summary_longformat.py 3.49 KB
Newer Older
Chris Jewell's avatar
Chris Jewell committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
"""Produces a long-format summary of fitted model results"""

import pickle as pkl
from datetime import date
import numpy as np
import pandas as pd
import xarray

from gemlib.util import compute_state
from covid.model_spec import STOICHIOMETRY
from covid import model_spec
from covid.formats import make_dstl_template


def xarray2summarydf(arr):
    mean = arr.mean(dim="iteration").to_dataset(name="value")
17
18
19
    q = np.arange(start=0.05, stop=1.0, step=0.05)
    quantiles = arr.quantile(q=q, dim="iteration").to_dataset(dim="quantile")
    ds = mean.merge(quantiles).rename_vars({qi: f"{qi:.2f}" for qi in q})
Chris Jewell's avatar
Chris Jewell committed
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
    return ds.to_dataframe().reset_index()


def prevalence(events, popsize):
    prev = compute_state(events.attrs["initial_state"], events, STOICHIOMETRY)
    prev = xarray.DataArray(
        prev.numpy(),
        coords=[
            np.arange(prev.shape[0]),
            events.coords["location"],
            events.coords["time"],
            np.arange(prev.shape[-1]),
        ],
        dims=["iteration", "location", "time", "state"],
    )
    prev_per_1e5 = (
        prev[..., 1:3].sum(dim="state").reset_coords(drop=True)
        / popsize[np.newaxis, :, np.newaxis]
        * 100000
    )
    return xarray2summarydf(prev_per_1e5)


def summary_longformat(input_files, output_file):
    """Draws together pipeline results into a long format
       csv file.

    :param input_files: a list of filenames [data_pkl,
                                             insample14_pkl,
                                             medium_term_pred_pkl,
                                             ngm_pkl]
    :param output_file: the output CSV with columns `[date,
                        location,value_name,value,q0.025,q0.975]`
    """

    with open(input_files[0], "rb") as f:
        data = pkl.load(f)
    da = data["cases"].rename({"date": "time"})
    df = da.to_dataframe(name="value").reset_index()
    df["value_name"] = "newCasesBySpecimenDate"
    df["0.05"] = np.nan
    df["0.5"] = np.nan
    df["0.95"] = np.nan

    # Insample predictive incidence
    with open(input_files[1], "rb") as f:
        insample = pkl.load(f)
    insample_df = xarray2summarydf(insample[..., 2].reset_coords(drop=True))
    insample_df["value_name"] = "insample14_Cases"
    df = pd.concat([df, insample_df], axis="index")

    # Medium term incidence
    with open(input_files[2], "rb") as f:
        medium_term = pkl.load(f)
    medium_df = xarray2summarydf(medium_term[..., 2].reset_coords(drop=True))
    medium_df["value_name"] = "Cases"
    df = pd.concat([df, medium_df], axis="index")

    # Medium term prevalence
    prev_df = prevalence(medium_term, data["N"])
80
    prev_df["value_name"] = "prevalence"
Chris Jewell's avatar
Chris Jewell committed
81
82
83
84
85
86
87
88
89
90
91
92
    df = pd.concat([df, prev_df], axis="index")

    # Rt
    with open(input_files[3], "rb") as f:
        ngms = pkl.load(f)
    rt = ngms.sum(dim="dest")
    rt = rt.rename({"src": "location"})
    rt_summary = xarray2summarydf(rt)
    rt_summary["value_name"] = "R"
    rt_summary["time"] = data["date_range"][1]
    df = pd.concat([df, rt_summary], axis="index")

93
94
    quantiles = df.columns[df.columns.str.startswith("0.")]

Chris Jewell's avatar
Chris Jewell committed
95
96
97
    return make_dstl_template(
        group="Lancaster",
        model="SpatialStochasticSEIR",
98
        scenario="Nowcast",
Chris Jewell's avatar
Chris Jewell committed
99
100
101
102
103
104
        creation_date=date.today(),
        version=model_spec.VERSION,
        age_band="All",
        geography=df["location"],
        value_date=df["time"],
        value_type=df["value_name"],
105
        quantiles={q: df[q] for q in quantiles},
106
    ).to_excel(output_file, index=False)