Source code for tlo.analysis.utils

General utility functions for TLO analysis
import json
import os
import pickle
from import Mapping
from pathlib import Path
from typing import Dict, Optional, TextIO

import numpy as np
import pandas as pd

from tlo import logging, util
from tlo.logging.reader import LogData
from tlo.util import create_age_range_lookup

logger = logging.getLogger(__name__)

def _parse_log_file_inner_loop(filepath, level: int = logging.INFO):
    """Parses the log file and returns dictionary of dataframes"""
    log_data = LogData()
    with open(filepath) as log_file:
        for line in log_file:
            # only parse json entities
            if line.startswith('{'):
                log_data.parse_log_line(line, level)
                print('FAILURE: found old-style log:')
                raise RuntimeError
    # convert dictionaries to dataframes
    output_logs = {**log_data.get_log_dataframes()}
    return output_logs

[docs]def parse_log_file(log_filepath): """Parses logged output from a TLO run, split it into smaller logfiles and returns a class containing paths to these split logfiles. :param log_filepath: file path to log file :return: a class containing paths to split logfiles """ print(f'Processing log file {log_filepath}') uuid_to_module_name: Dict[str, str] = dict() # uuid to module name module_name_to_filehandle: Dict[str, TextIO] = dict() # module name to file handle log_directory = Path(log_filepath).parent print(f'Writing module-specific log files to {log_directory}') # iterate over each line in the logfile with open(log_filepath) as log_file: for line in log_file: # only parse lines that are json log lines (old-style logging is not supported) if line.startswith('{'): log_data_json = json.loads(line) uuid = log_data_json['uuid'] # if this is a header line (only header lines have a `type` key) if 'type' in log_data_json: module_name = log_data_json["module"] uuid_to_module_name[uuid] = module_name # we only need to create the file if we don't already have one for this module if module_name not in module_name_to_filehandle: module_name_to_filehandle[module_name] = open(log_directory / f"{module_name}.log", mode="w") # copy line from log file to module-specific log file (both headers and non-header lines) module_name_to_filehandle[uuid_to_module_name[uuid]].write(line) print('Finished writing module-specific log files.') # close all module-specific files for file_handle in module_name_to_filehandle.values(): file_handle.close() # return an object that accepts as an argument a dictionary containing paths to split logfiles return LogsDict({name: for name, handle in module_name_to_filehandle.items()})
[docs]def write_log_to_excel(filename, log_dataframes): """Takes the output of parse_log_file() and creates an Excel file from dataframes""" metadata = list() sheet_count = 0 for module, dataframes in log_dataframes.items(): for key, dataframe in dataframes.items(): if key != '_metadata': sheet_count += 1 metadata.append([module, key, sheet_count, dataframes['_metadata'][module][key]['description']]) writer = pd.ExcelWriter(filename) index = pd.DataFrame(data=metadata, columns=['module', 'key', 'sheet', 'description']) index.to_excel(writer, sheet_name='Index') sheet_count = 0 for module, dataframes in log_dataframes.items(): for key, df in dataframes.items(): if key != '_metadata': sheet_count += 1 df.to_excel(writer, sheet_name=f'Sheet {sheet_count}')
[docs]def make_calendar_period_lookup(): """Returns a dictionary mapping calendar year (in years) to five year period i.e. { 1950: '1950-1954', 1951: '1950-1954, ...} """ # Recycles the code used to make age-range lookups: ranges, lookup = util.create_age_range_lookup(1950, 2100, 5) # Removes the '0-1950' category ranges.remove('0-1950') for year in range(1950): lookup.pop(year) return ranges, lookup
[docs]def make_calendar_period_type(): """ Make an ordered categorical type for calendar periods Returns CategoricalDType """ keys, _ = make_calendar_period_lookup() return pd.CategoricalDtype(categories=keys, ordered=True)
[docs]def make_age_grp_lookup(): """Returns a dictionary mapping age (in years) to five year period i.e. { 0: '0-4', 1: '0-4', ..., 119: '100+', 120: '100+' } """ return create_age_range_lookup(min_age=0, max_age=100, range_size=5)
[docs]def make_age_grp_types(): """ Make an ordered categorical type for age-groups Returns CategoricalDType """ keys, _ = create_age_range_lookup(min_age=0, max_age=100, range_size=5) return pd.CategoricalDtype(categories=keys, ordered=True)
[docs]def get_scenario_outputs(scenario_filename: str, outputs_dir: Path) -> list: """Returns paths of folders associated with a batch_file, in chronological order.""" stub = scenario_filename.rstrip('.py') f: os.DirEntry folders = [Path(f.path) for f in os.scandir(outputs_dir) if f.is_dir() and] folders.sort() return folders
[docs]def get_scenario_info(scenario_output_dir: Path) -> dict: """Utility function to get the the number draws and the number of runs in a batch set. TODO: read the JSON file to get further information """ info = dict() f: os.DirEntry draw_folders = [f for f in os.scandir(scenario_output_dir) if f.is_dir()] info['number_of_draws'] = len(draw_folders) run_folders = [f for f in os.scandir(draw_folders[0]) if f.is_dir()] info['runs_per_draw'] = len(run_folders) return info
[docs]def load_pickled_dataframes(results_folder: Path, draw=0, run=0, name=None) -> dict: """Utility function to create a dict contaning all the logs from the specified run within a batch set""" folder = results_folder / str(draw) / str(run) p: os.DirEntry pickles = [p for p in os.scandir(folder) if'.pickle')] if name is not None: pickles = [p for p in pickles if in f"{name}.pickle"] output = dict() for p in pickles: name = os.path.splitext([0] with open(p.path, "rb") as f: output[name] = pickle.load(f) return output
[docs]def extract_params(results_folder: Path) -> Optional[pd.DataFrame]: """Utility function to get overridden parameters from scenario runs Returns dateframe summarizing parameters that change across the draws. It produces a dataframe with index of draw and columns of each parameters that is specified to be varied in the batch. NB. This does the extraction from run 0 in each draw, under the assumption that the over-written parameters are the same in each run. """ try: f: os.DirEntry # Get the paths for the draws draws = [f for f in os.scandir(results_folder) if f.is_dir()] list_of_param_changes = list() for d in draws: p = load_pickled_dataframes(results_folder,, 0, name="tlo.scenario") p = p["tlo.scenario"]["override_parameter"] p['module_param'] = p['module'] + ':' + p['name'] p.index = [int(] * len(p.index) list_of_param_changes.append(p[['module_param', 'new_value']]) params = pd.concat(list_of_param_changes) = 'draw' params = params.rename(columns={'new_value': 'value'}) params = params.sort_index() return params except KeyError: print("No parameters changed between the runs") return None
[docs]def extract_results(results_folder: Path, module: str, key: str, column: str = None, index: str = None, custom_generate_series=None, do_scaling: bool = False, ) -> pd.DataFrame: """Utility function to unpack results Produces a dataframe that summaries one series from the log, with column multi-index for the draw/run. If an 'index' component of the log_element is provided, the dataframe uses that index (but note that this will only work if the index is the same in each run). Optionally, instead of a series that exists in the dataframe already, a function can be provided that, when applied to the dataframe indicated, yields a new pd.Series. Optionally, with `do_scaling`, each element is multiplied by the the scaling_factor recorded in the simulation (if available) """ # get number of draws and numbers of runs info = get_scenario_info(results_folder) cols = pd.MultiIndex.from_product( [range(info['number_of_draws']), range(info['runs_per_draw'])], names=["draw", "run"] ) def get_multiplier(_draw, _run): """Helper function to get the multiplier from the simulation, if it's specified and do_scaling=True""" if not do_scaling: return 1.0 else: try: return load_pickled_dataframes(results_folder, _draw, _run, 'tlo.methods.population' )['tlo.methods.population']['scaling_factor']['scaling_factor'].values[0] except KeyError: return 1.0 if custom_generate_series is None: assert column is not None, "Must specify which column to extract" results_index = None if index is not None: # extract the index from the first log, and use this ensure that all other are exactly the same. filename = f"{module}.pickle" df: pd.DataFrame = load_pickled_dataframes(results_folder, draw=0, run=0, name=filename)[module][key] results_index = df[index] results = pd.DataFrame(columns=cols) for draw in range(info['number_of_draws']): for run in range(info['runs_per_draw']): try: df: pd.DataFrame = load_pickled_dataframes(results_folder, draw, run, module)[module][key] results[draw, run] = df[column] * get_multiplier(draw, run) if index is not None: idx = df[index] assert idx.equals(results_index), "Indexes are not the same between runs" except ValueError: results[draw, run] = np.nan # if 'index' is provided, set this to be the index of the results if index is not None: results.index = results_index return results else: # A custom commaand to generate a series has been provided. # No other arguements should be provided. assert index is None, "Cannot specify an index if using custom_generate_series" assert column is None, "Cannot specify a column if using custom_generate_series" # Collect results and then use pd.concat as indicies may be different betweeen runs res = dict() for draw in range(info['number_of_draws']): for run in range(info['runs_per_draw']): df: pd.DataFrame = load_pickled_dataframes(results_folder, draw, run, module)[module][key] output_from_eval = custom_generate_series(df) assert pd.Series == type(output_from_eval), 'Custom command does not generate a pd.Series' res[f"{draw}_{run}"] = output_from_eval * get_multiplier(draw, run) results = pd.concat(res.values(), axis=1).fillna(0) results.columns = cols return results
[docs]def summarize(results: pd.DataFrame, only_mean: bool = False, collapse_columns: bool = False) -> pd.DataFrame: """Utility function to compute summary statistics Finds mean value and 95% interval across the runs for each draw. """ summary = pd.DataFrame( columns=pd.MultiIndex.from_product( [ results.columns.unique(level='draw'), ["mean", "lower", "upper"] ], names=['draw', 'stat']), index=results.index ) summary.loc[:, (slice(None), "mean")] = results.groupby(axis=1, by='draw').mean().values summary.loc[:, (slice(None), "lower")] = results.groupby(axis=1, by='draw').quantile(0.025).values summary.loc[:, (slice(None), "upper")] = results.groupby(axis=1, by='draw').quantile(0.975).values if only_mean and (not collapse_columns): # Remove other metrics and simplify if 'only_mean' across runs for each draw is required: om: pd.DataFrame = summary.loc[:, (slice(None), "mean")] om.columns = [c[0] for c in om.columns.to_flat_index()] return om elif collapse_columns and (len(summary.columns.levels[0]) == 1): # With 'collapse_columns', if number of draws is 1, then collapse columns multi-index: summary_droppedlevel = summary.droplevel('draw', axis=1) if only_mean: return summary_droppedlevel['mean'] else: return summary_droppedlevel else: return summary
[docs]def get_grid(params: pd.DataFrame, res: pd.Series): """Utility function to create the arrays needed to plot a heatmap. :param pd.DataFrame params: the dataframe of parameters with index=draw (made using `extract_params()`). :param pd.Series res: results of interest with index=draw (can be made using `extract_params()`) :returns: grid as dictionary """ res = pd.concat([params.pivot(columns='module_param', values='value'), res], axis=1) piv = res.pivot_table(index=res.columns[0], columns=res.columns[1], values=res.columns[2]) grid = dict() grid[res.columns[0]], grid[res.columns[1]] = np.meshgrid(piv.index, piv.columns) grid[res.columns[2]] = piv.values return grid
[docs]def format_gbd(gbd_df: pd.DataFrame): """Format GBD data to give standarize categories for age_group and period""" # Age-groups: gbd_df['Age_Grp'] = gbd_df['Age_Grp'].astype(make_age_grp_types()) # label periods: calperiods, calperiodlookup = make_calendar_period_lookup() gbd_df['Period'] = gbd_df['Year'].map(calperiodlookup).astype(make_calendar_period_type()) return gbd_df
[docs]def create_pickles_locally(scenario_output_dir): """For a run from the Batch system that has not resulted in the creation of the pickles, reconstruct the pickles locally.""" def turn_log_into_pickles(logfile): print(f"Opening {logfile}") outputs = parse_log_file(logfile) for key, output in outputs.items(): if key.startswith("tlo."): print(f" - Writing {key}.pickle") with open(logfile.parent / f"{key}.pickle", "wb") as f: pickle.dump(output, f) f: os.DirEntry draw_folders = [f for f in os.scandir(scenario_output_dir) if f.is_dir()] for draw_folder in draw_folders: run_folders = [f for f in os.scandir(draw_folder) if f.is_dir()] for run_folder in run_folders: logfile = [x for x in os.listdir(run_folder) if x.endswith('.log')][0] turn_log_into_pickles(Path(run_folder) / logfile)
[docs]def compare_number_of_deaths(logfile: Path, resourcefilepath: Path): """Helper function to produce tables summarising deaths in the model run (given be a logfile) and the corresponding number of deaths in the GBD dataset. NB. * Requires output from the module `tlo.methods.demography` * Will do scaling automatically if the scaling-factor has been computed in the simulation (but not otherwise). """ output = parse_log_file(logfile) # 1) Get model outputs: # - get scaling factor: if 'scaling_factor' in output['tlo.methods.population']: sf = output['tlo.methods.population']['scaling_factor']['scaling_factor'].values[0] else: sf = 1.0 # - extract number of death by period/sex/age-group model = output['tlo.methods.demography']['death'].assign( year=lambda x: x['date'].dt.year ).groupby( ['sex', 'year', 'age', 'label'] )['person_id'].count().mul(sf) # - format categories: agegrps, agegrplookup = make_age_grp_lookup() calperiods, calperiodlookup = make_calendar_period_lookup() model = model.reset_index() model['age_grp'] = model['age'].map(agegrplookup).astype(make_age_grp_types()) model['period'] = model['year'].map(calperiodlookup).astype(make_calendar_period_type()) model = model.drop(columns=['age', 'year']) # - sum over period and divide by five to give yearly averages model = model.groupby(['period', 'sex', 'age_grp', 'label']).sum().div(5.0).rename( columns={'person_id': 'model'}).replace({0: np.nan}) # 2) Load comparator GBD datasets # - Load data, format and limit to deaths only: gbd_dat = format_gbd(pd.read_csv(resourcefilepath / 'gbd' / 'ResourceFile_Deaths_And_DALYS_GBD2019.csv')) gbd_dat = gbd_dat.loc[gbd_dat['measure_name'] == 'Deaths'] gbd_dat = gbd_dat.rename(columns={ 'Sex': 'sex', 'Age_Grp': 'age_grp', 'Period': 'period', 'GBD_Est': 'mean', 'GBD_Lower': 'lower', 'GBD_Upper': 'upper'}) # - Label GBD causes of death by 'label' defined in the simulation mapper_from_gbd_causes = pd.Series( output['tlo.methods.demography']['mapper_from_gbd_cause_to_common_label'].drop(columns={'date'}).loc[0] ).to_dict() gbd_dat['label'] = gbd_dat['cause_name'].map(mapper_from_gbd_causes) assert not gbd_dat['label'].isna().any() # - Create comparable data structure: gbd = gbd_dat.groupby(['period', 'sex', 'age_grp', 'label'])[['mean', 'lower', 'upper']].sum().div(5.0) gbd = gbd.add_prefix('GBD_') # 3) Return summary return gbd.merge(model, on=['period', 'sex', 'age_grp', 'label'], how='left')
[docs]def flatten_multi_index_series_into_dict_for_logging(ser: pd.Series) -> dict: """Helper function that converts a pd.Series with multi-index into a dict format that is suitable for logging. It does this by converting the multi-index into keys of type `str` in a format that later be used to reconstruct the multi-index (using `unflatten_flattened_multi_index_in_logging`).""" assert not ser.index.has_duplicates, "There should not be any duplicates in the multi-index. These will be lost" \ "in the conversion to a dict." names_of_multi_index = ser.index.names _df = ser.reset_index() flat_index = list() for _, row in _df.iterrows(): flat_index.append('|'.join([f"{col}={row[col]}" for col in names_of_multi_index])) return dict(zip(flat_index, ser.values))
[docs]def unflatten_flattened_multi_index_in_logging(_x: pd.DataFrame) -> pd.DataFrame: """Helper function that recreate the multi-index of logged results from a pd.DataFrame that is generated by `parse_log`. If a pd.DataFrame created by `parse_log` is the result of repeated logging of a pd.Series with a multi-index that was transformed before logging using `flatten_multi_index_series_into_dict_for_logging`, then the pd.DataFrame's columns will be those flattened labels. This helper function recreates the original multi-index from which the flattened labels were created and applies it to the pd.DataFrame.""" cols = _x.columns index_value_list = list() for col in cols.str.split('|'): index_value_list.append(tuple(component.split('=')[1] for component in col)) index_name_list = tuple(component.split('=')[0] for component in cols[0].split('|')) _y = _x.copy() _y.columns = pd.MultiIndex.from_tuples(index_value_list, names=index_name_list) return _y
[docs]class LogsDict(Mapping): """Parses module-specific log files and returns Pandas dataframes. The dictionary returned has the format:: { <logger 1 name>: { <log key 1>: <pandas dataframe>, <log key 2>: <pandas dataframe>, <log key 3>: <pandas dataframe> }, <logger 2 name>: { <log key 4>: <pandas dataframe>, <log key 5>: <pandas dataframe>, <log key 6>: <pandas dataframe> }, ... } """ def __init__(self, file_names_and_paths): super().__init__() # initialise class with module-specific log files paths self._logfile_names_and_paths: Dict[str, str] = file_names_and_paths # create a dictionary that will contain cached data self._results_cache: Dict[str, Dict] = dict() def __getitem__(self, key, cache=True): # check if the requested key is found in a dictionary containing module name and log file paths. if key # is found, return parsed logs else return KeyError if key in self._logfile_names_and_paths: # check if key is found in cache if key not in self._results_cache: result_df = _parse_log_file_inner_loop(self._logfile_names_and_paths[key]) # get metadata for the selected log file and merge it all with the selected key result_df[key]['_metadata'] = result_df['_metadata'] if not cache: # check if caching is disallowed return result_df[key] self._results_cache[key] = result_df[key] # add key specific parsed results to cache return self._results_cache[key] # return the added results else: raise KeyError def __contains__(self, k): # if key k is a valid logfile entry return k in self._logfile_names_and_paths
[docs] def items(self): # parse module-specific log file and return results as a generator for key in self._logfile_names_and_paths.keys(): module_specific_logs = self.__getitem__(key, cache=False) yield key, module_specific_logs
def __repr__(self): return repr(self._logfile_names_and_paths) def __len__(self): return len(self._logfile_names_and_paths)
[docs] def keys(self): # return dictionary keys return self._logfile_names_and_paths.keys()
[docs] def values(self): # parse module-specific log file and yield the results for key in self._logfile_names_and_paths.keys(): module_specific_logs = self.__getitem__(key, cache=False) yield module_specific_logs
def __iter__(self): return iter(self._logfile_names_and_paths) def __getstate__(self): # Ensure all items cached before pickling for key in self.keys(): self.__getitem__(key, cache=True) return self.__dict__