suffix: str = "_concentration", parameter_name="negbinom_rv", + eps: float = 1e-10, ) -> None: """Default constructor @@ -32,6 +33,9 @@ def __init__( Suffix for the numpy variable. parameter_name : str, optional Name for the numpy variable. + eps : float, optional + Small value to add to the predicted mean to prevent numerical + instability. Returns ------- @@ -50,6 +54,7 @@ def __init__( self.parameter_name = parameter_name self.concentration_suffix = concentration_suffix + self.eps = eps def sample( self, @@ -79,7 +84,7 @@ def sample( numpyro.sample( self.parameter_name, dist.NegativeBinomial2( - mean=predicted, + mean=predicted + self.eps, concentration=concentration, ), obs=obs, diff --git a/model/src/test/test_model_basic_renewal.py b/model/src/test/test_model_basic_renewal.py index 83aa27a5..11b8e443 100644 --- a/model/src/test/test_model_basic_renewal.py +++ b/model/src/test/test_model_basic_renewal.py @@ -34,7 +34,7 @@ def test_model_basicrenewal_no_obs_model(): I0=I0, latent_infections=latent_infections, Rt_process=rt, - observed_infections=DeterministicVariable((1,)), + observed_infections=DeterministicVariable(1), ) # Sampling and fitting model 0 (with no obs for infections) diff --git a/model/src/test/test_model_hospitalizations.py b/model/src/test/test_model_hospitalizations.py index 11c7844a..36a0c522 100644 --- a/model/src/test/test_model_hospitalizations.py +++ b/model/src/test/test_model_hospitalizations.py @@ -89,7 +89,7 @@ def test_model_hosp_no_obs_model(): Rt_process=Rt_process, latent_infections=latent_infections, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=DeterministicVariable((0,)), + observed_hospitalizations=DeterministicVariable(0), ) # Sampling and fitting model 0 (with no obs for infections) @@ -342,7 +342,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): weekday = weekday / weekday.sum() weekday = weekday[:31] - weekday = DeterministicVariable((weekday,)) + weekday = DeterministicVariable(weekday) hosp_report_prob_dist = jnp.array([0.9, 0.8, 0.7, 0.7, 0.6, 0.4]) hosp_report_prob_dist = jnp.tile(hosp_report_prob_dist, 10) @@ -350,9 +350,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): hosp_report_prob_dist = hosp_report_prob_dist[:31] - hosp_report_prob_dist = DeterministicVariable( - vars=(hosp_report_prob_dist,) - ) + hosp_report_prob_dist = DeterministicVariable(vars=hosp_report_prob_dist) latent_hospitalizations = HospitalAdmissions( infection_to_admission_interval=inf_hosp, From 0ac530ff228965cf8e8fb21108d234e131e14756 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 9 Apr 2024 15:58:52 -0600 Subject: [PATCH 08/24] Adding deterministic test and fixing qmd --- model/docs/pyrenew_demo.md | 6 +- model/docs/pyrenew_demo.qmd | 6 +- model/src/test/test_deterministic.py | 51 +++++ .../src/test/test_latent_hospitalizations.py | 45 ++-- model/src/test/test_model_basic_renewal.py | 8 +- model/src/test/test_model_hospitalizations.py | 193 ++++++++---------- 6 files changed, 168 insertions(+), 141 deletions(-) create mode 100644 model/src/test/test_deterministic.py diff --git a/model/docs/pyrenew_demo.md b/model/docs/pyrenew_demo.md index 5fc9d402..19aa22b6 100644 --- a/model/docs/pyrenew_demo.md +++ b/model/docs/pyrenew_demo.md @@ -47,9 +47,7 @@ from pyrenew.process import RtRandomWalkProcess # Initializing model components: # A deterministic generation time -gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) +gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) # Initial infections I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) @@ -59,7 +57,7 @@ latent_infections = Infections() # A deterministic infection to hosp pmf inf_hosp_int = DeterministicPMF( - (jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]),), + jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]), ) # The latent hospitalization process diff --git a/model/docs/pyrenew_demo.qmd b/model/docs/pyrenew_demo.qmd index 78e8ad12..eb150e32 100644 --- a/model/docs/pyrenew_demo.qmd +++ b/model/docs/pyrenew_demo.qmd @@ -49,9 +49,7 @@ from pyrenew.process import RtRandomWalkProcess # Initializing model components: # A deterministic generation time -gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) +gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) # Initial infections I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) @@ -61,7 +59,7 @@ latent_infections = Infections() # A deterministic infection to hosp pmf inf_hosp_int = DeterministicPMF( - (jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]),), + jnp.array([0, 0, 0,0,0,0,0,0,0,0,0,0,0, 0.25, 0.5, 0.1, 0.1, 0.05]), ) # The latent hospitalization process diff --git a/model/src/test/test_deterministic.py b/model/src/test/test_deterministic.py new file mode 100644 index 00000000..c52e0f90 --- /dev/null +++ b/model/src/test/test_deterministic.py @@ -0,0 +1,51 @@ +import jax.numpy as jnp +import numpy.testing as testing +from pyrenew.deterministic import ( + DeterministicPMF, + DeterministicProcess, + DeterministicVariable, +) + + +def test_deterministic(): + """Test the DeterministicVariable, DeterministicPMF, and + DeterministicProcess classes in the deterministic module. + """ + + var1 = DeterministicVariable( + jnp.array( + [ + 1, + ] + ) + ) + var2 = DeterministicPMF(jnp.array([0.25, 0.25, 0.2, 0.3])) + var3 = DeterministicProcess(jnp.array([1, 2, 3, 4])) + + testing.assert_array_equal( + var1.sample()[0], + jnp.array( + [ + 1, + ] + ), + ) + testing.assert_array_equal( + var2.sample()[0], + jnp.array([0.25, 0.25, 0.2, 0.3]), + ) + testing.assert_array_equal( + var3.sample(n_timepoints=5)[0], + jnp.array([1, 2, 3, 4, 4]), + ) + + testing.assert_array_equal( + var3.sample(n_timepoints=3)[0], + jnp.array( + [ + 1, + 2, + 3, + ] + ), + ) diff --git a/model/src/test/test_latent_hospitalizations.py b/model/src/test/test_latent_hospitalizations.py index 9a49d9ee..056ce1e9 100644 --- a/model/src/test/test_latent_hospitalizations.py +++ b/model/src/test/test_latent_hospitalizations.py @@ -32,31 +32,30 @@ def test_hospitalizations_sample(): # Testing the hospitalizations inf_hosp = DeterministicPMF( - ( - jnp.array( - [ - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0.25, - 0.5, - 0.1, - 0.1, - 0.05, - ] - ), + jnp.array( + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.25, + 0.5, + 0.1, + 0.1, + 0.05, + ] ), ) + hosp1 = HospitalAdmissions( infection_to_admission_interval=inf_hosp, infect_hosp_rate_dist=InfectHospRate( diff --git a/model/src/test/test_model_basic_renewal.py b/model/src/test/test_model_basic_renewal.py index 11b8e443..ef400e3c 100644 --- a/model/src/test/test_model_basic_renewal.py +++ b/model/src/test/test_model_basic_renewal.py @@ -19,9 +19,7 @@ def test_model_basicrenewal_no_obs_model(): from the perspective of the infections. It returns expected, not sampled. """ - gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) @@ -68,9 +66,7 @@ def test_model_basicrenewal_with_obs_model(): from the perspective of the infections. It returns sampled, not expected. """ - gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) diff --git a/model/src/test/test_model_hospitalizations.py b/model/src/test/test_model_hospitalizations.py index 36a0c522..59627954 100644 --- a/model/src/test/test_model_hospitalizations.py +++ b/model/src/test/test_model_hospitalizations.py @@ -41,40 +41,37 @@ def test_model_hosp_no_obs_model(): Hospitalization model runs """ - gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) latent_infections = Infections() Rt_process = RtRandomWalkProcess() inf_hosp = DeterministicPMF( - ( - jnp.array( - [ - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0.25, - 0.5, - 0.1, - 0.1, - 0.05, - ], - ), + jnp.array( + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.25, + 0.5, + 0.1, + 0.1, + 0.05, + ] ), ) + latent_hospitalizations = HospitalAdmissions( infection_to_admission_interval=inf_hosp, hospitalizations_predicted_varname="observed_hospitalizations", @@ -122,9 +119,7 @@ def test_model_hosp_with_obs_model(): Checks that the random Hospitalization model runs """ - gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) @@ -133,29 +128,27 @@ def test_model_hosp_with_obs_model(): observed_hospitalizations = PoissonObservation() inf_hosp = DeterministicPMF( - ( - jnp.array( - [ - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0.25, - 0.5, - 0.1, - 0.1, - 0.05, - ], - ), + jnp.array( + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.25, + 0.5, + 0.1, + 0.1, + 0.05, + ], ), ) @@ -205,9 +198,7 @@ def test_model_hosp_with_obs_model_weekday_phosp_2(): Checks that the random Hospitalization model runs """ - gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) @@ -216,29 +207,27 @@ def test_model_hosp_with_obs_model_weekday_phosp_2(): observed_hospitalizations = PoissonObservation() inf_hosp = DeterministicPMF( - ( - jnp.array( - [ - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0.25, - 0.5, - 0.1, - 0.1, - 0.05, - ], - ), + jnp.array( + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.25, + 0.5, + 0.1, + 0.1, + 0.05, + ], ), ) @@ -299,9 +288,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): Checks that the random Hospitalization model runs """ - gen_int = DeterministicPMF( - (jnp.array([0.25, 0.25, 0.25, 0.25]),), - ) + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) @@ -310,29 +297,27 @@ def test_model_hosp_with_obs_model_weekday_phosp(): observed_hospitalizations = PoissonObservation() inf_hosp = DeterministicPMF( - ( - jnp.array( - [ - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0.25, - 0.5, - 0.1, - 0.1, - 0.05, - ], - ), + jnp.array( + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0.25, + 0.5, + 0.1, + 0.1, + 0.05, + ], ), ) From e6f56a8faf117c42e5e47cfaf58fcd691a9dd6aa Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 10 Apr 2024 14:26:54 -0600 Subject: [PATCH 09/24] Adding weekday effect to model. Still not good enough --- model/docs/example-with-datasets.md | 135 ++++++++++++++++-- model/docs/example-with-datasets.qmd | 114 ++++++++++++++- .../figure-commonmark/basic-fig-output-1.png | Bin 32559 -> 32697 bytes .../output-hospitalizations-output-1.png | Bin 64304 -> 63274 bytes ...tput-hospitalizations-weekday-output-1.png | Bin 0 -> 62065 bytes .../figure-commonmark/fig-hosp-output-1.png | Bin 0 -> 14325 bytes .../fig-randwalk-output-1.png | Bin 0 -> 29905 bytes .../fig-sampled-rt-output-1.png | Bin 0 -> 38249 bytes model/src/pyrenew/process/rtrandomwalk.py | 4 +- 9 files changed, 238 insertions(+), 15 deletions(-) create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospitalizations-weekday-output-1.png create mode 100644 model/docs/pyrenew_demo_files/figure-commonmark/fig-hosp-output-1.png create mode 100644 model/docs/pyrenew_demo_files/figure-commonmark/fig-randwalk-output-1.png create mode 100644 model/docs/pyrenew_demo_files/figure-commonmark/fig-sampled-rt-output-1.png diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index ccf10a29..01beda57 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -12,10 +12,6 @@ from pyrenew.datasets import load_wastewater from pyrenew.model import HospitalizationsModel ``` - /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html - from .autonotebook import tqdm as notebook_tqdm - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - ## Model definition In this section we provide the formal definition of the model. The @@ -177,7 +173,7 @@ src="example-with-datasets_files/figure-commonmark/data-extract-output-2.png" id="data-extract-2" /> With these two in hand, we can start building the model. First, we will -define the latent hospitalizations. +define the latent hospitalizations: ``` python from pyrenew.latent import HospitalAdmissions, InfectHospRate @@ -188,7 +184,7 @@ import numpyro.distributions as dist inf_hosp_int = DeterministicPMF(inf_hosp_int) hosp_rate = InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.05) + dist=dist.LogNormal(jnp.log(0.05), 0.01) ) latent_hosp = HospitalAdmissions( @@ -197,7 +193,13 @@ latent_hosp = HospitalAdmissions( ) ``` -Now we can define the rest of the other model components: +The `inf_hosp_int` is a `DeterministicPMF` object that takes the +infection to hospitalization interval as input. The `hosp_rate` is an +`InfectHospRate` object that takes the infection to hospitalization rate +as input. The `HospitalAdmissions` class is a `RandomVariable` that +takes two distributions as inputs: the infection to admission interval +and the infection to hospitalization rate. Now we can define the rest of +the other model components: ``` python from pyrenew.model import HospitalizationsModel @@ -207,7 +209,7 @@ from pyrenew.observation import NegativeBinomialObservation # Infection process latent_inf = Infections() -I0 = Infections0() +I0 = Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=0.5)) # Generation interval and Rt gen_int = DeterministicPMF(gen_int) @@ -277,7 +279,7 @@ import jax model.run( num_samples=2000, - num_warmup=1000, + num_warmup=2000, n_timepoints=dat.shape[0] - 1, observed_hospitalizations=dat["hospitalizations"].to_numpy(), rng_key=jax.random.PRNGKey(54), @@ -310,3 +312,118 @@ ax.legend() ![Hospitalizations posterior distribution](example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png) + +The first half of the model is not looking good. We can try to improve +the model by incorporating weekday effects. The following section will +show how to do this. + +## Round 2: Incorporating weekday effects + +We will re-use the infection to admission interval and infection to +hospitalization rate from the previous model. But we will also add a +weekday effect distribution. To do this, we will create a new instance +of `RandomVariable` to model the weekday effect. The weekday effect will +be a truncated normal distribution with a mean of 1.0 and a standard +deviation of 0.5. The distribution will be truncated between 0.1 and +10.0. The weekday effect will be repeated for the number of weeks in the +dataset. + +``` python +from pyrenew.metaclass import RandomVariable +import numpyro as npro + +class WeekdayEffect(RandomVariable): + """Weekday effect distribution""" + def __init__(self, len: int): + """ Initialize the weekday effect distribution + Parameters + ---------- + len : int + The number of observations + """ + self.nweeks = jnp.ceil(len/7).astype(int) + self.len = len + + @staticmethod + def validate(): + return None + + def sample(self, **kwargs): + ans = npro.sample( + name="weekday_effect", + fn=npro.distributions.TruncatedNormal( + loc=1.0, scale=.5, low=0.1, high=10.0 + ), + sample_shape=(7,) + ) + + return jnp.tile(ans, self.nweeks)[:self.len] + +# Initializing the weekday effect +weekday_effect = WeekdayEffect(dat.shape[0]) +``` + +Notice that the instance’s `nweeks` and `len` members are passed during +construction. Trying to compute the number of weeks and the length of +the dataset in the `validate` method will raise a `jit` error in `jax` +as the shape and size of elements are not known during the validation +step, which happens before the model is run. With the new weekday +effect, we can rebuild the latent hospitalization model: + +``` python +latent_hosp_wday_effect = HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist=hosp_rate, + weekday_effect_dist=weekday_effect, + ) + +model_weekday = HospitalizationsModel( + latent_infections=latent_inf, + latent_hospitalizations=latent_hosp_wday_effect, + I0=I0, + gen_int=gen_int, + Rt_process=process, + observed_hospitalizations=obs, +) +``` + +Running the model: + +``` python +model_weekday.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat.shape[0] - 1, + observed_hospitalizations=dat["hospitalizations"].to_numpy(), + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), +) +``` + +And plotting the results: + +``` python +import polars as pl +samps = model_weekday.spread_draws([('predicted_hospitalizations', 'time')]) + +fig, ax = plt.subplots(figsize=[4, 5]) + +ax.plot(dat["hospitalizations"].to_numpy(), color="black") +samp_ids = np.random.randint(size=25, low=0, high=999) +for samp_id in samp_ids: + sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) + ax.plot(sub_samps.select("time").to_numpy(), + sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) + +# Some labels +ax.set_xlabel("Time") +ax.set_ylabel("Hospitalizations") + +# Adding a legend +ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") +ax.plot([], [], color="black", label="Observed hospitalizations") +ax.legend() +``` + +![Hospitalizations posterior +distribution](example-with-datasets_files/figure-commonmark/output-hospitalizations-weekday-output-1.png) diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 946d9445..0927d2d2 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -115,7 +115,7 @@ axs[1].plot(inf_hosp_int) axs[1].set_title("Infection to hospitalization interval") ``` -With these two in hand, we can start building the model. First, we will define the latent hospitalizations. +With these two in hand, we can start building the model. First, we will define the latent hospitalizations: ```{python} #| label: latent-hosp @@ -127,7 +127,7 @@ import numpyro.distributions as dist inf_hosp_int = DeterministicPMF(inf_hosp_int) hosp_rate = InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.05) + dist=dist.LogNormal(jnp.log(0.05), 0.01) ) latent_hosp = HospitalAdmissions( @@ -136,7 +136,7 @@ latent_hosp = HospitalAdmissions( ) ``` -Now we can define the rest of the other model components: +The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate as input. The `HospitalAdmissions` class is a `RandomVariable` that takes two distributions as inputs: the infection to admission interval and the infection to hospitalization rate. Now we can define the rest of the other model components: ```{python} #| label: initializing-rest-of-model @@ -147,7 +147,7 @@ from pyrenew.observation import NegativeBinomialObservation # Infection process latent_inf = Infections() -I0 = Infections0() +I0 = Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=0.5)) # Generation interval and Rt gen_int = DeterministicPMF(gen_int) @@ -218,7 +218,7 @@ import jax model.run( num_samples=2000, - num_warmup=1000, + num_warmup=2000, n_timepoints=dat.shape[0] - 1, observed_hospitalizations=dat["hospitalizations"].to_numpy(), rng_key=jax.random.PRNGKey(54), @@ -252,3 +252,107 @@ ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") ax.plot([], [], color="black", label="Observed hospitalizations") ax.legend() ``` + +The first half of the model is not looking good. We can try to improve the model by incorporating weekday effects. The following section will show how to do this. + +## Round 2: Incorporating weekday effects + +We will re-use the infection to admission interval and infection to hospitalization rate from the previous model. But we will also add a weekday effect distribution. To do this, we will create a new instance of `RandomVariable` to model the weekday effect. The weekday effect will be a truncated normal distribution with a mean of 1.0 and a standard deviation of 0.5. The distribution will be truncated between 0.1 and 10.0. The weekday effect will be repeated for the number of weeks in the dataset. + +```{python} +#| label: weekly-effect +from pyrenew.metaclass import RandomVariable +import numpyro as npro + +class WeekdayEffect(RandomVariable): + """Weekday effect distribution""" + def __init__(self, len: int): + """ Initialize the weekday effect distribution + Parameters + ---------- + len : int + The number of observations + """ + self.nweeks = jnp.ceil(len/7).astype(int) + self.len = len + + @staticmethod + def validate(): + return None + + def sample(self, **kwargs): + ans = npro.sample( + name="weekday_effect", + fn=npro.distributions.TruncatedNormal( + loc=1.0, scale=.5, low=0.1, high=10.0 + ), + sample_shape=(7,) + ) + + return jnp.tile(ans, self.nweeks)[:self.len] + +# Initializing the weekday effect +weekday_effect = WeekdayEffect(dat.shape[0]) +``` + +Notice that the instance's `nweeks` and `len` members are passed during construction. Trying to compute the number of weeks and the length of the dataset in the `validate` method will raise a `jit` error in `jax` as the shape and size of elements are not known during the validation step, which happens before the model is run. With the new weekday effect, we can rebuild the latent hospitalization model: + +```{python} +#| label: latent-hosp-weekday +latent_hosp_wday_effect = HospitalAdmissions( + infection_to_admission_interval=inf_hosp_int, + infect_hosp_rate_dist=hosp_rate, + weekday_effect_dist=weekday_effect, + ) + +model_weekday = HospitalizationsModel( + latent_infections=latent_inf, + latent_hospitalizations=latent_hosp_wday_effect, + I0=I0, + gen_int=gen_int, + Rt_process=process, + observed_hospitalizations=obs, +) +``` + +Running the model: + +```{python} +#| label: model-2-run +model_weekday.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat.shape[0] - 1, + observed_hospitalizations=dat["hospitalizations"].to_numpy(), + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), +) +``` + +And plotting the results: + +```{python} +#| label: output-hospitalizations-weekday +#| fig-cap: Hospitalizations posterior distribution +#| fig-cap-location: bottom +import polars as pl +samps = model_weekday.spread_draws([('predicted_hospitalizations', 'time')]) + +fig, ax = plt.subplots(figsize=[4, 5]) + +ax.plot(dat["hospitalizations"].to_numpy(), color="black") +samp_ids = np.random.randint(size=25, low=0, high=999) +for samp_id in samp_ids: + sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) + ax.plot(sub_samps.select("time").to_numpy(), + sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) + +# Some labels +ax.set_xlabel("Time") +ax.set_ylabel("Hospitalizations") + +# Adding a legend +ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") +ax.plot([], [], color="black", label="Observed hospitalizations") +ax.legend() +``` diff --git a/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png b/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png index db6ef442f05c6e7c99ffdd4f1812b176d278e39d..bac3e02c38ce4252c87c13e642486cfff5448a60 100644 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init=Rt0_trans, ) Rt = npro.deterministic("Rt", self.Rt_transform.inverse(Rt_trans_ts)) From e749c74b72ab8c437c97439371d728eb4d8688a2 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 11 Apr 2024 13:25:37 -0600 Subject: [PATCH 10/24] Rewriting downloaders to python --- model/src/pyrenew/datasets/data-raw/README.md | 3 ++ .../{ => data-raw}/generation_interval.R | 0 .../datasets/data-raw/generation_interval.py | 14 ++++++++ .../data-raw/infection_admission_interval.py | 15 ++++++++ .../pyrenew/datasets/data-raw/wastewater.R | 13 +++++++ model/src/pyrenew/datasets/example_df.R | 11 ------ model/src/pyrenew/datasets/example_df.rda | Bin 4533 -> 0 bytes .../datasets/infection_admission_interval.R | 13 ------- .../datasets/infection_admission_interval.tsv | 34 +++++++++--------- 9 files changed, 62 insertions(+), 41 deletions(-) create mode 100644 model/src/pyrenew/datasets/data-raw/README.md rename model/src/pyrenew/datasets/{ => data-raw}/generation_interval.R (100%) create mode 100644 model/src/pyrenew/datasets/data-raw/generation_interval.py create mode 100644 model/src/pyrenew/datasets/data-raw/infection_admission_interval.py create mode 100644 model/src/pyrenew/datasets/data-raw/wastewater.R delete mode 100644 model/src/pyrenew/datasets/example_df.R delete mode 100644 model/src/pyrenew/datasets/example_df.rda delete mode 100644 model/src/pyrenew/datasets/infection_admission_interval.R diff --git a/model/src/pyrenew/datasets/data-raw/README.md b/model/src/pyrenew/datasets/data-raw/README.md new file mode 100644 index 00000000..d3053f86 --- /dev/null +++ b/model/src/pyrenew/datasets/data-raw/README.md @@ -0,0 +1,3 @@ +# Raw data + +This folder contains the scripts used to download raw data from the wastewater project and save it as tsv for the pyrenew project. diff --git a/model/src/pyrenew/datasets/generation_interval.R b/model/src/pyrenew/datasets/data-raw/generation_interval.R similarity index 100% rename from model/src/pyrenew/datasets/generation_interval.R rename to model/src/pyrenew/datasets/data-raw/generation_interval.R diff --git a/model/src/pyrenew/datasets/data-raw/generation_interval.py b/model/src/pyrenew/datasets/data-raw/generation_interval.py new file mode 100644 index 00000000..878c37a3 --- /dev/null +++ b/model/src/pyrenew/datasets/data-raw/generation_interval.py @@ -0,0 +1,14 @@ +# Dataset from: +# https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv +import polars as pl + +gen_int = pl.read_csv( + "https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv", +) + +gen_int.write_csv( + file="src/pyrenew/datasets/gen_int.tsv", + separator="\t", + include_header=True, + null_value="", +) diff --git a/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py b/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py new file mode 100644 index 00000000..05b62009 --- /dev/null +++ b/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py @@ -0,0 +1,15 @@ +# Dataset from: +# https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv +import polars as pl + +# Read CSV file from URL +url = "https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv" +infection_admission_interval = pl.read_csv(url) + +# Save as TSV +infection_admission_interval.write_csv( + file="src/pyrenew/datasets/infection_admission_interval.tsv", + separator="\t", + include_header=True, + null_value="", +) diff --git a/model/src/pyrenew/datasets/data-raw/wastewater.R b/model/src/pyrenew/datasets/data-raw/wastewater.R new file mode 100644 index 00000000..17144248 --- /dev/null +++ b/model/src/pyrenew/datasets/data-raw/wastewater.R @@ -0,0 +1,13 @@ +load( + "https://github.com/CDCgov/wastewater-informed-covid-forecasting/blob/292526383ece582f10823fc939c7e590ca349c6d/cfaforecastrenewalww/data/example_df.rda" + ) + +# Saving as TSV +write.table( + x = example_df, + file = "src/pyrenew/datasets/wastewater.tsv", + sep = "\t", + quote = FALSE, + row.names = FALSE, + na = "" + ) diff --git a/model/src/pyrenew/datasets/example_df.R b/model/src/pyrenew/datasets/example_df.R deleted file mode 100644 index 81d970dd..00000000 --- a/model/src/pyrenew/datasets/example_df.R +++ /dev/null @@ -1,11 +0,0 @@ -load("src/pyrenew/datasets/example_df.rda") - -# Saving as TSV -write.table( - x = example_df, - file = "src/pyrenew/datasets/wastewater.tsv", - sep = "\t", - quote = FALSE, - row.names = FALSE, - na = "" - ) diff --git a/model/src/pyrenew/datasets/example_df.rda b/model/src/pyrenew/datasets/example_df.rda deleted file mode 100644 index 553640ea6ed1c7cee87671600e6240b1f95fd343..0000000000000000000000000000000000000000 GIT binary patch literal 0 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zf%%ZJ0od=Uf2zJ9kkN$owd_G3{kQgs5t@paMoZ_#1UiO!-*l#{+r0!5)(n*fdyIn- zui7`yNtG4%4vCXL_zEX6tua5^WqwWwpghL4jt;Xi%f1Hil?=_y@7VmMi<-A2o9*d! zZ9}RMcfxR>_em6VjUwhW`lhnArVw_cmahC0V_bgk)eT{`b5q3(pBq;y964IFeV%hd zd$=!lk*3#Seek?(P2cf2N{P+w5e)KTNVX5`8FmnDe8#9DN%2?87>=}7W#(ewAUo Date: Thu, 11 Apr 2024 15:17:13 -0600 Subject: [PATCH 11/24] Using os.path.exist --- .../datasets/data-raw/generation_interval.R | 13 ------------- .../datasets/data-raw/generation_interval.py | 14 +++++++++++++- .../data-raw/infection_admission_interval.py | 14 +++++++++++++- 3 files changed, 26 insertions(+), 15 deletions(-) delete mode 100644 model/src/pyrenew/datasets/data-raw/generation_interval.R diff --git a/model/src/pyrenew/datasets/data-raw/generation_interval.R b/model/src/pyrenew/datasets/data-raw/generation_interval.R deleted file mode 100644 index e25d91ba..00000000 --- a/model/src/pyrenew/datasets/data-raw/generation_interval.R +++ /dev/null @@ -1,13 +0,0 @@ -# Dataset from: -# https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv -gen_int <- read.csv("https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv") - -# Saving as TSV -write.table( - x = gen_int, - file = "src/pyrenew/datasets/gen_int.tsv", - sep = "\t", - quote = FALSE, - row.names = FALSE, - na = "" - ) diff --git a/model/src/pyrenew/datasets/data-raw/generation_interval.py b/model/src/pyrenew/datasets/data-raw/generation_interval.py index 878c37a3..7d0d1a34 100644 --- a/model/src/pyrenew/datasets/data-raw/generation_interval.py +++ b/model/src/pyrenew/datasets/data-raw/generation_interval.py @@ -1,13 +1,25 @@ # Dataset from: # https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv +import os + import polars as pl gen_int = pl.read_csv( "https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/generation_interval.csv", ) +# Building path to save the file +path = os.path.join( + "src", + "pyrenew", + "datasets", + "gen_int.tsv", +) + +assert os.path.exists(os.path.dirname(path)) + gen_int.write_csv( - file="src/pyrenew/datasets/gen_int.tsv", + file=path, separator="\t", include_header=True, null_value="", diff --git a/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py b/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py index 05b62009..0243d842 100644 --- a/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py +++ b/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py @@ -1,14 +1,26 @@ # Dataset from: # https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv +import os + import polars as pl # Read CSV file from URL url = "https://raw.githubusercontent.com/CDCgov/wastewater-informed-covid-forecasting/0962c5d1652787479ac72caebf076ab55fe4e10c/input/saved_pmfs/inf_to_hosp.csv" infection_admission_interval = pl.read_csv(url) +# Building path to save the file +path = os.path.join( + "src", + "pyrenew", + "datasets", + "infection_admission_interval.tsv", +) + +assert os.path.exists(os.path.dirname(path)) + # Save as TSV infection_admission_interval.write_csv( - file="src/pyrenew/datasets/infection_admission_interval.tsv", + file=path, separator="\t", include_header=True, null_value="", From 5c26233b5c191f329d481462e2ec9ddc6bedb199 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 16 Apr 2024 16:03:50 -0600 Subject: [PATCH 12/24] Adding test for plot --- .github/workflows/test_model.yml | 2 +- model/Makefile | 16 +- model/docs/example-with-datasets.md | 153 +++++++----------- model/docs/example-with-datasets.qmd | 102 ++++-------- .../figure-commonmark/basic-fig-output-1.png | Bin 32697 -> 47013 bytes .../output-hospital-admissions-output-1.png | Bin 0 -> 69266 bytes ...t-hospital-admissions-weekday-output-1.png | Bin 0 -> 66437 bytes .../output-hospitalizations-output-1.png | Bin 62338 -> 0 bytes ...tput-hospitalizations-weekday-output-1.png | Bin 62184 -> 0 bytes .../plot-hospital-admissions-output-1.png | Bin 0 -> 42267 bytes .../plot-hospitalizations-output-1.png | Bin 44459 -> 0 bytes model/docs/getting-started.md | 12 +- model/docs/getting-started.qmd | 8 +- .../figure-commonmark/basic-fig-output-1.png | Bin 36656 -> 39115 bytes model/docs/pyrenew_demo.md | 56 ++----- model/docs/pyrenew_demo.qmd | 16 +- model/pyproject.toml | 1 + model/src/pyrenew/mcmcutils.py | 70 ++++++++ model/src/pyrenew/metaclass.py | 17 +- model/src/pyrenew/model/hospitalizations.py | 18 +-- .../src/pyrenew/model/rtinfectionsrenewal.py | 14 +- .../test_model_basicrenewal_plot.png | Bin 0 -> 32380 bytes model/src/test/test_model_basic_renewal.py | 45 +++++- model/src/test/test_model_hospitalizations.py | 8 +- 24 files changed, 283 insertions(+), 255 deletions(-) create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospital-admissions-output-1.png create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospital-admissions-weekday-output-1.png delete mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png delete mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospitalizations-weekday-output-1.png create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/plot-hospital-admissions-output-1.png delete mode 100644 model/docs/example-with-datasets_files/figure-commonmark/plot-hospitalizations-output-1.png create mode 100644 model/src/test/reference_images/test_model_basicrenewal_plot.png diff --git a/.github/workflows/test_model.yml b/.github/workflows/test_model.yml index 9ea0c79f..0408c767 100644 --- a/.github/workflows/test_model.yml +++ b/.github/workflows/test_model.yml @@ -29,7 +29,7 @@ jobs: - name: run tests run: | poetry run -C model pytest \ - --cov=pyrenew --cov-report term --cov-report xml model + --mpl --cov=pyrenew --cov-report term --cov-report xml model - name: Upload coverage to Codecov uses: codecov/codecov-action@v4 diff --git a/model/Makefile b/model/Makefile index 243726ec..35064f32 100644 --- a/model/Makefile +++ b/model/Makefile @@ -2,7 +2,7 @@ install: poetry install test: - poetry run pytest + poetry run pytest --mpl docs: docs/pyrenew_demo.md docs/getting-started.md \ docs/example-with-datasets.md @@ -27,10 +27,16 @@ docs/notebooks: quarto convert docs/example-with-datasets.qmd --output \ docs/example-with-datasets.ipynb +test_images: + echo "Generating reference images for tests" + poetry run pytest --mpl-generate-path=src/test/reference_image + clean: rm -rf docs/*_files/ - rm -f docs/getting-started.ipynb - rm -f docs/pyrenew_demo.ipynb - rm -f docs/example-with-datasets.ipynb + for i in .py, .md, .ipynb; do \ + rm -f docs/getting-started.$$i; \ + rm -f docs/pyrenew_demo.$$i; \ + rm -f docs/example-with-datasets.$$i; \ + done -.PHONY: install test docs clean docs/notebooks docs/py +.PHONY: install test docs clean docs/notebooks docs/py test_images diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index d839bcba..4fa2c328 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -1,41 +1,36 @@ -# Fitting an hospitalizations only model +# Fitting a Hospital Admissions-only Model -This document illustrates how a hospitalization-only model can be fitted -using data from the Pyrenew package, particularly the wastewater +This document illustrates how a hospital admissions-only model can be +fitted using data from the Pyrenew package, particularly the wastewater dataset. The CFA wastewater team created this dataset, which contains simulated data. ``` python import polars as pl from pyrenew.datasets import load_wastewater -from pyrenew.model import HospitalizationsModel ``` - /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html - from .autonotebook import tqdm as notebook_tqdm - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - ## Model definition -In this section we provide the formal definition of the model. The +In this section, we provide the formal definition of the model. The hospitalization model is a semi-mechanistic model that describes the -number of observed hospitalizations as a function of a set of latent -variables. Particularly, the observed number of hospitalizations is -discretily distributed with location at the number of latent -hospitalizations: +number of observed hospital admissions as a function of a set of latent +variables. Mainly, the observed number of hospital admissions is +discretely distributed with location at the number of latent hospital +admissions: $$ h(t) \sim \text{HospDist}\left(H(t)\right) $$ -Where $h(t)$ is the observed number of hospitalizations at time $t$, and -$H(t)$ is the number of latent hospitalizations at time $t$. The -distribution $\text{HospDist}$ can be any discrete distribution, but in -this case we will use a negative binomial distribution. +Where $h(t)$ is the observed number of hospital admissions at time $t$, +and $H(t)$ is the number of latent hospital admissions at time $t$. The +distribution $\text{HospDist}$ can be any discrete distribution, but we +will use a negative binomial distribution. -The number of latent hospitalizations at time $t$ is a function of the -number of latent infections at time $t$ and the infection to +The number of latent hospital admissions at time $t$ is a function of +the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: @@ -82,14 +77,13 @@ dat.head(5) -The data shows one entry per site. We will take the aggregated data for -all sites by date. +The data shows one entry per site, but the way it was simulated, the +number of admissions is the same across sites. Thus we will only keep +the first observation per day. ``` python -dat = dat.group_by("date").agg( - hospitalizations = pl.sum("daily_hosp_admits"), - population = pl.sum("pop"), -) +# Keeping the first observation of each date +dat = dat.group_by("date").first().select(["date", "daily_hosp_admits"]) # Now, sorting by date dat = dat.sort("date") @@ -107,20 +101,20 @@ dat.head(5) white-space: pre-wrap; } -shape: (5, 3) +shape: (5, 2) -| date | hospitalizations | population | -|--------------|------------------|------------| -| str | i64 | f64 | -| "2023-10-30" | 30 | 5e6 | -| "2023-10-31" | 40 | 5e6 | -| "2023-11-01" | 20 | 5e6 | -| "2023-11-02" | 40 | 5e6 | -| "2023-11-03" | 20 | 5e6 | +| date | daily_hosp_admits | +|--------------|-------------------| +| str | i64 | +| "2023-10-30" | 6 | +| "2023-10-31" | 8 | +| "2023-11-01" | 4 | +| "2023-11-02" | 8 | +| "2023-11-03" | 4 | -Let’s take a look at the daily prevalence of hospitalizations. +Let’s take a look at the daily prevalence of hospital admissions. ``` python import matplotlib.pyplot as plt @@ -129,15 +123,15 @@ import matplotlib.pyplot as plt ax = plt.gca() ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) ax.xaxis.set_tick_params(rotation=45) -plt.plot(dat["date"].to_numpy(), dat["hospitalizations"].to_numpy()) +plt.plot(dat["date"].to_numpy(), dat["daily_hosp_admits"].to_numpy()) plt.xlabel("Date") -plt.ylabel("Hospitalizations") +plt.ylabel("Admissions") plt.show() ``` +src="example-with-datasets_files/figure-commonmark/plot-hospital-admissions-output-1.png" +id="plot-hospital-admissions" /> ## Building the model @@ -177,7 +171,7 @@ src="example-with-datasets_files/figure-commonmark/data-extract-output-2.png" id="data-extract-2" /> With these two in hand, we can start building the model. First, we will -define the latent hospitalizations: +define the latent hospital admissions: ``` python from pyrenew.latent import HospitalAdmissions, InfectHospRate @@ -202,8 +196,8 @@ infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate as input. The `HospitalAdmissions` class is a `RandomVariable` that takes two distributions as inputs: the infection to admission interval -and the infection to hospitalization rate. Now we can define the rest of -the other model components: +and the infection to hospitalization rate. Now, we can define the rest +of the other components: ``` python from pyrenew.model import HospitalizationsModel @@ -237,7 +231,7 @@ model = HospitalizationsModel( ) ``` -Let’s run a simulation to check if the model is working: +Let’s simulate to check if the model is working: ``` python import numpyro as npro @@ -256,12 +250,13 @@ import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 2) # Rt plot -axs[0].plot(range(0, timeframe + 1), sim_data[0]) +axs[0].plot(range(0, timeframe + 1), sim_data.Rt) axs[0].set_ylabel('Rt') # Infections plot -axs[1].plot(range(0, timeframe + 1), sim_data[1]) +axs[1].plot(range(0, timeframe + 1), sim_data.sampled_admissions) axs[1].set_ylabel('Infections') +axs[1].set_yscale('log') fig.suptitle('Basic renewal model') fig.supxlabel('Time') @@ -274,9 +269,9 @@ Infections](example-with-datasets_files/figure-commonmark/basic-fig-output-1.png ## Fitting the model -Now we can fit the model to the data. We will use the `run` method of -the model object. The two inputs this model requires are `n_timepoints` -and `observed_hospitalizations` +We can fit the model to the data. We will use the `run` method of the +model object. The two inputs this model requires are `n_timepoints` and +`observed_hospitalizations` ``` python import jax @@ -285,37 +280,22 @@ model.run( num_samples=2000, num_warmup=2000, n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["hospitalizations"].to_numpy(), + observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), ) ``` ``` python -import polars as pl -samps = model.spread_draws([('predicted_hospitalizations', 'time')]) - -fig, ax = plt.subplots(figsize=[4, 5]) - -ax.plot(dat["hospitalizations"].to_numpy(), color="black") -samp_ids = np.random.randint(size=25, low=0, high=999) -for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) - -# Some labels -ax.set_xlabel("Time") -ax.set_ylabel("Hospitalizations") - -# Adding a legend -ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") -ax.plot([], [], color="black", label="Observed hospitalizations") -ax.legend() +model.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat["daily_hosp_admits"].to_numpy(), +) ``` -![Hospitalizations posterior -distribution](example-with-datasets_files/figure-commonmark/output-hospitalizations-output-1.png) +![Hospital Admissions posterior +distribution](example-with-datasets_files/figure-commonmark/output-hospital-admissions-output-1.png) The first half of the model is not looking good. We can try to improve the model by incorporating weekday effects. The following section will @@ -398,7 +378,7 @@ model_weekday.run( num_samples=2000, num_warmup=2000, n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["hospitalizations"].to_numpy(), + observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), ) @@ -407,27 +387,12 @@ model_weekday.run( And plotting the results: ``` python -import polars as pl -samps = model_weekday.spread_draws([('predicted_hospitalizations', 'time')]) - -fig, ax = plt.subplots(figsize=[4, 5]) - -ax.plot(dat["hospitalizations"].to_numpy(), color="black") -samp_ids = np.random.randint(size=25, low=0, high=999) -for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) - -# Some labels -ax.set_xlabel("Time") -ax.set_ylabel("Hospitalizations") - -# Adding a legend -ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") -ax.plot([], [], color="black", label="Observed hospitalizations") -ax.legend() +model_weekday.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat["daily_hosp_admits"].to_numpy(), +) ``` -![Hospitalizations posterior -distribution](example-with-datasets_files/figure-commonmark/output-hospitalizations-weekday-output-1.png) +![Hospital Admissions posterior +distribution](example-with-datasets_files/figure-commonmark/output-hospital-admissions-weekday-output-1.png) diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 0927d2d2..22943802 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -1,29 +1,28 @@ --- -title: Fitting an hospitalizations only model +title: Fitting a Hospital Admissions-only Model format: gfm engine: jupyter --- -This document illustrates how a hospitalization-only model can be fitted using data from the Pyrenew package, particularly the wastewater dataset. The CFA wastewater team created this dataset, which contains simulated data. +This document illustrates how a hospital admissions-only model can be fitted using data from the Pyrenew package, particularly the wastewater dataset. The CFA wastewater team created this dataset, which contains simulated data. ```{python} #| label: setup import polars as pl from pyrenew.datasets import load_wastewater -from pyrenew.model import HospitalizationsModel ``` ## Model definition -In this section we provide the formal definition of the model. The hospitalization model is a semi-mechanistic model that describes the number of observed hospitalizations as a function of a set of latent variables. Particularly, the observed number of hospitalizations is discretily distributed with location at the number of latent hospitalizations: +In this section, we provide the formal definition of the model. The hospitalization model is a semi-mechanistic model that describes the number of observed hospital admissions as a function of a set of latent variables. Mainly, the observed number of hospital admissions is discretely distributed with location at the number of latent hospital admissions: $$ h(t) \sim \text{HospDist}\left(H(t)\right) $$ -Where $h(t)$ is the observed number of hospitalizations at time $t$, and $H(t)$ is the number of latent hospitalizations at time $t$. The distribution $\text{HospDist}$ can be any discrete distribution, but in this case we will use a negative binomial distribution. +Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ can be any discrete distribution, but we will use a negative binomial distribution. -The number of latent hospitalizations at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: +The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: $$ I(t) = R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) @@ -51,14 +50,12 @@ dat = load_wastewater() dat.head(5) ``` -The data shows one entry per site. We will take the aggregated data for all sites by date. +The data shows one entry per site, but the way it was simulated, the number of admissions is the same across sites. Thus we will only keep the first observation per day. ```{python} #| label: aggregation -dat = dat.group_by("date").agg( - hospitalizations = pl.sum("daily_hosp_admits"), - population = pl.sum("pop"), -) +# Keeping the first observation of each date +dat = dat.group_by("date").first().select(["date", "daily_hosp_admits"]) # Now, sorting by date dat = dat.sort("date") @@ -69,19 +66,19 @@ dat = dat.head(90) dat.head(5) ``` -Let's take a look at the daily prevalence of hospitalizations. +Let's take a look at the daily prevalence of hospital admissions. ```{python} -#| label: plot-hospitalizations +#| label: plot-hospital admissions import matplotlib.pyplot as plt # Rotating the x-axis labels, and only showing ~10 labels ax = plt.gca() ax.xaxis.set_major_locator(plt.MaxNLocator(nbins=10)) ax.xaxis.set_tick_params(rotation=45) -plt.plot(dat["date"].to_numpy(), dat["hospitalizations"].to_numpy()) +plt.plot(dat["date"].to_numpy(), dat["daily_hosp_admits"].to_numpy()) plt.xlabel("Date") -plt.ylabel("Hospitalizations") +plt.ylabel("Admissions") plt.show() ``` @@ -115,7 +112,7 @@ axs[1].plot(inf_hosp_int) axs[1].set_title("Infection to hospitalization interval") ``` -With these two in hand, we can start building the model. First, we will define the latent hospitalizations: +With these two in hand, we can start building the model. First, we will define the latent hospital admissions: ```{python} #| label: latent-hosp @@ -136,7 +133,7 @@ latent_hosp = HospitalAdmissions( ) ``` -The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate as input. The `HospitalAdmissions` class is a `RandomVariable` that takes two distributions as inputs: the infection to admission interval and the infection to hospitalization rate. Now we can define the rest of the other model components: +The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate as input. The `HospitalAdmissions` class is a `RandomVariable` that takes two distributions as inputs: the infection to admission interval and the infection to hospitalization rate. Now, we can define the rest of the other components: ```{python} #| label: initializing-rest-of-model @@ -171,7 +168,7 @@ model = HospitalizationsModel( ) ``` -Let's run a simulation to check if the model is working: +Let's simulate to check if the model is working: ```{python} #| label: simulation @@ -194,12 +191,13 @@ import matplotlib.pyplot as plt fig, axs = plt.subplots(1, 2) # Rt plot -axs[0].plot(range(0, timeframe + 1), sim_data[0]) +axs[0].plot(range(0, timeframe + 1), sim_data.Rt) axs[0].set_ylabel('Rt') # Infections plot -axs[1].plot(range(0, timeframe + 1), sim_data[1]) +axs[1].plot(range(0, timeframe + 1), sim_data.sampled_admissions) axs[1].set_ylabel('Infections') +axs[1].set_yscale('log') fig.suptitle('Basic renewal model') fig.supxlabel('Time') @@ -209,7 +207,7 @@ plt.show() ## Fitting the model -Now we can fit the model to the data. We will use the `run` method of the model object. The two inputs this model requires are `n_timepoints` and `observed_hospitalizations` +We can fit the model to the data. We will use the `run` method of the model object. The two inputs this model requires are `n_timepoints` and `observed_hospitalizations` ```{python} @@ -220,7 +218,7 @@ model.run( num_samples=2000, num_warmup=2000, n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["hospitalizations"].to_numpy(), + observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), ) @@ -228,29 +226,14 @@ model.run( ```{python} -#| label: output-hospitalizations -#| fig-cap: Hospitalizations posterior distribution +#| label: output-hospital admissions +#| fig-cap: Hospital Admissions posterior distribution #| fig-cap-location: bottom -import polars as pl -samps = model.spread_draws([('predicted_hospitalizations', 'time')]) - -fig, ax = plt.subplots(figsize=[4, 5]) - -ax.plot(dat["hospitalizations"].to_numpy(), color="black") -samp_ids = np.random.randint(size=25, low=0, high=999) -for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) - -# Some labels -ax.set_xlabel("Time") -ax.set_ylabel("Hospitalizations") - -# Adding a legend -ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") -ax.plot([], [], color="black", label="Observed hospitalizations") -ax.legend() +model.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat["daily_hosp_admits"].to_numpy(), +) ``` The first half of the model is not looking good. We can try to improve the model by incorporating weekday effects. The following section will show how to do this. @@ -323,7 +306,7 @@ model_weekday.run( num_samples=2000, num_warmup=2000, n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["hospitalizations"].to_numpy(), + observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), ) @@ -332,27 +315,12 @@ model_weekday.run( And plotting the results: ```{python} -#| label: output-hospitalizations-weekday -#| fig-cap: Hospitalizations posterior distribution +#| label: output-hospital admissions-weekday +#| fig-cap: Hospital Admissions posterior distribution #| fig-cap-location: bottom -import polars as pl -samps = model_weekday.spread_draws([('predicted_hospitalizations', 'time')]) - -fig, ax = plt.subplots(figsize=[4, 5]) - -ax.plot(dat["hospitalizations"].to_numpy(), color="black") -samp_ids = np.random.randint(size=25, low=0, high=999) -for samp_id in samp_ids: - sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time')) - ax.plot(sub_samps.select("time").to_numpy(), - sub_samps.select("predicted_hospitalizations").to_numpy(), color="darkblue", alpha=0.1) - -# Some labels -ax.set_xlabel("Time") -ax.set_ylabel("Hospitalizations") - -# Adding a legend -ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") -ax.plot([], [], color="black", label="Observed hospitalizations") -ax.legend() +model_weekday.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat["daily_hosp_admits"].to_numpy(), +) ``` diff --git a/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png b/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png index bac3e02c38ce4252c87c13e642486cfff5448a60..4bcaa452401dfde1f9d938bc8f85e9e997445fc6 100644 GIT binary patch literal 47013 zcmdSBXH-*b*Df5T_dw`U1JV^xkRmmNCLKYFN)_oylMW&TX#&!e62MTT3y26vlNv;% 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z#9zpHWZG}%ChVU?2yJV!&ecf96OK=aLTRhWWZy(W<&`aU~G6n@C1{8wqgXia^9ygfG<`F9ifFfex zA6k@vRWBvH0xaOiAyoj-r4|orAdD~qaI1ND00g26=Qkocak3y`70OVxt$>XFi1~SQ zR156J;pOE5laL~c_b#_5WE2NPp+0b?G9#u|#YHoOgaaGC)5OEKW+z9#5oI4B;gI5um{xQa&{B$kwOKM| zCr|7~YK!d<5H=w^jc)KPB1R&Bld*tX6<%(D5laRBoT4!(dyxX7hXs`S$e;haa`pe@ c$%s0|$1g6HY+6}LK*1l?8=8ux@}?pG3*`WAOaK4? diff --git a/model/docs/pyrenew_demo.md b/model/docs/pyrenew_demo.md index 40e7f1c4..e1b20273 100644 --- a/model/docs/pyrenew_demo.md +++ b/model/docs/pyrenew_demo.md @@ -11,20 +11,6 @@ install `pyrenew`: pip install pyrenew ``` - Requirement already satisfied: pyrenew in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (0.1.0) - Requirement already satisfied: jax<0.5.0,>=0.4.24 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from pyrenew) (0.4.26) - Requirement already satisfied: numpy<2.0.0,>=1.26.4 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from pyrenew) (1.26.4) - Requirement already satisfied: numpyro<0.14.0,>=0.13.2 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from pyrenew) (0.13.2) - Requirement already satisfied: pillow<11.0.0,>=10.3.0 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from pyrenew) (10.3.0) - Requirement already satisfied: polars<0.21.0,>=0.20.13 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from pyrenew) (0.20.19) - Requirement already satisfied: ml-dtypes>=0.2.0 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from jax<0.5.0,>=0.4.24->pyrenew) (0.4.0) - Requirement already satisfied: opt-einsum in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from jax<0.5.0,>=0.4.24->pyrenew) (3.3.0) - Requirement already satisfied: scipy>=1.9 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from jax<0.5.0,>=0.4.24->pyrenew) (1.13.0) - Requirement already satisfied: jaxlib>=0.4.14 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from numpyro<0.14.0,>=0.13.2->pyrenew) (0.4.26) - Requirement already satisfied: multipledispatch in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from numpyro<0.14.0,>=0.13.2->pyrenew) (1.0.0) - Requirement already satisfied: tqdm in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from numpyro<0.14.0,>=0.13.2->pyrenew) (4.66.2) - Note: you may need to restart the kernel to use updated packages. - You’ll also need working installations of `matplotlib`, `numpy`, `jax`, `numpyro`, and `polars`: @@ -32,28 +18,6 @@ You’ll also need working installations of `matplotlib`, `numpy`, `jax`, pip install matplotlib numpy jax numpyro polars ``` - Requirement already satisfied: matplotlib in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (3.8.3) - Requirement already satisfied: numpy in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (1.26.4) - Requirement already satisfied: jax in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (0.4.26) - Requirement already satisfied: numpyro in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (0.13.2) - Requirement already satisfied: polars in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (0.20.19) - Requirement already satisfied: contourpy>=1.0.1 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (1.2.1) - Requirement already satisfied: cycler>=0.10 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (0.12.1) - Requirement already satisfied: fonttools>=4.22.0 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (4.50.0) - Requirement already satisfied: kiwisolver>=1.3.1 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (1.4.5) - Requirement already satisfied: packaging>=20.0 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (24.0) - Requirement already satisfied: pillow>=8 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (10.3.0) - Requirement already satisfied: pyparsing>=2.3.1 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (3.1.2) - Requirement already satisfied: python-dateutil>=2.7 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from matplotlib) (2.9.0.post0) - Requirement already satisfied: ml-dtypes>=0.2.0 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from jax) (0.4.0) - Requirement already satisfied: opt-einsum in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from jax) (3.3.0) - Requirement already satisfied: scipy>=1.9 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from jax) (1.13.0) - Requirement already satisfied: jaxlib>=0.4.14 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from numpyro) (0.4.26) - Requirement already satisfied: multipledispatch in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from numpyro) (1.0.0) - Requirement already satisfied: tqdm in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from numpyro) (4.66.2) - Requirement already satisfied: six>=1.5 in /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0) - Note: you may need to restart the kernel to use updated packages. - To begin, run the following import section to call external modules and functions necessary to run the `pyrenew` demo. The `import` statement imports the module and the `as` statement renames the module for use @@ -75,8 +39,6 @@ import numpyro.distributions as dist from pyrenew.process import SimpleRandomWalkProcess ``` - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - To understand the simple random walk process underlying the sampling within the renewal process model, we first examine a single random walk path. Using the `sample` method from an instance of the @@ -213,19 +175,19 @@ x 1.2466507, 1.2800207, 1.2749145, 1.2619376, 1.2189837, 1.2192641, 1.2290158, 1.2128737, 1.1908046, 1.2174997, 1.1941082, 1.2084603, 1.1965215, 1.2248698, 1.2308019, 1.2426206, 1.2131014, 1.207159 , - 1.1837622], dtype=float32), infections=Array([0.05214045, 0.06867922, 0.08761451, 0.11476436, 0.09757317, + 1.1837622], dtype=float32), latent_infections=Array([0.05214045, 0.06867922, 0.08761451, 0.11476436, 0.09757317, 0.10547114, 0.1167062 , 0.13010225, 0.13824694, 0.14372033, 0.15924728, 0.17601486, 0.19236736, 0.21483542, 0.23664482, 0.25865382, 0.27503362, 0.30029488, 0.3289544 , 0.35262382, 0.37418258, 0.41274938, 0.43839005, 0.47672123, 0.50913286, 0.5625195 , 0.6113282 , 0.67092246, 0.7138808 , 0.77217466, - 0.819254 ], dtype=float32), IHR=Array(0.04929917, dtype=float32), latent=Array([0. , 0. , 0. , 0. , 0. , + 0.819254 ], dtype=float32), IHR=Array(0.04929917, dtype=float32), latent_admissions=Array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.00064262, 0.0021317 , 0.00302979, 0.00416974, 0.0049305 , 0.00487205, 0.00530097, 0.00576412, 0.00624666, 0.00665578, 0.00711595, 0.0078055 , 0.00854396, 0.00939666, 0.01042083, 0.0114624 , 0.01246538, - 0.01345188], dtype=float32), sampled=Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0.01345188], dtype=float32), sampled_admissions=Array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], dtype=int32)) Visualizations of the single model output show (top) infections over the @@ -234,10 +196,10 @@ Visualizations of the single model output show (top) infections over the ``` python fig, ax = plt.subplots(nrows=3, sharex=True) -ax[0].plot(x.infections) +ax[0].plot(x.latent_infections) ax[0].set_ylim([1/5, 5]) -ax[1].plot(x.latent) -ax[2].plot(x.sampled, 'o') +ax[1].plot(x.latent_admissions) +ax[2].plot(x.sampled_admissions, 'o') for axis in ax[:-1]: axis.set_yscale("log") ``` @@ -248,7 +210,7 @@ To fit the `hospmodel` to the simulated data, we call `hospmodel.run()`, an MCMC algorithm, with the arguments generated in `hospmodel` object, using 1000 warmup stepts and 1000 samples to draw from the posterior distribution of the model parameters. The model is run for -`len(x.sampled)-1` time steps with the seed set by +`len(x.sampled_admissions)-1` time steps with the seed set by `jax.random.PRNGKey()` ``` python @@ -256,8 +218,8 @@ distribution of the model parameters. The model is run for hospmodel.run( num_warmup=1000, num_samples=1000, - observed_hospitalizations=x.sampled, - n_timepoints = len(x.sampled)-1, + observed_hospitalizations=x.sampled_admissions, + n_timepoints = len(x.sampled_admissions)-1, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), ) diff --git a/model/docs/pyrenew_demo.qmd b/model/docs/pyrenew_demo.qmd index d0cc205f..d045a1c0 100644 --- a/model/docs/pyrenew_demo.qmd +++ b/model/docs/pyrenew_demo.qmd @@ -8,14 +8,14 @@ This demo simulates some basic renewal process data and then fits to it using `p Assuming you've already installed Python and pip, you’ll need to first install `pyrenew`: -```{python} +```python pip install pyrenew ``` You’ll also need working installations of `matplotlib`, `numpy`, `jax`, `numpyro`, and `polars`: -```{python} +```python pip install matplotlib numpy jax numpyro polars ``` @@ -144,23 +144,23 @@ Visualizations of the single model output show (top) infections over the 30 time #| label: fig-hosp #| fig-cap: Infections fig, ax = plt.subplots(nrows=3, sharex=True) -ax[0].plot(x.infections) +ax[0].plot(x.latent_infections) ax[0].set_ylim([1/5, 5]) -ax[1].plot(x.latent) -ax[2].plot(x.sampled, 'o') +ax[1].plot(x.latent_admissions) +ax[2].plot(x.sampled_admissions, 'o') for axis in ax[:-1]: axis.set_yscale("log") ``` -To fit the `hospmodel` to the simulated data, we call `hospmodel.run()`, an MCMC algorithm, with the arguments generated in `hospmodel` object, using 1000 warmup stepts and 1000 samples to draw from the posterior distribution of the model parameters. The model is run for `len(x.sampled)-1` time steps with the seed set by `jax.random.PRNGKey()` +To fit the `hospmodel` to the simulated data, we call `hospmodel.run()`, an MCMC algorithm, with the arguments generated in `hospmodel` object, using 1000 warmup stepts and 1000 samples to draw from the posterior distribution of the model parameters. The model is run for `len(x.sampled_admissions)-1` time steps with the seed set by `jax.random.PRNGKey()` ```{python} # from numpyro.infer import MCMC, NUTS hospmodel.run( num_warmup=1000, num_samples=1000, - observed_hospitalizations=x.sampled, - n_timepoints = len(x.sampled)-1, + observed_hospitalizations=x.sampled_admissions, + n_timepoints = len(x.sampled_admissions)-1, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), ) diff --git a/model/pyproject.toml b/model/pyproject.toml index 47a21228..fdc6a6a4 100755 --- a/model/pyproject.toml +++ b/model/pyproject.toml @@ -28,6 +28,7 @@ numpydoc = "^1.6.0" nbclient = "^0.10.0" nbformat = "^5.10.0" pytest-cov = "^5.0.0" +pytest-mpl = "^0.17.0" [tool.numpydoc_validation] checks = [ diff --git a/model/src/pyrenew/mcmcutils.py b/model/src/pyrenew/mcmcutils.py index 3b081958..5d6a1328 100644 --- a/model/src/pyrenew/mcmcutils.py +++ b/model/src/pyrenew/mcmcutils.py @@ -4,8 +4,10 @@ Utilities to deal with MCMC outputs """ +import matplotlib.pyplot as plt import numpy as np import polars as pl +from jax.typing import ArrayLike def spread_draws( @@ -73,3 +75,71 @@ def spread_draws( pass return df + + +def plot_posterior( + var: str, + draws: pl.DataFrame, + obs_signal: ArrayLike = None, + ylab: str = None, + xlab: str = "Time", + samples: int = 25, +) -> plt.Figure: + """ + Plot the posterior distribution of a variable + + Parameters + ---------- + var : str + Name of the variable to plot + model : Model + Model object + obs_signal : ArrayLike, optional + Observed signal to plot as reference + ylab : str, optional + Label for the y-axis + xlab : str, optional + Label for the x-axis + samples : int, optional + Number of samples to plot + + Returns + ------- + plt.Figure + """ + + if ylab is None: + ylab = var + + fig, ax = plt.subplots(figsize=[4, 5]) + + # Reference signal (if any) + if obs_signal is not None: + ax.plot(obs_signal, color="black") + + samp_ids = np.random.randint(size=samples, low=0, high=999) + + for samp_id in samp_ids: + sub_samps = draws.filter(pl.col("draw") == samp_id).sort( + pl.col("time") + ) + ax.plot( + sub_samps.select("time").to_numpy(), + sub_samps.select(var).to_numpy(), + color="darkblue", + alpha=0.1, + ) + + # Some labels + ax.set_xlabel(xlab) + ax.set_ylabel(ylab) + + # Adding a legend + ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") + + if obs_signal is not None: + ax.plot([], [], color="black", label="Observed signal") + + ax.legend() + + return fig diff --git a/model/src/pyrenew/metaclass.py b/model/src/pyrenew/metaclass.py index d00c5ace..cdbf12d3 100644 --- a/model/src/pyrenew/metaclass.py +++ b/model/src/pyrenew/metaclass.py @@ -7,9 +7,10 @@ from abc import ABCMeta, abstractmethod import jax +import matplotlib.pyplot as plt import polars as pl from numpyro.infer import MCMC, NUTS -from pyrenew.mcmcutils import spread_draws +from pyrenew.mcmcutils import plot_posterior, spread_draws def _assert_sample_and_rtype( @@ -250,3 +251,17 @@ def print_summary( def spread_draws(self, variables_names: list) -> pl.DataFrame: """A wrapper of mcmcutils.spread_draws""" return spread_draws(self.mcmc.get_samples(), variables_names) + + def plot_posterior( + self, + var: list, + ylab: str = "Signal", + obs_signal: jax.typing.ArrayLike = None, + ) -> plt.Figure: + """A wrapper of pyrenew.mcmcutils.plot_posterior""" + return plot_posterior( + var=var, + draws=self.spread_draws([(var, "time")]), + ylab=ylab, + obs_signal=obs_signal, + ) diff --git a/model/src/pyrenew/model/hospitalizations.py b/model/src/pyrenew/model/hospitalizations.py index 05559fc3..bbb84c62 100644 --- a/model/src/pyrenew/model/hospitalizations.py +++ b/model/src/pyrenew/model/hospitalizations.py @@ -11,10 +11,10 @@ "HospModelSample", [ "Rt", - "infections", + "latent_infections", "IHR", - "latent", - "sampled", + "latent_admissions", + "sampled_admissions", ], defaults=[None, None, None, None, None], ) @@ -25,13 +25,13 @@ ---------- Rt : float or None The reproduction number over time. Defaults to None. -infections : ArrayLike or None +latent_infections : ArrayLike or None The estimated number of new infections over time. Defaults to None. IHR : float or None The infected hospitalization rate. Defaults to None. -latent : ArrayLike or None +latent_admissions : ArrayLike or None The estimated latent hospitalizations. Defaults to None. -sampled : ArrayLike or None +sampled_admissions : ArrayLike or None The sampled or observed hospital admissions. Defaults to None. Notes @@ -252,8 +252,8 @@ def sample( return HospModelSample( Rt=Rt, - infections=infections, + latent_infections=infections, IHR=IHR, - latent=latent, - sampled=sampled, + latent_admissions=latent, + sampled_admissions=sampled, ) diff --git a/model/src/pyrenew/model/rtinfectionsrenewal.py b/model/src/pyrenew/model/rtinfectionsrenewal.py index 253948ff..488cea29 100644 --- a/model/src/pyrenew/model/rtinfectionsrenewal.py +++ b/model/src/pyrenew/model/rtinfectionsrenewal.py @@ -9,7 +9,7 @@ # Output class of the RtInfectionsRenewalModel RtInfectionsRenewalSample = namedtuple( "InfectModelSample", - ["Rt", "latent", "observed"], + ["Rt", "latent_infections", "sampled_infections"], defaults=[None, None, None], ) RtInfectionsRenewalSample.__doc__ = """ @@ -19,10 +19,10 @@ ---------- Rt : float or None The reproduction number over time. Defaults to None. -latent : ArrayLike or None +latent_infections : ArrayLike or None The estimated latent infections. Defaults to None. -observed : ArrayLike or None - The observed infections. Defaults to None. +sampled_infections : ArrayLike or None + The sampled infections. Defaults to None. Notes ----- @@ -292,12 +292,12 @@ def sample( ) # Using the predicted infections to sample from the observation process - observed, *_ = self.sample_infections_obs( + sampled, *_ = self.sample_infections_obs( predicted=latent, observed_infections=observed_infections, **kwargs ) return RtInfectionsRenewalSample( Rt=Rt, - latent=latent, - observed=observed, + latent_infections=latent, + sampled_infections=sampled, ) diff --git a/model/src/test/reference_images/test_model_basicrenewal_plot.png b/model/src/test/reference_images/test_model_basicrenewal_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..9414b1fe7dc031d6bd25641b1dde3173a4ef91ae GIT binary patch literal 32380 zcmd@6g;!SV7X^%NLQ13r1?dz4X{1w-5D-*Kx{8$n8>OC$spBqXIz&(_$Jc&?QH}9Jb8d zGKj+zMs_=cGnh<)V3CRl{-WUf7#lu|H_1g52$)~#{r}-dL=o4*$P^kJg6wM^2=rvncjO6^R#)tww4 zc=_a%E~&6l?9ou73(#wb2oU4wnfR~={!Pj;`|%-aYP#0E18?^z5I0nwm_`s+bjtm_ zutwh~KOuA2KdtH1^5gXgS+n7M4e$AE7eE|z24QUS9f9T&3$Xl|AuaE{C*kt zqj_h#1IF`h<~n;A+0b>LXCaetuQzYrw58~o)OXLoZ;t->qRiMy>(un+A!2K5+x~Z< zPr&u3ig)vLGd(YJ7^RU!){X1eaWusjDDUd%Q2C09Z}}m6e~Qc2dsyRUj}O+mYaUKy zE>-ee9~Opxm?(*I^YU0QJEPyeVIkw=;|&2Z3w>$B{Z|!wg8%&+zBS*p+0Xrt-}TQ9 zQt7dzo1P!Fw~4VlKHM`fWfZZaEN%Ex-zwzSstHE*nQ=cI#{lHOhXLYO8UmaZ7f*Jucj>46uvMrEPn2&leo90su9_);YoWYa{si_xU-7duP$jc^6 z?3FQ}9vV`>3j5a_;toF$bk?TGLT{jjFvF(erbJ`YKcX`bl9-#}YybP`Riu&o7o}M- zl5^+IJ!beA{K18;<>YAXqAxWL-Wjsn$zrj6yJWanIyyRHb7AM#F|sG(e3tsvc2r`q z9c6)A#Z6KS%&)`yL*ny1H$RKwAi^3Q`&&yJLpS-%7e1!wH~WgL!tTb7D_xs{gS^|x z7Z?J&O@Z`r{8S#I5Ix=>bkw@{>$%wCt0d=>qaAdIDNXS;h}zlNY2CSV=eIf85bL`z zCAE)Hm$M^rNW_pB0sntrl>4_t>_^N-N(`ulACSRLxe-Gh(#P9K{PgKl16j+1?G^nx zXXfphpddKb$tfx3aOrNPi^gG3X+^QiUCH4v;~!F~g)P~~MuV33%JS~3@=B7({ za54C=x5pg7KthX48;Bz4TnV_ixMnxJ`8}8Q(TMTD$!TD}{$?r=w zjhIU!M2@wz=;;bPS~9(^^KD8-?|JEx_j)_N%U5k>W=f)V%;sEq2T8||&L`#>_sfEm zap*tGsq|JDes`wBN4ytO^!V*Zc=)^c{k9!qs0Fa?M@rhSlS+rdNg_Pi2|9_;et826 zxm`#ZE4{7n5BmXKR(`i%>ik{hw!<7v5bx5Z{jS}JV|WS&^enRe;ruPLKVJ)JqDD)r zvTyR%F@+~{^0vrZG= zk=g(K(tD%xqJYPryXrYI^!c_HpEE@2cBcI{&^v{a%E<9kfAWnYDGK_xC6vbv+~36~#Hmw)We$92OqS^95}+343e&|Gtzj6`oYySLoxGa!zk zL-#p4T}HeYAyTm){?TiF|L0rr#@{|Z$&)O1=ZmcI{;+Mh*^UPO@DBVDk|pw341Y_V)H}r3J&bo^TQbkmcUX zIiGeJ5C?Q~jxLY>iRLct@s?>Q#rQ8v@6j&|4F_SInl^%zP6Fj_>f^{syq&8W$k;qi z(A+sORcYP-R<>SjdPF2T>5OxKAp0qe(EVV6@Zz$TORP6y+8~*P)znO>>_b$45F?i2nMdn4y9xdK7kX_4v?xse-;8)R6U&*}}E1+qtkh%>+wi=KGqeh+O*mjUUWV2qE{_IFG1)}3s>)sUIBwZ%KG zfu(&59~(?`bW%3`Z^`AoAnF{7iSmN4jkjMTvV;pPB7Lgzjx zFSgdSDY)7?`{*o!$9&1%QjmV@4df-v>Z!3x})jCyB62zzvrNysXtW~ zx3Oe;aw)jlNJm%q7tg~_&qYatzippKG~vLOtWhbE-^o{(2b3doylY9!C_{IrKvU`A z!>h39Bqhd;7Yed{xzNZ%=leTXe1^C&N$vIeVnI$G1ccKa=m&`c07#lg02-r(A85rf z+UpItOxC$jUvr>tIodW=V0NegN^f3-iSb=RC%0#dWF~BV_?qNsxxd-<49p5V~U6$U~{%Y!8+PdEq>-Sgk z&(#k81kF1Lvk(7$b(&PZ9OL-z6{ix`nJ-xngo%);RinzX3BqwL&M+7tqv(vkkV)X4 zHKy!mQ4yDs=a_ZfMi;Hyv2}gb_O>T~*Wl#jGH!kLABiWwqq3#CKR*{`rHHb^jh7F_ zTk$*!vKcjUJ6BXxv_2b3`opXBfPS_0BoN?w;hlmXrH!i}y*Fh6Qlsd|;@|HtVw<0Y z1gv_<>ZTl#wxgYy9HDwrCd-&>e%ES>%WUth(m0hKCU8b;I$G%6Gc?3POar#Q@tSrk zvdkTxz!BO?&7&UNTK*YOk&6f--p!?fIfRtl2j>b33$I{CSB&+IXVTF`AbY$KXBIOD zn62&Pc&Zdu#`Bp&ynNdqsfLb=8}kgF-_84pa|^crX*4gnO+r+uY{^~{-~Q#| zaM8#ai??nTTaksl>0q@H4c!~)Q7OoDB^oI+jr;S*uEeDI5?{+MOa85#*ijR|tCr&Z z!WxI$wXG}PEO)xLS0<`12`x6n0@4ao$TbcuJCZz@BMpkYXyTpQ9XFOV^JQ9vL*yUi z7w(TC@rm##5m<5~Blp^T&+h?NB1FV#-CgJ+2+woOe&jCu5KYC6AVDVwGg)O5H}8d; zTLm~Vk+p>po?qX?IE^Cxy_VTCxOdvKS|Wo*00sBy&1 z@pi4)?-!vci~@Yxv^{Q*R)kOIV! z8`gC5^ji6-AvzTXnUFFF9;@78Ofb{=J>i?TZ#!e@CD*^{na*W-O}9U+oeIYxl_HZH z2vTl-y~J_%8)p=vfRT&tk@H~)z?#a$S?rHs4D3O-j;@YQCqR_q;$q8_y_}QY8po*& zml>$)+Ar|vw{hIc{sb{4olVsN$}0x|y325b-aWTlVsp3`h#pgSy8s}mceU=4?n}LD zS&-#<4iA0e!}6P^|sh@-^`B5tsZoczp~W;hD>g zaDgreO=AOr+d|g>X#DbQ9Q*y{qRGdtEKs?Ad3DBzjisvE{`vRDwLq;?KS388 z@S6B&!_7{Z_OPEFC}n~k?#8J?JqOR7HUEhk-0|@tl>*VZNLCWQKRY-dL!hoBSA8v9 zH-(LxyE~KpBq$83);KL4oFconwV5M>R~B0Fi0j0|+K_oMG`Aa=`T>e?nRMv}^luKQ zBV9Hqu#1^Nfq>=>WoNK0wv4GQZhTY0UaiB}Bc&%77D@&-0UgZ(bpNosG4Hb0ykh|? 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zgf+w4$7c+!t>ZADaN^=`@ plt.Figure: + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) + + I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) + + latent_infections = Infections() + + observed_infections = PoissonObservation() + + rt = RtRandomWalkProcess() + + model1 = RtInfectionsRenewalModel( + I0=I0, + gen_int=gen_int, + latent_infections=latent_infections, + observed_infections=observed_infections, + Rt_process=rt, + ) + + # Sampling and fitting model 1 (with obs infections) + np.random.seed(2203) + with npro.handlers.seed(rng_seed=np.random.randint(1, 600)): + model1_samp = model1.sample(n_timepoints=30) + + model1.run( + num_warmup=500, + num_samples=500, + rng_key=jax.random.PRNGKey(22), + observed_infections=model1_samp.sampled_infections, + n_timepoints=30, + ) + + return model1.plot_posterior( + var="latent_infections", + ylab="latent infections", + ) diff --git a/model/src/test/test_model_hospitalizations.py b/model/src/test/test_model_hospitalizations.py index 59627954..f22ec665 100644 --- a/model/src/test/test_model_hospitalizations.py +++ b/model/src/test/test_model_hospitalizations.py @@ -98,7 +98,7 @@ def test_model_hosp_no_obs_model(): num_warmup=500, num_samples=500, rng_key=jax.random.PRNGKey(272), - obs_mean=model0_samp.sampled, + obs_mean=model0_samp.sampled_admissions, n_timepoints=30, ) @@ -177,7 +177,7 @@ def test_model_hosp_with_obs_model(): num_warmup=500, num_samples=500, rng_key=jax.random.PRNGKey(272), - observed_hospitalizations=model1_samp.sampled, + observed_hospitalizations=model1_samp.sampled_admissions, n_timepoints=30, ) @@ -267,7 +267,7 @@ def test_model_hosp_with_obs_model_weekday_phosp_2(): num_warmup=500, num_samples=500, rng_key=jax.random.PRNGKey(272), - observed_hospitalizations=model1_samp.sampled, + observed_hospitalizations=model1_samp.sampled_admissions, n_timepoints=30, ) @@ -364,7 +364,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): num_warmup=500, num_samples=500, rng_key=jax.random.PRNGKey(272), - observed_hospitalizations=model1_samp.sampled, + observed_hospitalizations=model1_samp.sampled_admissions, n_timepoints=30, ) From e73adaf6d7dedd90bc2847660ee19aa6037d6c03 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 16 Apr 2024 16:06:11 -0600 Subject: [PATCH 13/24] Updating poetry.lock --- model/poetry.lock | 38 ++++++++++++++++++++++++++++++-------- 1 file changed, 30 insertions(+), 8 deletions(-) diff --git a/model/poetry.lock b/model/poetry.lock index 395d7445..6ae2435b 100644 --- a/model/poetry.lock +++ b/model/poetry.lock @@ -1487,17 +1487,17 @@ testing = ["pytest", "pytest-benchmark"] [[package]] name = "polars" -version = "0.20.19" +version = "0.20.21" description = "Blazingly fast DataFrame library" optional = false python-versions = ">=3.8" files = [ - {file = "polars-0.20.19-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:1d5a63001c8e47376f35b6e389e7f88e6984c721bd9468b01998807bad3c0f2f"}, - {file = "polars-0.20.19-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:6790e9e8ce0bb8dcefa4e494b5e4ee5eca01df0c7d7151dcc45a7b24aa58939b"}, - {file = "polars-0.20.19-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9243752dd2f0c9077b2caa285e4b05d3f80558ad4b8ae448dc41a0b9d5f10a5"}, - {file = "polars-0.20.19-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:a0d57955cdc3610ec644ffd249c94c3102ce32df31661b759ab33a6bb4fae65c"}, - {file = "polars-0.20.19-cp38-abi3-win_amd64.whl", hash = "sha256:20ff4990cbdc05fc5a526902561a16c66ead4d740ff8e7b3bf52d4f3f7def60a"}, - {file = "polars-0.20.19.tar.gz", hash = "sha256:58f9c3766c6fa3d7193364cfcf93c6c640e6c8e6a49ad8c6fd48b326ad8060f2"}, + {file = "polars-0.20.21-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:f1de9b7190111b0d6c3731157da26252697ba27f2eef201d01e121c6873e713e"}, + {file = "polars-0.20.21-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:18847d198cbdb133c35847d096f0b72783c9cc81df0878959dba60495a99a1c7"}, + {file = "polars-0.20.21-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:32d5e7f5a1bf529ca0a3c14416a8802e746534c4220dc8af42b39ea4962e729a"}, + {file = "polars-0.20.21-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:f8196b42791ca6d1de9448e985f0d32d783166a43928bd294486fc37e1c78310"}, + {file = "polars-0.20.21-cp38-abi3-win_amd64.whl", hash = "sha256:584d3fe8d14516de2d733b9a355ae27a1be943bb2d54e76826887a0e3e122ee3"}, + {file = "polars-0.20.21.tar.gz", hash = "sha256:1ef8c44dc1a0dd79f3c1a6f73677cddac6e5aca3093cc9fb666f2e098f9b3bef"}, ] [package.extras] @@ -1671,6 +1671,28 @@ pytest = ">=4.6" [package.extras] testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"] +[[package]] +name = "pytest-mpl" +version = "0.17.0" +description = "pytest plugin to help with testing figures output from Matplotlib" +optional = false +python-versions = ">=3.6" +files = [ + {file = "pytest-mpl-0.17.0.tar.gz", hash = "sha256:fbef05d7e664b4b33452fb698ac188e522791f327de34e7ea37dbdfe9d52cac6"}, + {file = "pytest_mpl-0.17.0-py3-none-any.whl", hash = "sha256:2ee7cf902ef071e45ae14fe190b3d8a9839c2fd8933e0514bf58df310d8ed66b"}, +] + +[package.dependencies] +Jinja2 = "*" +matplotlib = "*" +packaging = "*" +Pillow = "*" +pytest = "*" + +[package.extras] +docs = ["matplotlib (==3.6)", "mpl-sphinx-theme (>=3.6.0.dev0)", "sphinx", "sphinx-design"] +test = ["pytest-cov"] + [[package]] name = "python-dateutil" version = "2.9.0.post0" @@ -2345,4 +2367,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "efdc5cb1ee45f3e7239a90ec4210574bff65fcd21449b3020b918e8763ea81b3" +content-hash = "3fb2c4d09caa9055098619152c9a6f4727fd6a5ae25750e7d1040afba9d6b31e" From eff32122066a8fb8166885e0c738fb3bec2af7aa Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Tue, 16 Apr 2024 16:11:02 -0600 Subject: [PATCH 14/24] Reference image for testing in the correct location --- model/Makefile | 2 +- .../test_model_basicrenewal_plot.png | Bin 2 files changed, 1 insertion(+), 1 deletion(-) rename model/src/test/{reference_images => baseline}/test_model_basicrenewal_plot.png (100%) diff --git a/model/Makefile b/model/Makefile index 35064f32..29aac2c1 100644 --- a/model/Makefile +++ b/model/Makefile @@ -29,7 +29,7 @@ docs/notebooks: test_images: echo "Generating reference images for tests" - poetry run pytest --mpl-generate-path=src/test/reference_image + poetry run pytest --mpl-generate-path=src/test/baseline clean: rm -rf docs/*_files/ diff --git a/model/src/test/reference_images/test_model_basicrenewal_plot.png b/model/src/test/baseline/test_model_basicrenewal_plot.png similarity index 100% rename from model/src/test/reference_images/test_model_basicrenewal_plot.png rename to model/src/test/baseline/test_model_basicrenewal_plot.png From 9e5e4b4f5470a604f7dc028d265d84200079bfb5 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 17 Apr 2024 13:25:17 -0600 Subject: [PATCH 15/24] Adding padding for models --- .github/workflows/test_model.yml | 3 +- model/Makefile | 2 +- model/docs/example-with-datasets.md | 77 ++++++++++++++---- model/docs/example-with-datasets.qmd | 62 +++++++++++--- .../figure-commonmark/basic-fig-output-1.png | Bin 47013 -> 46652 bytes ...dmissions-padding-and-weekday-output-1.png | Bin 0 -> 67792 bytes ...utput-admissions-with-padding-output-1.png | Bin 0 -> 71416 bytes .../output-hospital-admissions-output-1.png | Bin 69266 -> 70247 bytes ...t-hospital-admissions-weekday-output-1.png | Bin 66437 -> 0 bytes model/docs/pyrenew_demo.md | 2 + model/src/pyrenew/model/hospitalizations.py | 36 ++++++-- .../src/pyrenew/model/rtinfectionsrenewal.py | 28 ++++++- .../pyrenew/observation/negativebinomial.py | 11 ++- model/src/pyrenew/observation/poisson.py | 11 ++- 14 files changed, 191 insertions(+), 41 deletions(-) create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-admissions-padding-and-weekday-output-1.png create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-admissions-with-padding-output-1.png delete mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-hospital-admissions-weekday-output-1.png diff --git a/.github/workflows/test_model.yml b/.github/workflows/test_model.yml index 0408c767..395ced2e 100644 --- a/.github/workflows/test_model.yml +++ b/.github/workflows/test_model.yml @@ -29,7 +29,8 @@ jobs: - name: run tests run: | poetry run -C model pytest \ - --mpl --cov=pyrenew --cov-report term --cov-report xml model + --mpl --mpl-default-tolerance=10 \ + --cov=pyrenew --cov-report term --cov-report xml model - name: Upload coverage to Codecov uses: codecov/codecov-action@v4 diff --git a/model/Makefile b/model/Makefile index 29aac2c1..89dd88b6 100644 --- a/model/Makefile +++ b/model/Makefile @@ -2,7 +2,7 @@ install: poetry install test: - poetry run pytest --mpl + poetry run pytest --mpl --mpl-default-tolerance=10 docs: docs/pyrenew_demo.md docs/getting-started.md \ docs/example-with-datasets.md diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index 4fa2c328..56082447 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -26,8 +26,8 @@ $$ Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The -distribution $\text{HospDist}$ can be any discrete distribution, but we -will use a negative binomial distribution. +distribution $\text{HospDist}$ is discrete. For this example, we will +use a negative binomial distribution. The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to @@ -135,8 +135,8 @@ id="plot-hospital-admissions" /> ## Building the model -First, we will extract two datasets that we will use as deterministic -quantities, the generation interval and the infection to hospitalization +First, we will extract two datasets we will use as deterministic +quantities: the generation interval and the infection to hospitalization interval. ``` python @@ -191,6 +191,10 @@ latent_hosp = HospitalAdmissions( ) ``` + /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html + from .autonotebook import tqdm as notebook_tqdm + An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. + The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate @@ -211,7 +215,9 @@ I0 = Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=0.5)) # Generation interval and Rt gen_int = DeterministicPMF(gen_int) -process = RtRandomWalkProcess() +process = RtRandomWalkProcess( + Rt_rw_dist=dist.Normal(0, 0.1) +) # The observation model obs = NegativeBinomialObservation(concentration_prior=1.0) @@ -286,8 +292,10 @@ model.run( ) ``` +We can use the `plot_posterior` method to visualize the results[^1]: + ``` python -model.plot_posterior( +out = model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat["daily_hosp_admits"].to_numpy(), @@ -297,9 +305,44 @@ model.plot_posterior( ![Hospital Admissions posterior distribution](example-with-datasets_files/figure-commonmark/output-hospital-admissions-output-1.png) -The first half of the model is not looking good. We can try to improve -the model by incorporating weekday effects. The following section will -show how to do this. +The first half of the model is not looking good. The reason is that the +infection to hospitalization interval PMF makes it unlikely to observe +admissions from the beginning. To solve this, we can use the padding +argument to add a few days of missing data at the beginning of the +model. The following code will add 14 days of missing data at the +beginning of the model: + +``` python +days_to_inpute = 14 + +dat2 = dat["daily_hosp_admits"].to_numpy() + +# Add 14 Nas to the beginning of dat2 +dat2 = np.hstack((np.repeat(np.nan, days_to_inpute), dat2)) + +model.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat2.shape[0] - 1, + observed_hospitalizations=dat2, + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + padding=days_to_inpute, +) +``` + +And plotting the results: + +``` python +out = model.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat2, +) +``` + +![Hospital Admissions posterior +distribution](example-with-datasets_files/figure-commonmark/output-admissions-with-padding-output-1.png) ## Round 2: Incorporating weekday effects @@ -371,28 +414,32 @@ model_weekday = HospitalizationsModel( ) ``` -Running the model: +Running the model (with the same padding as before): ``` python model_weekday.run( num_samples=2000, num_warmup=2000, - n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), + n_timepoints=dat2.shape[0] - 1, + observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), + padding=days_to_inpute, ) ``` And plotting the results: ``` python -model_weekday.plot_posterior( +out = model_weekday.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", - obs_signal=dat["daily_hosp_admits"].to_numpy(), + obs_signal=dat2, ) ``` ![Hospital Admissions posterior -distribution](example-with-datasets_files/figure-commonmark/output-hospital-admissions-weekday-output-1.png) +distribution](example-with-datasets_files/figure-commonmark/output-admissions-padding-and-weekday-output-1.png) + +[^1]: The output is captured to avoid `quarto` from displaying the + output twice. diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 22943802..926a0c21 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -20,7 +20,7 @@ $$ h(t) \sim \text{HospDist}\left(H(t)\right) $$ -Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ can be any discrete distribution, but we will use a negative binomial distribution. +Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ is discrete. For this example, we will use a negative binomial distribution. The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: @@ -84,7 +84,7 @@ plt.show() ## Building the model -First, we will extract two datasets that we will use as deterministic quantities, the generation interval and the infection to hospitalization interval. +First, we will extract two datasets we will use as deterministic quantities: the generation interval and the infection to hospitalization interval. ```{python} #| label: data-extract @@ -148,7 +148,9 @@ I0 = Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=0.5)) # Generation interval and Rt gen_int = DeterministicPMF(gen_int) -process = RtRandomWalkProcess() +process = RtRandomWalkProcess( + Rt_rw_dist=dist.Normal(0, 0.1) +) # The observation model obs = NegativeBinomialObservation(concentration_prior=1.0) @@ -224,19 +226,55 @@ model.run( ) ``` +We can use the `plot_posterior` method to visualize the results[^capture]: + +[^capture]: The output is captured to avoid `quarto` from displaying the output twice. ```{python} #| label: output-hospital admissions #| fig-cap: Hospital Admissions posterior distribution #| fig-cap-location: bottom -model.plot_posterior( +out = model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat["daily_hosp_admits"].to_numpy(), ) ``` -The first half of the model is not looking good. We can try to improve the model by incorporating weekday effects. The following section will show how to do this. +The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe admissions from the beginning. To solve this, we can use the padding argument to add a few days of missing data at the beginning of the model. The following code will add 14 days of missing data at the beginning of the model: + +```{python} +#| label: model-fit-padding +days_to_inpute = 14 + +dat2 = dat["daily_hosp_admits"].to_numpy() + +# Add 14 Nas to the beginning of dat2 +dat2 = np.hstack((np.repeat(np.nan, days_to_inpute), dat2)) + +model.run( + num_samples=2000, + num_warmup=2000, + n_timepoints=dat2.shape[0] - 1, + observed_hospitalizations=dat2, + rng_key=jax.random.PRNGKey(54), + mcmc_args=dict(progress_bar=False), + padding=days_to_inpute, +) +``` + +And plotting the results: + +```{python} +#| label: output-admissions-with-padding +#| fig-cap: Hospital Admissions posterior distribution +#| fig-cap-location: bottom +out = model.plot_posterior( + var="predicted_hospitalizations", + ylab="Hospital Admissions", + obs_signal=dat2, +) +``` ## Round 2: Incorporating weekday effects @@ -298,29 +336,31 @@ model_weekday = HospitalizationsModel( ) ``` -Running the model: +Running the model (with the same padding as before): ```{python} #| label: model-2-run model_weekday.run( num_samples=2000, num_warmup=2000, - n_timepoints=dat.shape[0] - 1, - observed_hospitalizations=dat["daily_hosp_admits"].to_numpy(), + n_timepoints=dat2.shape[0] - 1, + observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), + padding=days_to_inpute, ) ``` And plotting the results: ```{python} -#| label: output-hospital admissions-weekday +#| label: output-admissions-padding-and-weekday #| fig-cap: Hospital Admissions posterior distribution #| fig-cap-location: bottom -model_weekday.plot_posterior( +#| +out = model_weekday.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", - obs_signal=dat["daily_hosp_admits"].to_numpy(), + obs_signal=dat2, ) ``` diff --git a/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png b/model/docs/example-with-datasets_files/figure-commonmark/basic-fig-output-1.png index 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Falling back to cpu. + To understand the simple random walk process underlying the sampling within the renewal process model, we first examine a single random walk path. Using the `sample` method from an instance of the diff --git a/model/src/pyrenew/model/hospitalizations.py b/model/src/pyrenew/model/hospitalizations.py index bbb84c62..b370385e 100644 --- a/model/src/pyrenew/model/hospitalizations.py +++ b/model/src/pyrenew/model/hospitalizations.py @@ -2,6 +2,7 @@ from collections import namedtuple +import jax.numpy as jnp from numpy.typing import ArrayLike from pyrenew.deterministic import DeterministicVariable from pyrenew.metaclass import Model, RandomVariable, _assert_sample_and_rtype @@ -160,6 +161,7 @@ def sample_hospitalizations_obs( self, predicted: ArrayLike, observed_hospitalizations: ArrayLike, + name: str | None = None, **kwargs, ) -> tuple: """Sample number of hospitalizations @@ -170,6 +172,8 @@ def sample_hospitalizations_obs( The predicted hospitalizations. observed_hospitalizations : ArrayLike The observed hospitalization data (to fit). + name : str, optional + Name of the random variable. Defaults to None. **kwargs : dict, optional Additional keyword arguments passed through to internal sample_hospitalizations_obs calls, should there be any. @@ -188,13 +192,17 @@ def sample_hospitalizations_obs( """ return self.observed_hospitalizations.sample( - predicted=predicted, obs=observed_hospitalizations, **kwargs + predicted=predicted, + obs=observed_hospitalizations, + name=name, + **kwargs, ) def sample( self, n_timepoints: int, observed_hospitalizations: ArrayLike | None = None, + padding: int = 0, **kwargs, ) -> HospModelSample: """ @@ -207,6 +215,9 @@ def sample( observed_hospitalizations : ArrayLike, optional The observed hospitalization data (passed to the basic renewal model). Defaults to None (simulation, rather than fit). + padding : int, optional + Number of padding timepoints to add to the beginning of the + simulation. Defaults to 0. **kwargs : dict, optional Additional keyword arguments passed through to internal sample() calls, should there be any. @@ -230,6 +241,7 @@ def sample( Rt, infections, *_ = self.basic_renewal.sample( n_timepoints=n_timepoints, observed_infections=None, + padding=padding, **kwargs, ) @@ -244,11 +256,23 @@ def sample( ) # Sampling the hospitalizations - sampled, *_ = self.sample_hospitalizations_obs( - predicted=latent, - observed_hospitalizations=observed_hospitalizations, - **kwargs, - ) + if (observed_hospitalizations is not None) and (padding > 0): + sampled_imputed = jnp.repeat(jnp.nan, padding) + + sampled_observed, *_ = self.sample_hospitalizations_obs( + predicted=latent[padding:], + observed_hospitalizations=observed_hospitalizations[padding:], + **kwargs, + ) + + sampled = jnp.hstack([sampled_imputed, sampled_observed]) + + else: + sampled, *_ = self.sample_hospitalizations_obs( + predicted=latent, + observed_hospitalizations=observed_hospitalizations, + **kwargs, + ) return HospModelSample( Rt=Rt, diff --git a/model/src/pyrenew/model/rtinfectionsrenewal.py b/model/src/pyrenew/model/rtinfectionsrenewal.py index 488cea29..d9a7a46d 100644 --- a/model/src/pyrenew/model/rtinfectionsrenewal.py +++ b/model/src/pyrenew/model/rtinfectionsrenewal.py @@ -3,6 +3,7 @@ from collections import namedtuple from typing import Optional +import jax.numpy as jnp from numpy.typing import ArrayLike from pyrenew.metaclass import Model, RandomVariable, _assert_sample_and_rtype @@ -211,6 +212,7 @@ def sample_infections_obs( self, predicted: ArrayLike, observed_infections: Optional[ArrayLike] = None, + name: str | None = None, **kwargs, ) -> tuple: """Sample number of hospitalizations @@ -221,6 +223,8 @@ def sample_infections_obs( The predicted infecteds. observed_hospitalizations : ArrayLike, optional The observed values of hospital admissions, if any, for inference. Defaults to None. + name : str, optional + Name of the random variable passed to the RandomVariable. **kwargs : dict, optional Additional keyword arguments passed through to internal sample() calls, should there be any. @@ -240,6 +244,7 @@ def sample_infections_obs( return self.observed_infections.sample( predicted=predicted, obs=observed_infections, + name=name, **kwargs, ) @@ -247,6 +252,7 @@ def sample( self, n_timepoints: int, observed_infections: Optional[ArrayLike] = None, + padding: int = 0, **kwargs, ) -> RtInfectionsRenewalSample: """Sample from the Basic Renewal Model @@ -257,6 +263,9 @@ def sample( Number of timepoints to sample. observed_infections : ArrayLike, optional Observed infections. + padding : int, optional + Number of padding timepoints to add to the beginning of the + simulation. Defaults to 0. **kwargs : dict, optional Additional keyword arguments passed through to internal sample() calls, if any @@ -292,9 +301,22 @@ def sample( ) # Using the predicted infections to sample from the observation process - sampled, *_ = self.sample_infections_obs( - predicted=latent, observed_infections=observed_infections, **kwargs - ) + if (observed_infections is not None) and (padding > 0): + sampled_pad = jnp.zeros(padding) + + sampled_obs, *_ = self.sample_infections_obs( + predicted=latent[padding:], + observed_infections=observed_infections[padding], + **kwargs, + ) + + sampled = jnp.concatenate([sampled_pad, sampled_obs]) + else: + sampled, *_ = self.sample_infections_obs( + predicted=latent, + observed_infections=observed_infections, + **kwargs, + ) return RtInfectionsRenewalSample( Rt=Rt, diff --git a/model/src/pyrenew/observation/negativebinomial.py b/model/src/pyrenew/observation/negativebinomial.py index 4c167699..f03538c8 100644 --- a/model/src/pyrenew/observation/negativebinomial.py +++ b/model/src/pyrenew/observation/negativebinomial.py @@ -68,6 +68,7 @@ def __init__( def sample( self, predicted: ArrayLike, + name: str | None = None, obs: ArrayLike | None = None, **kwargs, ) -> tuple: @@ -79,6 +80,9 @@ def sample( Mean parameter of the negative binomial distribution. obs : ArrayLike, optional Observed data, by default None. + name : str, optional + Name of the random variable if other than that defined during + construction, by default None (self.parameter_name). **kwargs : dict, optional Additional keyword arguments passed through to internal sample calls, should there be any. @@ -88,10 +92,13 @@ def sample( """ concentration = self.sample_prior() + if name is None: + name = self.parameter_name + return ( numpyro.sample( - self.parameter_name, - dist.NegativeBinomial2( + name=name, + fn=dist.NegativeBinomial2( mean=predicted + self.eps, concentration=concentration, ), diff --git a/model/src/pyrenew/observation/poisson.py b/model/src/pyrenew/observation/poisson.py index 0600199c..5feaf0e8 100644 --- a/model/src/pyrenew/observation/poisson.py +++ b/model/src/pyrenew/observation/poisson.py @@ -45,6 +45,7 @@ def sample( self, predicted: ArrayLike, obs: ArrayLike | None = None, + name: str | None = None, **kwargs, ) -> tuple: """Sample from the Poisson process @@ -55,6 +56,8 @@ def sample( Rate parameter of the Poisson distribution. obs : ArrayLike, optional Observed data. Defaults to None. + name : str, optional + Name of the random variable. Defaults to None. **kwargs : dict, optional Additional keyword arguments passed through to internal sample calls, should there be any. @@ -62,10 +65,14 @@ def sample( ------- tuple """ + + if name is None: + name = self.parameter_name + return ( numpyro.sample( - self.parameter_name, - dist.Poisson(rate=predicted + self.eps), + name=name, + fn=dist.Poisson(rate=predicted + self.eps), obs=obs, ), ) From 1d6d2e1d29fcc7cdf43fd4891d85f5e5e78183c9 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Wed, 17 Apr 2024 14:04:50 -0600 Subject: [PATCH 16/24] Adding padding test --- .../src/pyrenew/model/rtinfectionsrenewal.py | 5 +- .../baseline/test_model_basicrenewal_plot.png | Bin 32380 -> 42682 bytes model/src/test/test_model_basic_renewal.py | 52 +++++++++++++++++- model/src/test/test_model_hospitalizations.py | 8 ++- 4 files changed, 61 insertions(+), 4 deletions(-) diff --git a/model/src/pyrenew/model/rtinfectionsrenewal.py b/model/src/pyrenew/model/rtinfectionsrenewal.py index d9a7a46d..98b65f19 100644 --- a/model/src/pyrenew/model/rtinfectionsrenewal.py +++ b/model/src/pyrenew/model/rtinfectionsrenewal.py @@ -302,7 +302,7 @@ def sample( # Using the predicted infections to sample from the observation process if (observed_infections is not None) and (padding > 0): - sampled_pad = jnp.zeros(padding) + sampled_pad = jnp.repeat(jnp.nan, padding) sampled_obs, *_ = self.sample_infections_obs( predicted=latent[padding:], @@ -310,7 +310,8 @@ def sample( **kwargs, ) - sampled = jnp.concatenate([sampled_pad, sampled_obs]) + sampled = jnp.hstack([sampled_pad, sampled_obs]) + else: sampled, *_ = self.sample_infections_obs( predicted=latent, diff --git a/model/src/test/baseline/test_model_basicrenewal_plot.png b/model/src/test/baseline/test_model_basicrenewal_plot.png index 9414b1fe7dc031d6bd25641b1dde3173a4ef91ae..7dc6f305000fa136c779412dbf9a1db9368a0370 100644 GIT binary patch literal 42682 zcmd?Rg;!PU*FQ=qpi)u-l8S($2vX8rN*SayNOyNA-Jx_y2nqrs-3=lo-5}lFQg<%D z?|tvRC|PFGV#2STlGZ1kon9}TZ0~HvyGOOxMqa_v zA;uS1_?7i3eYeu`3uAssNgfk(p5oWjp~~S@MWzQ9#X&C{f{2K|y}OfWM68cN#S?i@0HXGTUc1I7wh-I7oqUN#iJ>k z_TbHs443F<&&kPAt4@iEYAiPH=c!ahmS4diSLghw?(%JS5;xyoCz~t3yrSZSg<~80 zGb~!MDmxXFUiQ%2merA_1vN1`O@1ZLQ>~%5rDbI3?%xlj;&)-uYsD5xQ;_6%g+<%! zvHG3%+bu1($c^!`BBz~M&YqRw0&0d7r#nr4I8AdqEQZ|_R4&W3oE8&51usu}!c21D z_MU$t_0yr)Onj}Z%x3jRXtQ#S*`jKLlcstWt~coNe9P0-eXGFp-1Oq&sBUjiS&P+W zf4OP$HLVZ+=~qz~nxhF@yp@%e38%SG%(JO+i>lB%_@jrK)#Y6%FBKJA8hvl9^k?JA 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RtRandomWalkProcess() latent_infections = Infections() # (5) The observed infections process (with mean at the latent infections) -observed_infections = PoissonObservation() +observation_process = PoissonObservation() ``` With these five pieces, we can build the basic renewal model: @@ -98,7 +98,7 @@ model1 = RtInfectionsRenewalModel( I0 = I0, Rt_process = rt_proc, latent_infections = latent_infections, - observed_infections = observed_infections, + observation_process = observation_process, ) ``` @@ -114,7 +114,7 @@ flowchart TB i0["(2) I0\n(Infections0)"] rt["(3) rt_proc\n(RtRandomWalkProcess)"] inf["(4) latent_infections\n(Infections)"] - obs["(5) observed_infections\n(PoissonObservation)"] + obs["(5) observation_process\n(PoissonObservation)"] model1["model1\n(RtInfectionsRenewalModel)"] @@ -241,7 +241,7 @@ flowchart LR models((Model\nmetaclass)) subgraph observations[Observations module] - obs["observed_infections\n(PoissonObservation)"] + obs["observation_process\n(PoissonObservation)"] end subgraph latent[Latent module] diff --git a/model/docs/getting-started.qmd b/model/docs/getting-started.qmd index 33ced168..8ca96a11 100644 --- a/model/docs/getting-started.qmd +++ b/model/docs/getting-started.qmd @@ -65,7 +65,7 @@ rt_proc = RtRandomWalkProcess() latent_infections = Infections() # (5) The observed infections process (with mean at the latent infections) -observed_infections = PoissonObservation() +observation_process = PoissonObservation() ``` With these five pieces, we can build the basic renewal model: @@ -77,7 +77,7 @@ model1 = RtInfectionsRenewalModel( I0 = I0, Rt_process = rt_proc, latent_infections = latent_infections, - observed_infections = observed_infections, + observation_process = observation_process, ) ``` @@ -90,7 +90,7 @@ flowchart TB i0["(2) I0\n(Infections0)"] rt["(3) rt_proc\n(RtRandomWalkProcess)"] inf["(4) latent_infections\n(Infections)"] - obs["(5) observed_infections\n(PoissonObservation)"] + obs["(5) observation_process\n(PoissonObservation)"] model1["model1\n(RtInfectionsRenewalModel)"] @@ -193,7 +193,7 @@ flowchart LR models((Model\nmetaclass)) subgraph observations[Observations module] - obs["observed_infections\n(PoissonObservation)"] + obs["observation_process\n(PoissonObservation)"] end subgraph latent[Latent module] diff --git a/model/docs/pyrenew_demo.md b/model/docs/pyrenew_demo.md index 31e08f70..76eb0444 100644 --- a/model/docs/pyrenew_demo.md +++ b/model/docs/pyrenew_demo.md @@ -141,7 +141,7 @@ latent_hospitalizations = HospitalAdmissions( ) # 5) An observation process for the hospitalizations -observed_hospitalizations = PoissonObservation() +obs_process = PoissonObservation() # 6) A random walk process (it could be deterministic using # pyrenew.process.DeterministicProcess()) @@ -157,7 +157,7 @@ hospmodel = HospitalizationsModel( gen_int=gen_int, I0=I0, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=observed_hospitalizations, + observation_process=obs_process, latent_infections=latent_infections, Rt_process=Rt_process ) diff --git a/model/docs/pyrenew_demo.qmd b/model/docs/pyrenew_demo.qmd index d045a1c0..6bea20dd 100644 --- a/model/docs/pyrenew_demo.qmd +++ b/model/docs/pyrenew_demo.qmd @@ -109,7 +109,7 @@ latent_hospitalizations = HospitalAdmissions( ) # 5) An observation process for the hospitalizations -observed_hospitalizations = PoissonObservation() +obs_process = PoissonObservation() # 6) A random walk process (it could be deterministic using # pyrenew.process.DeterministicProcess()) @@ -124,7 +124,7 @@ hospmodel = HospitalizationsModel( gen_int=gen_int, I0=I0, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=observed_hospitalizations, + observation_process=obs_process, latent_infections=latent_infections, Rt_process=Rt_process ) diff --git a/model/src/pyrenew/mcmcutils.py b/model/src/pyrenew/mcmcutils.py index 5d6a1328..1a8d8eea 100644 --- a/model/src/pyrenew/mcmcutils.py +++ b/model/src/pyrenew/mcmcutils.py @@ -83,7 +83,10 @@ def plot_posterior( obs_signal: ArrayLike = None, ylab: str = None, xlab: str = "Time", - samples: int = 25, + samples: int = 50, + figsize: list = [4, 5], + draws_col: str = "darkblue", + obs_col: str = "black", ) -> plt.Figure: """ Plot the posterior distribution of a variable @@ -102,6 +105,11 @@ def plot_posterior( Label for the x-axis samples : int, optional Number of samples to plot + figsize : list, optional + Size of the figure + draws_col : str, optional + Color of the draws + obs_col : str, optional Returns ------- @@ -111,11 +119,11 @@ def plot_posterior( if ylab is None: ylab = var - fig, ax = plt.subplots(figsize=[4, 5]) + fig, ax = plt.subplots(figsize=figsize) # Reference signal (if any) if obs_signal is not None: - ax.plot(obs_signal, color="black") + ax.plot(obs_signal, color=obs_col) samp_ids = np.random.randint(size=samples, low=0, high=999) @@ -126,7 +134,7 @@ def plot_posterior( ax.plot( sub_samps.select("time").to_numpy(), sub_samps.select(var).to_numpy(), - color="darkblue", + color=draws_col, alpha=0.1, ) @@ -135,10 +143,10 @@ def plot_posterior( ax.set_ylabel(ylab) # Adding a legend - ax.plot([], [], color="darkblue", alpha=0.9, label="Posterior samples") + ax.plot([], [], color=draws_col, alpha=0.9, label="Posterior samples") if obs_signal is not None: - ax.plot([], [], color="black", label="Observed signal") + ax.plot([], [], color=obs_col, label="Observed signal") ax.legend() diff --git a/model/src/pyrenew/metaclass.py b/model/src/pyrenew/metaclass.py index cdbf12d3..7450cf0d 100644 --- a/model/src/pyrenew/metaclass.py +++ b/model/src/pyrenew/metaclass.py @@ -255,13 +255,23 @@ def spread_draws(self, variables_names: list) -> pl.DataFrame: def plot_posterior( self, var: list, - ylab: str = "Signal", obs_signal: jax.typing.ArrayLike = None, + xlab: str = None, + ylab: str = "Signal", + samples: int = 50, + figsize: list = [4, 5], + draws_col: str = "darkblue", + obs_col: str = "black", ) -> plt.Figure: """A wrapper of pyrenew.mcmcutils.plot_posterior""" return plot_posterior( var=var, draws=self.spread_draws([(var, "time")]), + xlab=xlab, ylab=ylab, + samples=samples, obs_signal=obs_signal, + figsize=figsize, + draws_col=draws_col, + obs_col=obs_col, ) diff --git a/model/src/pyrenew/model/hospitalizations.py b/model/src/pyrenew/model/hospitalizations.py index b370385e..33aa9837 100644 --- a/model/src/pyrenew/model/hospitalizations.py +++ b/model/src/pyrenew/model/hospitalizations.py @@ -4,7 +4,6 @@ import jax.numpy as jnp from numpy.typing import ArrayLike -from pyrenew.deterministic import DeterministicVariable from pyrenew.metaclass import Model, RandomVariable, _assert_sample_and_rtype from pyrenew.model.rtinfectionsrenewal import RtInfectionsRenewalModel @@ -56,7 +55,7 @@ def __init__( gen_int: RandomVariable, I0: RandomVariable, Rt_process: RandomVariable, - observed_hospitalizations: RandomVariable, + observation_process: RandomVariable, ) -> None: """ Default constructor @@ -73,7 +72,7 @@ def __init__( Initial infections (passed to RtInfectionsRenewalModel) Rt_process : RandomVariable Rt process (passed to RtInfectionsRenewalModel). - observed_hospitalizations : RandomVariable, optional + observation_process : RandomVariable, optional Observation process for the hospitalizations. Returns @@ -88,19 +87,19 @@ def __init__( gen_int=gen_int, I0=I0, latent_infections=latent_infections, - observed_infections=DeterministicVariable(0), + observation_process=None, Rt_process=Rt_process, ) HospitalizationsModel.validate( - latent_hospitalizations, observed_hospitalizations + latent_hospitalizations, observation_process ) self.latent_hospitalizations = latent_hospitalizations - self.observed_hospitalizations = observed_hospitalizations + self.observation_process = observation_process @staticmethod - def validate(latent_hospitalizations, observed_hospitalizations) -> None: + def validate(latent_hospitalizations, observation_process) -> None: """ Verifies types and status (RV) of latent and observed hospitalizations @@ -108,7 +107,7 @@ def validate(latent_hospitalizations, observed_hospitalizations) -> None: ---------- latent_hospitalizations : ArrayLike The latent process for the hospitalizations. - observed_hospitalizations : ArrayLike + observation_process : ArrayLike The observed hospitalizations. Returns @@ -120,7 +119,7 @@ def validate(latent_hospitalizations, observed_hospitalizations) -> None: _assert_sample_and_rtype : Perform type-checking and verify RV """ _assert_sample_and_rtype(latent_hospitalizations, skip_if_none=False) - _assert_sample_and_rtype(observed_hospitalizations, skip_if_none=False) + _assert_sample_and_rtype(observation_process, skip_if_none=False) return None def sample_hospitalizations_latent( @@ -182,16 +181,12 @@ def sample_hospitalizations_obs( ------- tuple - See Also - -------- - observed_hospitalizations.sample : For sampling observed hospitalizations - Notes ----- TODO: Include example(s) here. """ - return self.observed_hospitalizations.sample( + return self.observation_process.sample( predicted=predicted, obs=observed_hospitalizations, name=name, @@ -238,7 +233,7 @@ def sample( """ # Getting the initial quantities from the basic model - Rt, infections, *_ = self.basic_renewal.sample( + basic_model = self.basic_renewal.sample( n_timepoints=n_timepoints, observed_infections=None, padding=padding, @@ -251,7 +246,7 @@ def sample( latent, *_, ) = self.sample_hospitalizations_latent( - infections=infections, + infections=basic_model.latent_infections, **kwargs, ) @@ -275,8 +270,8 @@ def sample( ) return HospModelSample( - Rt=Rt, - latent_infections=infections, + Rt=basic_model.Rt, + latent_infections=basic_model.latent_infections, IHR=IHR, latent_admissions=latent, sampled_admissions=sampled, diff --git a/model/src/pyrenew/model/rtinfectionsrenewal.py b/model/src/pyrenew/model/rtinfectionsrenewal.py index 98b65f19..154847b3 100644 --- a/model/src/pyrenew/model/rtinfectionsrenewal.py +++ b/model/src/pyrenew/model/rtinfectionsrenewal.py @@ -43,7 +43,7 @@ def __init__( gen_int: RandomVariable, I0: RandomVariable, Rt_process: RandomVariable, - observed_infections: RandomVariable, + observation_process: RandomVariable, ) -> None: """Default constructor @@ -59,7 +59,7 @@ def __init__( Rt_process : RandomVariable The sample function of the process should return a tuple where the first element is the drawn Rt. - observed_infections : RandomVariable + observation_process : RandomVariable Infections observation process (e.g., pyrenew.observations.Poisson.). @@ -72,14 +72,14 @@ def __init__( gen_int=gen_int, i0=I0, latent_infections=latent_infections, - observed_infections=observed_infections, + observation_process=observation_process, Rt_process=Rt_process, ) self.gen_int = gen_int self.i0 = I0 self.latent_infections = latent_infections - self.observed_infections = observed_infections + self.observation_process = observation_process self.Rt_process = Rt_process @staticmethod @@ -87,7 +87,7 @@ def validate( gen_int, i0, latent_infections, - observed_infections, + observation_process, Rt_process, ) -> None: """ @@ -112,7 +112,7 @@ def validate( _assert_sample_and_rtype(gen_int, skip_if_none=False) _assert_sample_and_rtype(i0, skip_if_none=False) _assert_sample_and_rtype(latent_infections, skip_if_none=False) - _assert_sample_and_rtype(observed_infections, skip_if_none=True) + _assert_sample_and_rtype(observation_process, skip_if_none=True) _assert_sample_and_rtype(Rt_process, skip_if_none=False) return None @@ -233,15 +233,11 @@ def sample_infections_obs( ------- tuple - See Also - -------- - observed_infections.sample : For sampling observed infections - Notes ----- TODO: Include example(s) here. """ - return self.observed_infections.sample( + return self.observation_process.sample( predicted=predicted, obs=observed_infections, name=name, @@ -301,23 +297,26 @@ def sample( ) # Using the predicted infections to sample from the observation process - if (observed_infections is not None) and (padding > 0): - sampled_pad = jnp.repeat(jnp.nan, padding) - - sampled_obs, *_ = self.sample_infections_obs( - predicted=latent[padding:], - observed_infections=observed_infections[padding], - **kwargs, - ) - - sampled = jnp.hstack([sampled_pad, sampled_obs]) - + if self.observation_process is not None: + if (observed_infections is not None) and (padding > 0): + sampled_pad = jnp.repeat(jnp.nan, padding) + + sampled_obs, *_ = self.sample_infections_obs( + predicted=latent[padding:], + observed_infections=observed_infections[padding:], + **kwargs, + ) + + sampled = jnp.hstack([sampled_pad, sampled_obs]) + + else: + sampled, *_ = self.sample_infections_obs( + predicted=latent, + 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zgf+w4$7c+!t>ZADaN^=`@ plt.Figure: return model1.plot_posterior( var="latent_infections", - ylab="latent infections", + obs_signal=model1_samp.sampled_infections, ) + + +def test_model_basicrenewal_padding() -> None: + gen_int = DeterministicPMF(jnp.array([0.25, 0.25, 0.25, 0.25])) + + I0 = Infections0(I0_dist=dist.LogNormal(0, 1)) + + latent_infections = Infections() + + observed_infections = PoissonObservation() + + rt = RtRandomWalkProcess() + + model1 = RtInfectionsRenewalModel( + I0=I0, + gen_int=gen_int, + latent_infections=latent_infections, + observed_infections=observed_infections, + Rt_process=rt, + ) + + # Sampling and fitting model 1 (with obs infections) + np.random.seed(2203) + with npro.handlers.seed(rng_seed=np.random.randint(1, 600)): + model1_samp = model1.sample(n_timepoints=30) + + new_obs = jnp.hstack( + [jnp.repeat(jnp.nan, 5), model1_samp.sampled_infections[5:]], + ) + + model1.run( + num_warmup=500, + num_samples=500, + rng_key=jax.random.PRNGKey(22), + observed_infections=new_obs, + n_timepoints=35, + padding=5, + ) + + inf = model1.spread_draws(["latent_infections"]) + + inf_mean = ( + inf.group_by("draw") + .agg(pl.col("latent_infections").mean()) + .sort(pl.col("draw")) + ) + + # For now the assertion is only about the expected number of rows + # It should be about the MCMC inference. + assert inf_mean.to_numpy().shape[0] == 500 diff --git a/model/src/test/test_model_hospitalizations.py b/model/src/test/test_model_hospitalizations.py index f22ec665..cac95858 100644 --- a/model/src/test/test_model_hospitalizations.py +++ b/model/src/test/test_model_hospitalizations.py @@ -360,12 +360,18 @@ def test_model_hosp_with_obs_model_weekday_phosp(): with npro.handlers.seed(rng_seed=np.random.randint(1, 600)): model1_samp = model1.sample(n_timepoints=30) + obs = jnp.hstack( + [jnp.repeat(jnp.nan, 5), model1_samp.sampled_admissions[5:]] + ) + + # Running with padding model1.run( num_warmup=500, num_samples=500, rng_key=jax.random.PRNGKey(272), - observed_hospitalizations=model1_samp.sampled_admissions, + observed_hospitalizations=obs, n_timepoints=30, + padding=5, ) inf = model1.spread_draws(["predicted_hospitalizations"]) From 4a943fb7c53e2c6767f40c911aaf9226aacaa074 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 18 Apr 2024 13:47:03 -0600 Subject: [PATCH 17/24] Renaming obs proc in models and working on data demo --- model/docs/example-with-datasets.md | 98 ++++++++--------- model/docs/example-with-datasets.qmd | 100 ++++++++---------- .../figure-commonmark/basic-fig-output-1.png | Bin 46652 -> 46416 bytes ...dmissions-padding-and-weekday-output-1.png | Bin 67792 -> 78217 bytes ...utput-admissions-with-padding-output-1.png | Bin 71416 -> 70061 bytes .../output-hospital-admissions-output-1.png | Bin 70247 -> 75472 bytes model/docs/getting-started.md | 8 +- model/docs/getting-started.qmd | 8 +- model/docs/pyrenew_demo.md | 4 +- model/docs/pyrenew_demo.qmd | 4 +- model/src/pyrenew/mcmcutils.py | 20 ++-- model/src/pyrenew/metaclass.py | 12 ++- model/src/pyrenew/model/hospitalizations.py | 31 +++--- .../src/pyrenew/model/rtinfectionsrenewal.py | 53 +++++----- .../baseline/test_model_basicrenewal_plot.png | Bin 42682 -> 48663 bytes model/src/test/test_model_basic_renewal.py | 10 +- model/src/test/test_model_hospitalizations.py | 8 +- 17 files changed, 179 insertions(+), 177 deletions(-) diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index 56082447..e91f19b8 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -6,11 +6,6 @@ fitted using data from the Pyrenew package, particularly the wastewater dataset. The CFA wastewater team created this dataset, which contains simulated data. -``` python -import polars as pl -from pyrenew.datasets import load_wastewater -``` - ## Model definition In this section, we provide the formal definition of the model. The @@ -27,7 +22,11 @@ $$ Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ is discrete. For this example, we will -use a negative binomial distribution. +use a negative binomial distribution: + +$$ +h(t) \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{logits} = \log(H(t))\right) +$$ The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to @@ -35,7 +34,10 @@ hospitalization rate. The latent infections are modeled as a renewal process: $$ -I(t) = R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) +\begin{align*} +I(t) &= R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) \\ +I(0) &\sim \text{LogNormal}(\mu = \log(80/0.05), \sigma = 1.5) +\end{align*} $$ The reproductive number $R(t)$ is modeled as a random walk process: @@ -43,7 +45,7 @@ The reproductive number $R(t)$ is modeled as a random walk process: $$ \begin{align*} R(t) & = R(t-1) + \epsilon\\ -\log{\epsilon} & \sim \text{Normal}(0, \sigma) \\ +\log{\epsilon} & \sim \text{Normal}(\mu=0, \sigma=0.1) \\ R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) \end{align*} $$ @@ -53,7 +55,10 @@ $$ We start by loading the data and inspecting the first five rows. ``` python -dat = load_wastewater() +import polars as pl +from pyrenew import datasets + +dat = datasets.load_wastewater() dat.head(5) ``` @@ -78,7 +83,7 @@ dat.head(5) The data shows one entry per site, but the way it was simulated, the -number of admissions is the same across sites. Thus we will only keep +number of admissions is the same across sites. Thus, we will only keep the first observation per day. ``` python @@ -140,13 +145,8 @@ quantities: the generation interval and the infection to hospitalization interval. ``` python -from pyrenew.datasets import ( - load_generation_interval, - load_infection_admission_interval, -) - -gen_int = load_generation_interval() -inf_hosp_int = load_infection_admission_interval() +gen_int = datasets.load_generation_interval() +inf_hosp_int = datasets.load_infection_admission_interval() # We only need the probability_mass column of each dataset gen_int = gen_int["probability_mass"].to_numpy() @@ -174,18 +174,17 @@ With these two in hand, we can start building the model. First, we will define the latent hospital admissions: ``` python -from pyrenew.latent import HospitalAdmissions, InfectHospRate -from pyrenew.deterministic import DeterministicPMF +from pyrenew import latent, deterministic import jax.numpy as jnp import numpyro.distributions as dist -inf_hosp_int = DeterministicPMF(inf_hosp_int) +inf_hosp_int = deterministic.DeterministicPMF(inf_hosp_int) -hosp_rate = InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.01) +hosp_rate = latent.InfectHospRate( + dist=dist.LogNormal(jnp.log(0.05), 0.1) ) -latent_hosp = HospitalAdmissions( +latent_hosp = latent.HospitalAdmissions( infection_to_admission_interval=inf_hosp_int, infect_hosp_rate_dist=hosp_rate, ) @@ -204,36 +203,33 @@ and the infection to hospitalization rate. Now, we can define the rest of the other components: ``` python -from pyrenew.model import HospitalizationsModel -from pyrenew.latent import Infections, Infections0 -from pyrenew.process import RtRandomWalkProcess -from pyrenew.observation import NegativeBinomialObservation +from pyrenew import model, process, observation # Infection process -latent_inf = Infections() -I0 = Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=0.5)) +latent_inf = latent.Infections() +I0 = latent.Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=1.5)) # Generation interval and Rt -gen_int = DeterministicPMF(gen_int) -process = RtRandomWalkProcess( +gen_int = deterministic.DeterministicPMF(gen_int) +rtproc = process.RtRandomWalkProcess( Rt_rw_dist=dist.Normal(0, 0.1) ) # The observation model -obs = NegativeBinomialObservation(concentration_prior=1.0) +obs = observation.NegativeBinomialObservation(concentration_prior=1.0) ``` Notice all the components are `RandomVariable` instances. We can now build the model: ``` python -model = HospitalizationsModel( +hosp_model = model.HospitalizationsModel( latent_infections=latent_inf, latent_hospitalizations=latent_hosp, I0=I0, gen_int=gen_int, - Rt_process=process, - observed_hospitalizations=obs, + Rt_process=rtproc, + observation_process=obs, ) ``` @@ -247,7 +243,7 @@ timeframe = 120 np.random.seed(223) with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): - sim_data = model.sample(n_timepoints=timeframe) + sim_data = hosp_model.sample(n_timepoints=timeframe) ``` ``` python @@ -282,7 +278,7 @@ model object. The two inputs this model requires are `n_timepoints` and ``` python import jax -model.run( +hosp_model.run( num_samples=2000, num_warmup=2000, n_timepoints=dat.shape[0] - 1, @@ -295,7 +291,7 @@ model.run( We can use the `plot_posterior` method to visualize the results[^1]: ``` python -out = model.plot_posterior( +out = hosp_model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat["daily_hosp_admits"].to_numpy(), @@ -309,18 +305,18 @@ The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe admissions from the beginning. To solve this, we can use the padding argument to add a few days of missing data at the beginning of the -model. The following code will add 14 days of missing data at the +model. The following code will add 21 days of missing data at the beginning of the model: ``` python -days_to_inpute = 14 +days_to_inpute = 21 dat2 = dat["daily_hosp_admits"].to_numpy() -# Add 14 Nas to the beginning of dat2 +# Add 21 Nas to the beginning of dat2 dat2 = np.hstack((np.repeat(np.nan, days_to_inpute), dat2)) -model.run( +hosp_model.run( num_samples=2000, num_warmup=2000, n_timepoints=dat2.shape[0] - 1, @@ -334,7 +330,7 @@ model.run( And plotting the results: ``` python -out = model.plot_posterior( +out = hosp_model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat2, @@ -356,10 +352,10 @@ deviation of 0.5. The distribution will be truncated between 0.1 and dataset. ``` python -from pyrenew.metaclass import RandomVariable +from pyrenew import metaclass import numpyro as npro -class WeekdayEffect(RandomVariable): +class WeekdayEffect(metaclass.RandomVariable): """Weekday effect distribution""" def __init__(self, len: int): """ Initialize the weekday effect distribution @@ -398,26 +394,26 @@ step, which happens before the model is run. With the new weekday effect, we can rebuild the latent hospitalization model: ``` python -latent_hosp_wday_effect = HospitalAdmissions( +latent_hosp_wday_effect = latent.HospitalAdmissions( infection_to_admission_interval=inf_hosp_int, infect_hosp_rate_dist=hosp_rate, weekday_effect_dist=weekday_effect, ) -model_weekday = HospitalizationsModel( +hosp_model_weekday = model.HospitalizationsModel( latent_infections=latent_inf, latent_hospitalizations=latent_hosp_wday_effect, I0=I0, gen_int=gen_int, - Rt_process=process, - observed_hospitalizations=obs, + Rt_process=rtproc, + observation_process=obs, ) ``` Running the model (with the same padding as before): ``` python -model_weekday.run( +hosp_model_weekday.run( num_samples=2000, num_warmup=2000, n_timepoints=dat2.shape[0] - 1, @@ -431,7 +427,7 @@ model_weekday.run( And plotting the results: ``` python -out = model_weekday.plot_posterior( +out = hosp_model_weekday.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat2, diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 926a0c21..f7ce4127 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -6,12 +6,6 @@ engine: jupyter This document illustrates how a hospital admissions-only model can be fitted using data from the Pyrenew package, particularly the wastewater dataset. The CFA wastewater team created this dataset, which contains simulated data. -```{python} -#| label: setup -import polars as pl -from pyrenew.datasets import load_wastewater -``` - ## Model definition In this section, we provide the formal definition of the model. The hospitalization model is a semi-mechanistic model that describes the number of observed hospital admissions as a function of a set of latent variables. Mainly, the observed number of hospital admissions is discretely distributed with location at the number of latent hospital admissions: @@ -20,12 +14,19 @@ $$ h(t) \sim \text{HospDist}\left(H(t)\right) $$ -Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ is discrete. For this example, we will use a negative binomial distribution. +Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ is discrete. For this example, we will use a negative binomial distribution: + +$$ +h(t) \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{logits} = \log(H(t))\right) +$$ The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: $$ -I(t) = R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) +\begin{align*} +I(t) &= R(t) \times \sum_{\tau < t} I(\tau) g(t - \tau) \\ +I(0) &\sim \text{LogNormal}(\mu = \log(80/0.05), \sigma = 1.5) +\end{align*} $$ The reproductive number $R(t)$ is modeled as a random walk process: @@ -33,24 +34,26 @@ The reproductive number $R(t)$ is modeled as a random walk process: $$ \begin{align*} R(t) & = R(t-1) + \epsilon\\ -\log{\epsilon} & \sim \text{Normal}(0, \sigma) \\ +\log{\epsilon} & \sim \text{Normal}(\mu=0, \sigma=0.1) \\ R(0) &\sim \text{TruncatedNormal}(\text{loc}=1.2, \text{scale}=0.2, \text{min}=0) \end{align*} $$ - ## Data processing We start by loading the data and inspecting the first five rows. ```{python} #| label: data-inspect -dat = load_wastewater() +import polars as pl +from pyrenew import datasets + +dat = datasets.load_wastewater() dat.head(5) ``` -The data shows one entry per site, but the way it was simulated, the number of admissions is the same across sites. Thus we will only keep the first observation per day. +The data shows one entry per site, but the way it was simulated, the number of admissions is the same across sites. Thus, we will only keep the first observation per day. ```{python} #| label: aggregation @@ -88,13 +91,8 @@ First, we will extract two datasets we will use as deterministic quantities: the ```{python} #| label: data-extract -from pyrenew.datasets import ( - load_generation_interval, - load_infection_admission_interval, -) - -gen_int = load_generation_interval() -inf_hosp_int = load_infection_admission_interval() +gen_int = datasets.load_generation_interval() +inf_hosp_int = datasets.load_infection_admission_interval() # We only need the probability_mass column of each dataset gen_int = gen_int["probability_mass"].to_numpy() @@ -116,18 +114,17 @@ With these two in hand, we can start building the model. First, we will define t ```{python} #| label: latent-hosp -from pyrenew.latent import HospitalAdmissions, InfectHospRate -from pyrenew.deterministic import DeterministicPMF +from pyrenew import latent, deterministic import jax.numpy as jnp import numpyro.distributions as dist -inf_hosp_int = DeterministicPMF(inf_hosp_int) +inf_hosp_int = deterministic.DeterministicPMF(inf_hosp_int) -hosp_rate = InfectHospRate( - dist=dist.LogNormal(jnp.log(0.05), 0.01) +hosp_rate = latent.InfectHospRate( + dist=dist.LogNormal(jnp.log(0.05), 0.1) ) -latent_hosp = HospitalAdmissions( +latent_hosp = latent.HospitalAdmissions( infection_to_admission_interval=inf_hosp_int, infect_hosp_rate_dist=hosp_rate, ) @@ -137,36 +134,33 @@ The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to ho ```{python} #| label: initializing-rest-of-model -from pyrenew.model import HospitalizationsModel -from pyrenew.latent import Infections, Infections0 -from pyrenew.process import RtRandomWalkProcess -from pyrenew.observation import NegativeBinomialObservation +from pyrenew import model, process, observation # Infection process -latent_inf = Infections() -I0 = Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=0.5)) +latent_inf = latent.Infections() +I0 = latent.Infections0(I0_dist=dist.LogNormal(loc=jnp.log(80/.05), scale=1.5)) # Generation interval and Rt -gen_int = DeterministicPMF(gen_int) -process = RtRandomWalkProcess( +gen_int = deterministic.DeterministicPMF(gen_int) +rtproc = process.RtRandomWalkProcess( Rt_rw_dist=dist.Normal(0, 0.1) ) # The observation model -obs = NegativeBinomialObservation(concentration_prior=1.0) +obs = observation.NegativeBinomialObservation(concentration_prior=1.0) ``` Notice all the components are `RandomVariable` instances. We can now build the model: ```{python} #| label: init-model -model = HospitalizationsModel( +hosp_model = model.HospitalizationsModel( latent_infections=latent_inf, latent_hospitalizations=latent_hosp, I0=I0, gen_int=gen_int, - Rt_process=process, - observed_hospitalizations=obs, + Rt_process=rtproc, + observation_process=obs, ) ``` @@ -181,7 +175,7 @@ timeframe = 120 np.random.seed(223) with npro.handlers.seed(rng_seed = np.random.randint(1, timeframe)): - sim_data = model.sample(n_timepoints=timeframe) + sim_data = hosp_model.sample(n_timepoints=timeframe) ``` ```{python} @@ -216,7 +210,7 @@ We can fit the model to the data. We will use the `run` method of the model obje #| label: model-fit import jax -model.run( +hosp_model.run( num_samples=2000, num_warmup=2000, n_timepoints=dat.shape[0] - 1, @@ -234,25 +228,25 @@ We can use the `plot_posterior` method to visualize the results[^capture]: #| label: output-hospital admissions #| fig-cap: Hospital Admissions posterior distribution #| fig-cap-location: bottom -out = model.plot_posterior( +out = hosp_model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat["daily_hosp_admits"].to_numpy(), ) ``` -The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe admissions from the beginning. To solve this, we can use the padding argument to add a few days of missing data at the beginning of the model. The following code will add 14 days of missing data at the beginning of the model: +The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe admissions from the beginning. To solve this, we can use the padding argument to add a few days of missing data at the beginning of the model. The following code will add 21 days of missing data at the beginning of the model: ```{python} #| label: model-fit-padding -days_to_inpute = 14 +days_to_inpute = 21 dat2 = dat["daily_hosp_admits"].to_numpy() -# Add 14 Nas to the beginning of dat2 +# Add 21 Nas to the beginning of dat2 dat2 = np.hstack((np.repeat(np.nan, days_to_inpute), dat2)) -model.run( +hosp_model.run( num_samples=2000, num_warmup=2000, n_timepoints=dat2.shape[0] - 1, @@ -269,7 +263,7 @@ And plotting the results: #| label: output-admissions-with-padding #| fig-cap: Hospital Admissions posterior distribution #| fig-cap-location: bottom -out = model.plot_posterior( +out = hosp_model.plot_posterior( var="predicted_hospitalizations", ylab="Hospital Admissions", obs_signal=dat2, @@ -282,10 +276,10 @@ We will re-use the infection to admission interval and infection to hospitalizat ```{python} #| label: weekly-effect -from pyrenew.metaclass import RandomVariable +from pyrenew import metaclass import numpyro as npro -class WeekdayEffect(RandomVariable): +class WeekdayEffect(metaclass.RandomVariable): """Weekday effect distribution""" def __init__(self, len: int): """ Initialize the weekday effect distribution @@ -320,19 +314,19 @@ Notice that the instance's `nweeks` and `len` members are passed during construc ```{python} #| label: latent-hosp-weekday -latent_hosp_wday_effect = HospitalAdmissions( +latent_hosp_wday_effect = latent.HospitalAdmissions( infection_to_admission_interval=inf_hosp_int, infect_hosp_rate_dist=hosp_rate, weekday_effect_dist=weekday_effect, ) -model_weekday = HospitalizationsModel( +hosp_model_weekday = model.HospitalizationsModel( latent_infections=latent_inf, latent_hospitalizations=latent_hosp_wday_effect, I0=I0, gen_int=gen_int, - Rt_process=process, - observed_hospitalizations=obs, + Rt_process=rtproc, + observation_process=obs, ) ``` @@ -340,7 +334,7 @@ Running the model (with the same padding as before): ```{python} #| label: model-2-run -model_weekday.run( 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z^c6g2Qc!EsYItnN>DbXn-vKI@?#fCOV7YxiUaiI#~Z*-X+~b3X?$ z{6K03bli`y%T)kT_>nttx1D&Ae>IVN;H4cp6dqgaLnu;ZQ8G5^ntiPDX#unX8>p8E zV>Yg)2P;C1`>TBEI~$LoN-|B0df1Y9tc<{Fvkm*7?MT3+p3ZIgzs#mQv$!7KlVM zrKf1eh>fhv?+o;Z>gwtm-0tu@gSiWS%%nHOpe^hbgfeHOOG`@}NT$1>)C4PlCUAkb zc8ev5oYs&ACGbaIk?=3)e%yweewK#CYnH))jSBJ%v}MHY?4eF plt.Figure: I0=I0, gen_int=gen_int, latent_infections=latent_infections, - observed_infections=observed_infections, + observation_process=observed_infections, Rt_process=rt, ) @@ -165,7 +165,7 @@ def test_model_basicrenewal_padding() -> None: I0=I0, gen_int=gen_int, latent_infections=latent_infections, - observed_infections=observed_infections, + observation_process=observed_infections, Rt_process=rt, ) @@ -183,7 +183,7 @@ def test_model_basicrenewal_padding() -> None: num_samples=500, rng_key=jax.random.PRNGKey(22), observed_infections=new_obs, - n_timepoints=35, + n_timepoints=30, padding=5, ) diff --git a/model/src/test/test_model_hospitalizations.py b/model/src/test/test_model_hospitalizations.py index cac95858..34dba6ab 100644 --- a/model/src/test/test_model_hospitalizations.py +++ b/model/src/test/test_model_hospitalizations.py @@ -86,7 +86,7 @@ def test_model_hosp_no_obs_model(): Rt_process=Rt_process, latent_infections=latent_infections, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=DeterministicVariable(0), + observation_process=DeterministicVariable(0), ) # Sampling and fitting model 0 (with no obs for infections) @@ -165,7 +165,7 @@ def test_model_hosp_with_obs_model(): Rt_process=Rt_process, latent_infections=latent_infections, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=observed_hospitalizations, + observation_process=observed_hospitalizations, ) # Sampling and fitting model 0 (with no obs for infections) @@ -255,7 +255,7 @@ def test_model_hosp_with_obs_model_weekday_phosp_2(): Rt_process=Rt_process, latent_infections=latent_infections, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=observed_hospitalizations, + observation_process=observed_hospitalizations, ) # Sampling and fitting model 0 (with no obs for infections) @@ -352,7 +352,7 @@ def test_model_hosp_with_obs_model_weekday_phosp(): Rt_process=Rt_process, latent_infections=latent_infections, latent_hospitalizations=latent_hospitalizations, - observed_hospitalizations=observed_hospitalizations, + observation_process=observed_hospitalizations, ) # Sampling and fitting model 0 (with no obs for infections) From 9a6e1b0444ba6eb17087188dd2f6e4b5b6f11aa3 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 18 Apr 2024 15:13:58 -0600 Subject: [PATCH 18/24] Adding figure of latent infections and extending padding section --- model/docs/example-with-datasets.md | 36 +++++++++++++----- model/docs/example-with-datasets.qmd | 20 +++++++++- ...dmissions-padding-and-weekday-output-1.png | Bin 78217 -> 83648 bytes ...utput-infections-with-padding-output-1.png | Bin 0 -> 57042 bytes 4 files changed, 45 insertions(+), 11 deletions(-) create mode 100644 model/docs/example-with-datasets_files/figure-commonmark/output-infections-with-padding-output-1.png diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index e91f19b8..2dc792c8 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -190,10 +190,6 @@ latent_hosp = latent.HospitalAdmissions( ) ``` - /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html - from .autonotebook import tqdm as notebook_tqdm - An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. - The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate @@ -303,10 +299,20 @@ distribution](example-with-datasets_files/figure-commonmark/output-hospital-admi The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe -admissions from the beginning. To solve this, we can use the padding -argument to add a few days of missing data at the beginning of the -model. The following code will add 21 days of missing data at the -beginning of the model: +admissions from the beginning. The following section shows how to fix +this. + +## Padding the model + +We can use the padding argument to solve the overestimation of +hospitalizations in the first half of the model. By setting +`padding > 0`, the model then assumes that the first `padding` +observations are missing; thus, only observations after `padding` will +count towards the likelihood of the model. In practice, the model will +extend the estimated Rt and latent infections by `padding` days, given +time to adjust to the observed data. The following code will add 21 days +of missing data at the beginning of the model and re-estimate it with +`padding = 21`: ``` python days_to_inpute = 21 @@ -323,7 +329,7 @@ hosp_model.run( observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), - padding=days_to_inpute, + padding=days_to_inpute, # Padding the model ) ``` @@ -340,6 +346,18 @@ out = hosp_model.plot_posterior( ![Hospital Admissions posterior distribution](example-with-datasets_files/figure-commonmark/output-admissions-with-padding-output-1.png) +We can also take a look at the latent infections: + +``` python +out2 = hosp_model.plot_posterior( + var="latent_infections", + ylab="Latent Infections" +) +``` + +![Hospital Admissions posterior +distribution](example-with-datasets_files/figure-commonmark/output-infections-with-padding-output-1.png) + ## Round 2: Incorporating weekday effects We will re-use the infection to admission interval and infection to diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index f7ce4127..414ab0c6 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -235,7 +235,11 @@ out = hosp_model.plot_posterior( ) ``` -The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe admissions from the beginning. To solve this, we can use the padding argument to add a few days of missing data at the beginning of the model. The following code will add 21 days of missing data at the beginning of the model: +The first half of the model is not looking good. The reason is that the infection to hospitalization interval PMF makes it unlikely to observe admissions from the beginning. The following section shows how to fix this. + +## Padding the model + +We can use the padding argument to solve the overestimation of hospitalizations in the first half of the model. By setting `padding > 0`, the model then assumes that the first `padding` observations are missing; thus, only observations after `padding` will count towards the likelihood of the model. In practice, the model will extend the estimated Rt and latent infections by `padding` days, given time to adjust to the observed data. The following code will add 21 days of missing data at the beginning of the model and re-estimate it with `padding = 21`: ```{python} #| label: model-fit-padding @@ -253,7 +257,7 @@ hosp_model.run( observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), - padding=days_to_inpute, + padding=days_to_inpute, # Padding the model ) ``` @@ -270,6 +274,18 @@ out = hosp_model.plot_posterior( ) ``` +We can also take a look at the latent infections: + +```{python} +#| label: output-infections-with-padding +#| fig-cap: Hospital Admissions posterior distribution +#| fig-cap-location: bottom +out2 = hosp_model.plot_posterior( + var="latent_infections", + ylab="Latent Infections" +) +``` + ## Round 2: Incorporating weekday effects We will re-use the infection to admission interval and infection to hospitalization rate from the previous model. But we will also add a weekday effect distribution. To do this, we will create a new instance of `RandomVariable` to model the weekday effect. The weekday effect will be a truncated normal distribution with a mean of 1.0 and a standard deviation of 0.5. The distribution will be truncated between 0.1 and 10.0. 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model/src/pyrenew/datasets/data-raw/wastewater.R Co-authored-by: Dylan H. Morris --- model/src/pyrenew/datasets/data-raw/wastewater.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/model/src/pyrenew/datasets/data-raw/wastewater.R b/model/src/pyrenew/datasets/data-raw/wastewater.R index 17144248..69213f65 100644 --- a/model/src/pyrenew/datasets/data-raw/wastewater.R +++ b/model/src/pyrenew/datasets/data-raw/wastewater.R @@ -1,6 +1,8 @@ load( "https://github.com/CDCgov/wastewater-informed-covid-forecasting/blob/292526383ece582f10823fc939c7e590ca349c6d/cfaforecastrenewalww/data/example_df.rda" ) +datasets_dir <- file.path("src", "pyrenew", "datasets") +dir.create(datasets_dir) # Saving as TSV write.table( From cc407c171043224d8c0611987cd5edf41d10413f Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 19 Apr 2024 13:08:20 -0600 Subject: [PATCH 20/24] Update model/src/pyrenew/datasets/data-raw/wastewater.R Co-authored-by: Dylan H. Morris --- model/src/pyrenew/datasets/data-raw/wastewater.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model/src/pyrenew/datasets/data-raw/wastewater.R b/model/src/pyrenew/datasets/data-raw/wastewater.R index 69213f65..5ff804f1 100644 --- a/model/src/pyrenew/datasets/data-raw/wastewater.R +++ b/model/src/pyrenew/datasets/data-raw/wastewater.R @@ -7,7 +7,7 @@ dir.create(datasets_dir) # Saving as TSV write.table( x = example_df, - file = "src/pyrenew/datasets/wastewater.tsv", + file = file.path(datasets_dir, "wastewater.tsv"), sep = "\t", quote = FALSE, row.names = FALSE, From cb81af51bc8d54cf776958c2f39a82bf1c0ce94b Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 19 Apr 2024 13:08:29 -0600 Subject: [PATCH 21/24] Update model/src/pyrenew/datasets/data-raw/infection_admission_interval.py Co-authored-by: Dylan H. Morris --- .../pyrenew/datasets/data-raw/infection_admission_interval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py b/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py index 0243d842..6e95383e 100644 --- a/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py +++ b/model/src/pyrenew/datasets/data-raw/infection_admission_interval.py @@ -16,7 +16,7 @@ "infection_admission_interval.tsv", ) -assert os.path.exists(os.path.dirname(path)) +os.makedirs(os.path.dirname(path), exist_ok=True) # Save as TSV infection_admission_interval.write_csv( From 4fc97eddc77589b74b97f57f084b621ce93b3119 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 19 Apr 2024 13:12:00 -0600 Subject: [PATCH 22/24] Typos, language, and more content to qmd doc --- model/docs/example-with-datasets.md | 19 +++++++--- model/docs/example-with-datasets.qmd | 15 +++++--- .../src/pyrenew/latent/hospitaladmissions.py | 2 +- model/src/pyrenew/model/hospitalizations.py | 37 +++++++++++-------- 4 files changed, 46 insertions(+), 27 deletions(-) diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index 2dc792c8..ae4d08ae 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -25,9 +25,18 @@ distribution $\text{HospDist}$ is discrete. For this example, we will use a negative binomial distribution: $$ -h(t) \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{logits} = \log(H(t))\right) +\begin{align*} +h(t) & \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{mean} = H(t)\right) \\ +H(t) & = \omega(t) p_\mathrm{hosp}(t) \sum_{\tau = 0}^{T_d} d(\tau) I(t-\tau) +\end{align*} $$ +Were $d(\tau)$ is the infection to hospitalization interval, $I(t)$ is +the number of latent infections at time $t$, $p_\mathrm{hosp}(t)$ is the +infection to hospitalization rate, and $\omega(t)$ is the weekday effect +at time $t$; the last section provides an example building a weekly +effect `RandomVariable`. + The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal @@ -315,12 +324,12 @@ of missing data at the beginning of the model and re-estimate it with `padding = 21`: ``` python -days_to_inpute = 21 +days_to_impute = 21 dat2 = dat["daily_hosp_admits"].to_numpy() # Add 21 Nas to the beginning of dat2 -dat2 = np.hstack((np.repeat(np.nan, days_to_inpute), dat2)) +dat2 = np.hstack((np.repeat(np.nan, days_to_impute), dat2)) hosp_model.run( num_samples=2000, @@ -329,7 +338,7 @@ hosp_model.run( observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), - padding=days_to_inpute, # Padding the model + padding=days_to_impute, # Padding the model ) ``` @@ -438,7 +447,7 @@ hosp_model_weekday.run( observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), - padding=days_to_inpute, + padding=days_to_impute, ) ``` diff --git a/model/docs/example-with-datasets.qmd b/model/docs/example-with-datasets.qmd index 414ab0c6..6c58a08c 100644 --- a/model/docs/example-with-datasets.qmd +++ b/model/docs/example-with-datasets.qmd @@ -17,9 +17,14 @@ $$ Where $h(t)$ is the observed number of hospital admissions at time $t$, and $H(t)$ is the number of latent hospital admissions at time $t$. The distribution $\text{HospDist}$ is discrete. For this example, we will use a negative binomial distribution: $$ -h(t) \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{logits} = \log(H(t))\right) +\begin{align*} +h(t) & \sim \text{NegativeNinomial}\left(\text{concentration} = 1, \text{mean} = H(t)\right) \\ +H(t) & = \omega(t) p_\mathrm{hosp}(t) \sum_{\tau = 0}^{T_d} d(\tau) I(t-\tau) +\end{align*} $$ +Were $d(\tau)$ is the infection to hospitalization interval, $I(t)$ is the number of latent infections at time $t$, $p_\mathrm{hosp}(t)$ is the infection to hospitalization rate, and $\omega(t)$ is the weekday effect at time $t$; the last section provides an example building a weekly effect `RandomVariable`. + The number of latent hospital admissions at time $t$ is a function of the number of latent infections at time $t$ and the infection to hospitalization rate. The latent infections are modeled as a renewal process: $$ @@ -243,12 +248,12 @@ We can use the padding argument to solve the overestimation of hospitalizations ```{python} #| label: model-fit-padding -days_to_inpute = 21 +days_to_impute = 21 dat2 = dat["daily_hosp_admits"].to_numpy() # Add 21 Nas to the beginning of dat2 -dat2 = np.hstack((np.repeat(np.nan, days_to_inpute), dat2)) +dat2 = np.hstack((np.repeat(np.nan, days_to_impute), dat2)) hosp_model.run( num_samples=2000, @@ -257,7 +262,7 @@ hosp_model.run( observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), - padding=days_to_inpute, # Padding the model + padding=days_to_impute, # Padding the model ) ``` @@ -357,7 +362,7 @@ hosp_model_weekday.run( observed_hospitalizations=dat2, rng_key=jax.random.PRNGKey(54), mcmc_args=dict(progress_bar=False), - padding=days_to_inpute, + padding=days_to_impute, ) ``` diff --git a/model/src/pyrenew/latent/hospitaladmissions.py b/model/src/pyrenew/latent/hospitaladmissions.py index ad3eb3df..e04a7b25 100644 --- a/model/src/pyrenew/latent/hospitaladmissions.py +++ b/model/src/pyrenew/latent/hospitaladmissions.py @@ -158,7 +158,7 @@ class HospitalAdmissions(RandomVariable): .. math:: - H(t) = \omega(t) p_\mathrm{hosp}(t) \sum_{\\tau = 0}^{T_d} d(\tau) I(t-\tau) + H(t) = \omega(t) p_\mathrm{hosp}(t) \sum_{\tau = 0}^{T_d} d(\tau) I(t-\tau) Where :math:`T_d` is the maximum delay from infection to hospitalization that we consider. diff --git a/model/src/pyrenew/model/hospitalizations.py b/model/src/pyrenew/model/hospitalizations.py index 33aa9837..e59535b0 100644 --- a/model/src/pyrenew/model/hospitalizations.py +++ b/model/src/pyrenew/model/hospitalizations.py @@ -251,23 +251,28 @@ def sample( ) # Sampling the hospitalizations - if (observed_hospitalizations is not None) and (padding > 0): - sampled_imputed = jnp.repeat(jnp.nan, padding) - - sampled_observed, *_ = self.sample_hospitalizations_obs( - predicted=latent[padding:], - observed_hospitalizations=observed_hospitalizations[padding:], - **kwargs, - ) - - sampled = jnp.hstack([sampled_imputed, sampled_observed]) - + if self.observation_process is not None: + if (observed_hospitalizations is not None) and (padding > 0): + sampled_na = jnp.repeat(jnp.nan, padding) + + sampled_observed, *_ = self.sample_hospitalizations_obs( + predicted=latent[padding:], + observed_hospitalizations=observed_hospitalizations[ + padding: + ], + **kwargs, + ) + + sampled = jnp.hstack([sampled_na, sampled_observed]) + + else: + sampled, *_ = self.sample_hospitalizations_obs( + predicted=latent, + observed_hospitalizations=observed_hospitalizations, + **kwargs, + ) else: - sampled, *_ = self.sample_hospitalizations_obs( - predicted=latent, - observed_hospitalizations=observed_hospitalizations, - **kwargs, - ) + sampled = None return HospModelSample( Rt=basic_model.Rt, From e6d30513fd8e76c54df3ec3b9b3354becef90185 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 19 Apr 2024 13:19:17 -0600 Subject: [PATCH 23/24] Update model/src/pyrenew/datasets/data-raw/generation_interval.py Co-authored-by: Dylan H. Morris --- model/src/pyrenew/datasets/data-raw/generation_interval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model/src/pyrenew/datasets/data-raw/generation_interval.py b/model/src/pyrenew/datasets/data-raw/generation_interval.py index 7d0d1a34..75b4ea7a 100644 --- a/model/src/pyrenew/datasets/data-raw/generation_interval.py +++ b/model/src/pyrenew/datasets/data-raw/generation_interval.py @@ -16,7 +16,7 @@ "gen_int.tsv", ) -assert os.path.exists(os.path.dirname(path)) +os.makedirs(os.path.dirname(path), exist_ok=True) gen_int.write_csv( file=path, From e72c000f1934802a9ffb0ac2fb0cc17ea88dc26a Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Mon, 22 Apr 2024 15:17:14 -0600 Subject: [PATCH 24/24] Fixing updates with tuple check --- model/docs/example-with-datasets.md | 4 ++++ model/src/pyrenew/deterministic/deterministic.py | 12 ++++++------ model/src/pyrenew/deterministic/deterministicpmf.py | 6 +++--- 3 files changed, 13 insertions(+), 9 deletions(-) diff --git a/model/docs/example-with-datasets.md b/model/docs/example-with-datasets.md index ae4d08ae..9bee20aa 100644 --- a/model/docs/example-with-datasets.md +++ b/model/docs/example-with-datasets.md @@ -199,6 +199,10 @@ latent_hosp = latent.HospitalAdmissions( ) ``` + /mnt/c/Users/xrd4/Documents/repos/msr/model/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html + from .autonotebook import tqdm as notebook_tqdm + An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu. + The `inf_hosp_int` is a `DeterministicPMF` object that takes the infection to hospitalization interval as input. The `hosp_rate` is an `InfectHospRate` object that takes the infection to hospitalization rate diff --git a/model/src/pyrenew/deterministic/deterministic.py b/model/src/pyrenew/deterministic/deterministic.py index 94a7a187..a23cf93e 100644 --- a/model/src/pyrenew/deterministic/deterministic.py +++ b/model/src/pyrenew/deterministic/deterministic.py @@ -34,14 +34,14 @@ def __init__( return None @staticmethod - def validate(vars: tuple) -> None: + def validate(vars: ArrayLike) -> None: """ Validates inputted to DeterministicPMF Parameters ---------- - vars : tuple - A tuple with arraylike objects. + vars : ArrayLike + An ArrayLike object. Returns ------- @@ -50,10 +50,10 @@ def validate(vars: tuple) -> None: Raises ------ Exception - If the inputted vars object is not a tuple. + If the inputted vars object is not a ArrayLike. """ - if not isinstance(vars, tuple): - raise Exception("vars is not a tuple") + if not isinstance(vars, ArrayLike): + raise Exception("vars is not a ArrayLike") return None diff --git a/model/src/pyrenew/deterministic/deterministicpmf.py b/model/src/pyrenew/deterministic/deterministicpmf.py index b4b0155b..1a0c9450 100644 --- a/model/src/pyrenew/deterministic/deterministicpmf.py +++ b/model/src/pyrenew/deterministic/deterministicpmf.py @@ -48,14 +48,14 @@ def __init__( return None @staticmethod - def validate(vars: tuple) -> None: + def validate(vars: ArrayLike) -> None: """ Validates inputted to DeterministicPMF Parameters ---------- - vars : tuple - A tuple with arraylike objects. + vars : ArrayLike + An ArrayLike object. Returns -------