diff --git a/_delphi_utils_python/.pylintrc b/_delphi_utils_python/.pylintrc index 2a9334425..f30837c7e 100644 --- a/_delphi_utils_python/.pylintrc +++ b/_delphi_utils_python/.pylintrc @@ -1,8 +1,22 @@ -[DESIGN] -min-public-methods=1 +[MESSAGES CONTROL] +disable=logging-format-interpolation, + too-many-locals, + too-many-arguments, + # Allow pytest functions to be part of a class. + no-self-use, + # Allow pytest classes to have one test. + too-few-public-methods -[MESSAGES CONTROL] +[BASIC] + +# Allow arbitrarily short-named variables. +variable-rgx=[a-z_][a-z0-9_]* +argument-rgx=[a-z_][a-z0-9_]* +attr-rgx=[a-z_][a-z0-9_]* + +[DESIGN] -disable=R0801, C0330, E1101, E0611, C0114, C0116, C0103, R0913, R0914, W0702 +# Don't complain about pytest "unused" arguments. +ignored-argument-names=(_.*|run_as_module) \ No newline at end of file diff --git a/_delphi_utils_python/Makefile b/_delphi_utils_python/Makefile new file mode 100644 index 000000000..6db4b759d --- /dev/null +++ b/_delphi_utils_python/Makefile @@ -0,0 +1,24 @@ +.PHONY = venv, lint, test, clean + +dir = $(shell find ./delphi_* -name __init__.py | grep -o 'delphi_[_[:alnum:]]*') + +venv: + python3.8 -m venv env + +install: venv + . env/bin/activate; \ + pip install wheel ; \ + pip install -e . + +lint: + . env/bin/activate; \ + pylint $(dir); \ + pydocstyle $(dir) + +test: + . env/bin/activate ;\ + (cd tests && ../env/bin/pytest --cov=$(dir) --cov-report=term-missing) + +clean: + rm -rf env + rm -f params.json diff --git a/_delphi_utils_python/data_proc/geomap/geo_data_proc.py b/_delphi_utils_python/data_proc/geomap/geo_data_proc.py index 55428a2b6..da4f3857f 100644 --- a/_delphi_utils_python/data_proc/geomap/geo_data_proc.py +++ b/_delphi_utils_python/data_proc/geomap/geo_data_proc.py @@ -34,6 +34,7 @@ FIPS_MSA_OUT_FILENAME = "fips_msa_table.csv" FIPS_HRR_OUT_FILENAME = "fips_hrr_table.csv" FIPS_ZIP_OUT_FILENAME = "fips_zip_table.csv" +FIPS_HHS_FILENAME = "fips_hhs_table.csv" FIPS_POPULATION_OUT_FILENAME = "fips_pop.csv" ZIP_HSA_OUT_FILENAME = "zip_hsa_table.csv" @@ -42,8 +43,9 @@ ZIP_MSA_OUT_FILENAME = "zip_msa_table.csv" ZIP_POPULATION_OUT_FILENAME = "zip_pop.csv" ZIP_STATE_CODE_OUT_FILENAME = "zip_state_code_table.csv" +ZIP_HHS_FILENAME = "zip_hhs_table.csv" STATE_OUT_FILENAME = "state_codes_table.csv" -STATE_HHS_OUT_FILENAME = "state_code_hhs_region_number_table.csv" +STATE_HHS_OUT_FILENAME = "state_code_hhs_table.csv" JHU_FIPS_OUT_FILENAME = "jhu_uid_fips_table.csv" @@ -217,6 +219,12 @@ def create_jhu_uid_fips_crosswalk(): {"jhu_uid": "63072999", "fips": "72000", "weight": 1.0}, ] ) + cruise_ships = pd.DataFrame( + [ + {"jhu_uid": "84088888", "fips": "88888", "weight": 1.0}, + {"jhu_uid": "84099999", "fips": "99999", "weight": 1.0}, + ] + ) jhu_df = ( pd.read_csv(JHU_FIPS_URL, dtype={"UID": str, "FIPS": str}) @@ -234,7 +242,7 @@ def create_jhu_uid_fips_crosswalk(): # Drop the JHU UIDs that were hand-modified dup_ind = jhu_df["jhu_uid"].isin( pd.concat( - [hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned] + [hand_additions, unassigned_states, out_of_state, puerto_rico_unassigned, cruise_ships] )["jhu_uid"].values ) jhu_df.drop(jhu_df.index[dup_ind], inplace=True) @@ -326,12 +334,12 @@ def create_state_hhs_crosswalk(): hhs_state_pairs.append((9, "Northern Mariana Islands")) # Make dataframe - hhs_df = pd.DataFrame(hhs_state_pairs, columns=["hhs_region_number", "state_name"]) - hhs_df["hhs_region_number"] = hhs_df["hhs_region_number"].astype(str) + hhs_df = pd.DataFrame(hhs_state_pairs, columns=["hhs", "state_name"]) + hhs_df["hhs"] = hhs_df["hhs"].astype(str) ( ss_df.merge(hhs_df, on="state_name", how="left") - .dropna()[["state_code", "hhs_region_number"]] + .dropna()[["state_code", "hhs"]] .to_csv(join(OUTPUT_DIR, STATE_HHS_OUT_FILENAME), index=False) ) @@ -391,6 +399,7 @@ def create_fips_population_table(): df_pr = df_pr.groupby("fips").sum().reset_index() df_pr = df_pr[~df_pr["fips"].isin(census_pop["fips"])] census_pop_pr = pd.concat([census_pop, df_pr]) + census_pop_pr.to_csv(join(OUTPUT_DIR, FIPS_POPULATION_OUT_FILENAME), index=False) @@ -524,6 +533,56 @@ def derive_zip_to_state_code(): ) +def derive_fips_hhs_crosswalk(): + """ + Builds a crosswalk between FIPS county codes and HHS regions. + """ + if not isfile(join(OUTPUT_DIR, STATE_HHS_OUT_FILENAME)): + create_state_hhs_crosswalk() + if not isfile(join(OUTPUT_DIR, FIPS_POPULATION_OUT_FILENAME)): + create_fips_population_table() + + fips_pop = pd.read_csv( + join(OUTPUT_DIR, FIPS_POPULATION_OUT_FILENAME), dtype={"fips": str, "pop": int} + ) + state_hhs = pd.read_csv( + join(OUTPUT_DIR, STATE_HHS_OUT_FILENAME), + dtype={"state_code": str, "hhs": str}, + ) + + fips_pop["state_code"] = fips_pop["fips"].str[:2] + ( + fips_pop.merge(state_hhs, on="state_code", how="left") + .drop(columns=["state_code", "pop"]) + .to_csv(join(OUTPUT_DIR, FIPS_HHS_FILENAME), index=False) + ) + + +def derive_zip_hhs_crosswalk(): + """ + Builds a crosswalk between zip code and HHS regions. + """ + if not isfile(join(OUTPUT_DIR, STATE_HHS_OUT_FILENAME)): + create_state_hhs_crosswalk() + if not isfile(join(OUTPUT_DIR, ZIP_STATE_CODE_OUT_FILENAME)): + derive_zip_to_state_code() + + zip_state = pd.read_csv( + join(OUTPUT_DIR, ZIP_STATE_CODE_OUT_FILENAME), + dtype={"zip": str, "pop": int, "state_code": str} + ) + state_hhs = pd.read_csv( + join(OUTPUT_DIR, STATE_HHS_OUT_FILENAME), + dtype={"state_code": str, "hhs": str}, + ) + + ( + zip_state.merge(state_hhs, on="state_code", how="left") + .drop(columns=["state_code", "state_id", "state_name"]) + .to_csv(join(OUTPUT_DIR, ZIP_HHS_FILENAME), index=False) + ) + + if __name__ == "__main__": create_fips_zip_crosswalk() create_zip_hsa_hrr_crosswalk() @@ -538,3 +597,5 @@ def derive_zip_to_state_code(): derive_zip_to_state_code() derive_fips_state_crosswalk() derive_zip_population_table() + derive_fips_hhs_crosswalk() + derive_zip_hhs_crosswalk() \ No newline at end of file diff --git a/_delphi_utils_python/delphi_utils/__init__.py b/_delphi_utils_python/delphi_utils/__init__.py index cc2ca7cfd..63766533f 100644 --- a/_delphi_utils_python/delphi_utils/__init__.py +++ b/_delphi_utils_python/delphi_utils/__init__.py @@ -1,12 +1,16 @@ # -*- coding: utf-8 -*- -"""Common Utility Functions to Support DELPHI Indicators -""" +"""Common Utility Functions to Support DELPHI Indicators.""" from __future__ import absolute_import from .archive import ArchiveDiffer, GitArchiveDiffer, S3ArchiveDiffer from .export import create_export_csv from .utils import read_params + +from .slack_notifier import SlackNotifier +from .logger import get_structured_logger from .geomap import GeoMapper +from .smooth import Smoother +from .signal import add_prefix, public_signal __version__ = "0.1.0" diff --git a/_delphi_utils_python/delphi_utils/archive.py b/_delphi_utils_python/delphi_utils/archive.py index 7c0e8be06..a707fc4a1 100644 --- a/_delphi_utils_python/delphi_utils/archive.py +++ b/_delphi_utils_python/delphi_utils/archive.py @@ -1,5 +1,6 @@ """ Utilities for diffing and archiving covidcast export CSVs. + Aims to simplify the creation of issues for new and backfilled value for indicators. Also handles archiving of export CSVs to some backend (git, S3 etc.) before replacing them. @@ -52,6 +53,7 @@ def diff_export_csv( ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: """ Find differences in exported covidcast CSVs, using geo_id as the index. + Treats NA == NA as True. Parameters @@ -68,7 +70,6 @@ def diff_export_csv( changed_df is the pd.DataFrame of common rows from after_csv with changed values. added_df is the pd.DataFrame of added rows from after_csv. """ - export_csv_dtypes = {"geo_id": str, "val": float, "se": float, "sample_size": float} @@ -99,12 +100,13 @@ def run_module(archive_type: str, cache_dir: str, export_dir: str, **kwargs): - """Builds and runs an ArchiveDiffer. + """Build and run an ArchiveDiffer. Parameters ---------- archive_type: str - Type of ArchiveDiffer to run. Must be one of ["git", "s3"] which correspond to `GitArchiveDiffer` and `S3ArchiveDiffer`, respectively. + Type of ArchiveDiffer to run. Must be one of ["git", "s3"] which correspond to + `GitArchiveDiffer` and `S3ArchiveDiffer`, respectively. cache_dir: str The directory for storing most recent archived/uploaded CSVs to start diffing from. export_dir: str @@ -131,13 +133,11 @@ def run_module(archive_type: str, class ArchiveDiffer: - """ - Base class for performing diffing and archiving of exported covidcast CSVs - """ + """Base class for performing diffing and archiving of exported covidcast CSVs.""" def __init__(self, cache_dir: str, export_dir: str): """ - Initialize an ArchiveDiffer + Initialize an ArchiveDiffer. Parameters ---------- @@ -156,7 +156,8 @@ def __init__(self, cache_dir: str, export_dir: str): def update_cache(self): """ - For making sure cache_dir is updated correctly from a backend. + Make sure cache_dir is updated correctly from a backend. + To be implemented by specific archiving backends. Should set self._cache_updated = True after verifying cache is updated. """ @@ -164,7 +165,8 @@ def update_cache(self): def diff_exports(self) -> Tuple[Files, FileDiffMap, Files]: """ - Finds diffs across and within CSV files, from cache_dir to export_dir. + Find diffs across and within CSV files, from cache_dir to export_dir. + Should be called after update_cache() succeeds. Only works on *.csv files, ignores every other file. @@ -222,7 +224,8 @@ def diff_exports(self) -> Tuple[Files, FileDiffMap, Files]: def archive_exports(self, exported_files: Files) -> Tuple[Files, Files]: """ - Handles actual archiving of files, depending on specific backend. + Handle actual archiving of files, depending on specific backend. + To be implemented by specific archiving backends. Parameters @@ -240,6 +243,8 @@ def archive_exports(self, exported_files: Files) -> Tuple[Files, Files]: def filter_exports(self, common_diffs: FileDiffMap): """ + Filter export directory to only contain relevant files. + Filters down the export_dir to only contain: 1) New files, 2) Changed files, filtered-down to the ADDED and CHANGED rows only. Should be called after archive_exports() so we archive the raw exports before @@ -268,7 +273,7 @@ def filter_exports(self, common_diffs: FileDiffMap): replace(diff_file, exported_file) def run(self): - """Runs the differ and archives the changed and new files.""" + """Run the differ and archive the changed and new files.""" self.update_cache() # Diff exports, and make incremental versions @@ -292,7 +297,8 @@ def run(self): class S3ArchiveDiffer(ArchiveDiffer): """ - AWS S3 backend for archving + AWS S3 backend for archiving. + Archives CSV files into a S3 bucket, with keys "{indicator_prefix}/{csv_file_name}". Ideally, versioning should be enabled in this bucket to track versions of each CSV file. """ @@ -305,6 +311,7 @@ def __init__( ): """ Initialize a S3ArchiveDiffer. + See this link for possible aws_credentials kwargs: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session @@ -329,9 +336,7 @@ def __init__( self.indicator_prefix = indicator_prefix def update_cache(self): - """ - For making sure cache_dir is updated with all latest files from the S3 bucket. - """ + """Make sure cache_dir is updated with all latest files from the S3 bucket.""" # List all indicator-related objects from S3 archive_objects = self.bucket.objects.filter( Prefix=self.indicator_prefix).all() @@ -351,13 +356,13 @@ def update_cache(self): self._cache_updated = True - def archive_exports(self, + def archive_exports(self, # pylint: disable=arguments-differ exported_files: Files, update_cache: bool = True, update_s3: bool = True ) -> Tuple[Files, Files]: """ - Handles actual archiving of files to the S3 bucket. + Handle actual archiving of files to the S3 bucket. Parameters ---------- @@ -397,7 +402,8 @@ def archive_exports(self, class GitArchiveDiffer(ArchiveDiffer): """ - Local git repo backend for archiving + Local git repo backend for archiving. + Archives CSV files into a local git repo as commits. Assumes that a git repository is already set up. """ @@ -445,7 +451,8 @@ def __init__( def get_branch(self, branch_name: Optional[str] = None) -> Head: """ - Retrieves a Head object representing a branch of specified name. + Retrieve a Head object representing a branch of specified name. + Creates the branch from the current active branch if does not exist yet. Parameters @@ -468,6 +475,8 @@ def get_branch(self, branch_name: Optional[str] = None) -> Head: @contextmanager def archiving_branch(self): """ + Context manager for checking out a branch. + Useful for checking out self.branch within a context, then switching back to original branch when finished. """ @@ -481,8 +490,9 @@ def archiving_branch(self): def update_cache(self): """ + Check if cache_dir is clean: has everything nicely committed if override_dirty=False. + Since we are using a local git repo, assumes there is nothing to update from. - Checks if cache_dir is clean: has everything nice committed if override_dirty=False """ # Make sure cache directory is clean: has everything nicely committed if not self.override_dirty: @@ -494,14 +504,16 @@ def update_cache(self): def diff_exports(self) -> Tuple[Files, FileDiffMap, Files]: """ - Same as base class diff_exports, but in context of specified branch + Find diffs across and within CSV files, from cache_dir to export_dir. + + Same as base class diff_exports, but in context of specified branch. """ with self.archiving_branch(): return super().diff_exports() def archive_exports(self, exported_files: Files) -> Tuple[Files, Files]: """ - Handles actual archiving of files to the local git repo. + Handle actual archiving of files to the local git repo. Parameters ---------- @@ -587,11 +599,11 @@ def archive_exports(self, exported_files: Files) -> Tuple[Files, Files]: args = parser.parse_args() params = read_params() run_module(args.archive_type, - params.cache_dir, - params.export_dir, - aws_credentials=params.aws_credentials, + params["cache_dir"], + params["export_dir"], + aws_credentials=params["aws_credentials"], branch_name=args.branch_name, - bucket_name=params.bucket_name, + bucket_name=params["bucket_name"], commit_message=args.commit_message, commit_partial_success=args.commit_partial_success, indicator_prefix=args.indicator_prefix, diff --git a/_delphi_utils_python/delphi_utils/data/fips_hhs_table.csv b/_delphi_utils_python/delphi_utils/data/fips_hhs_table.csv new file mode 100644 index 000000000..f5b348c09 --- /dev/null +++ b/_delphi_utils_python/delphi_utils/data/fips_hhs_table.csv @@ -0,0 +1,3275 @@ +fips,hhs +01000,4 +01001,4 +01003,4 +01005,4 +01007,4 +01009,4 +01011,4 +01013,4 +01015,4 +01017,4 +01019,4 +01021,4 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+99825,1.0,10 +99826,1.0,10 +99827,0.9634896233666408,10 +99827,0.03651037663335895,10 +99829,1.0,10 +99830,1.0,10 +99832,1.0,10 +99833,1.0,10 +99835,1.0,10 +99836,1.0,10 +99840,1.0,10 +99841,1.0,10 +99901,0.9975570032573292,10 +99901,0.0024429967426710096,10 +99903,1.0,10 +99918,1.0,10 +99919,1.0,10 +99921,1.0,10 +99922,1.0,10 +99923,1.0,10 +99925,1.0,10 +99926,1.0,10 +99927,1.0,10 +99929,1.0,10 diff --git a/_delphi_utils_python/delphi_utils/export.py b/_delphi_utils_python/delphi_utils/export.py index 47f4a7604..4548c5773 100644 --- a/_delphi_utils_python/delphi_utils/export.py +++ b/_delphi_utils_python/delphi_utils/export.py @@ -1,16 +1,21 @@ +"""Export data in the format expected by the Delphi API.""" # -*- coding: utf-8 -*- from datetime import datetime from os.path import join +from typing import Optional +import numpy as np import pandas as pd def create_export_csv( df: pd.DataFrame, - start_date: datetime, export_dir: str, - metric: str, geo_res: str, sensor: str, + metric: Optional[str] = None, + start_date: Optional[datetime] = None, + end_date: Optional[datetime] = None, + remove_null_samples: Optional[bool] = False ): """Export data in the format expected by the Delphi API. @@ -20,21 +25,39 @@ def create_export_csv( Columns: geo_id, timestamp, val, se, sample_size export_dir: str Export directory - metric: str - Metric we are considering geo_res: str Geographic resolution to which the data has been aggregated sensor: str Sensor that has been calculated (cumulative_counts vs new_counts) + metric: Optional[str] + Metric we are considering, if any. + start_date: Optional[datetime] + Earliest date to export or None if no minimum date restrictions should be applied. + end_date: Optional[datetime] + Latest date to export or None if no maximum date restrictions should be applied. + remove_null_samples: Optional[bool] + Whether to remove entries whose sample sizes are null. """ df = df.copy() + df["timestamp"] = pd.to_datetime(df["timestamp"]) + if start_date is None: + start_date = min(df["timestamp"]) + if end_date is None: + end_date = max(df["timestamp"]) + dates = pd.Series( - df[df["timestamp"] >= start_date]["timestamp"].unique() + df[np.logical_and(df["timestamp"] >= start_date, + df["timestamp"] <= end_date)]["timestamp"].unique() ).sort_values() + for date in dates: - export_fn = f'{date.strftime("%Y%m%d")}_{geo_res}_' f"{metric}_{sensor}.csv" - export_file = join(export_dir, export_fn) - df[df["timestamp"] == date][["geo_id", "val", "se", "sample_size",]].to_csv( - export_file, index=False, na_rep="NA" - ) + if metric is None: + export_filename = f"{date.strftime('%Y%m%d')}_{geo_res}_{sensor}.csv" + else: + export_filename = f"{date.strftime('%Y%m%d')}_{geo_res}_{metric}_{sensor}.csv" + export_file = join(export_dir, export_filename) + export_df = df[df["timestamp"] == date][["geo_id", "val", "se", "sample_size",]] + if remove_null_samples: + export_df = export_df[export_df["sample_size"].notnull()] + export_df.to_csv(export_file, index=False, na_rep="NA") diff --git a/_delphi_utils_python/delphi_utils/geomap.py b/_delphi_utils_python/delphi_utils/geomap.py index 54103b93c..70fe01ad3 100644 --- a/_delphi_utils_python/delphi_utils/geomap.py +++ b/_delphi_utils_python/delphi_utils/geomap.py @@ -6,15 +6,12 @@ TODO: - use a caching utility to store the crossfiles see: https://github.com/cmu-delphi/covidcast-indicators/issues/282 -- remove deprecated functions once integration into JHU and Quidel is refactored - see: https://github.com/cmu-delphi/covidcast-indicators/issues/283 """ - +# pylint: disable=too-many-lines from os.path import join -import warnings -import pkg_resources import pandas as pd +import pkg_resources from pandas.api.types import is_string_dtype DATA_PATH = "data" @@ -25,6 +22,7 @@ "msa": join(DATA_PATH, "zip_msa_table.csv"), "pop": join(DATA_PATH, "zip_pop.csv"), "state": join(DATA_PATH, "zip_state_code_table.csv"), + "hhs": join(DATA_PATH, "zip_hhs_table.csv") }, "fips": { "zip": join(DATA_PATH, "fips_zip_table.csv"), @@ -32,16 +30,17 @@ "msa": join(DATA_PATH, "fips_msa_table.csv"), "pop": join(DATA_PATH, "fips_pop.csv"), "state": join(DATA_PATH, "fips_state_table.csv"), + "hhs": join(DATA_PATH, "fips_hhs_table.csv"), }, "state": {"state": join(DATA_PATH, "state_codes_table.csv")}, "state_code": { - "hhs_region_number": join(DATA_PATH, "state_code_hhs_region_number_table.csv") + "hhs": join(DATA_PATH, "state_code_hhs_table.csv") }, "jhu_uid": {"fips": join(DATA_PATH, "jhu_uid_fips_table.csv")}, } -class GeoMapper: +class GeoMapper: # pylint: disable=too-many-public-methods """Geo mapping tools commonly used in Delphi. The GeoMapper class provides utility functions for translating between different @@ -58,12 +57,14 @@ class GeoMapper: - [x] zip -> hrr : unweighted - [x] zip -> msa : unweighted - [x] zip -> state + - [x] zip -> hhs - [x] zip -> population - - [x] state code -> hhs_region_number + - [x] state code -> hhs - [x] fips -> state : unweighted - [x] fips -> msa : unweighted - [x] fips -> megacounty - [x] fips -> hrr + - [x] fips -> hhs - [x] nation - [ ] zip -> dma (postponed) @@ -94,8 +95,9 @@ class GeoMapper: """ def __init__(self): - """Initialize geomapper. Holds loading the crosswalk tables - until a conversion function is first used. + """Initialize geomapper. + + Holds loading the crosswalk tables until a conversion function is first used. Parameters --------- @@ -104,16 +106,25 @@ def __init__(self): """ self.crosswalk_filepaths = CROSSWALK_FILEPATHS self.crosswalks = { - "zip": {"fips": None, "hrr": None, "msa": None, "pop": None, "state": None}, - "fips": {"zip": None, "hrr": None, "msa": None, "pop": None, "state": None}, + "zip": { + geo: None for geo in ["fips", "hrr", "msa", "pop", "state", "hhs"] + }, + "fips": { + geo: None for geo in ["zip", "hrr", "msa", "pop", "state", "hhs"] + }, "state": {"state": None}, - "state_code": {"hhs_region_number": None}, + "state_code": {"hhs": None}, "jhu_uid": {"fips": None}, } + self.geo_lists = { + geo: None for geo in ["zip", "fips", "hrr", "state_id", "state_code", + "state_name", "hhs", "msa"] + } + self.geo_lists["nation"] = {"us"} # Utility functions def _load_crosswalk(self, from_code, to_code): - """Loads the crosswalk from from_code -> to_code.""" + """Load the crosswalk from from_code -> to_code.""" stream = pkg_resources.resource_stream( __name__, self.crosswalk_filepaths[from_code][to_code] ) @@ -125,6 +136,7 @@ def _load_crosswalk(self, from_code, to_code): ("jhu_uid", "fips"), ("zip", "msa"), ("fips", "hrr"), + ("zip", "hhs") ]: self.crosswalks[from_code][to_code] = pd.read_csv( stream, @@ -138,6 +150,8 @@ def _load_crosswalk(self, from_code, to_code): elif (from_code, to_code) in [ ("zip", "hrr"), ("fips", "msa"), + ("fips", "hhs"), + ("state_code", "hhs") ]: self.crosswalks[from_code][to_code] = pd.read_csv( stream, @@ -153,11 +167,6 @@ def _load_crosswalk(self, from_code, to_code): "state_name": str, }, ) - elif (from_code, to_code) == ("state_code", "hhs_region_number"): - self.crosswalks[from_code][to_code] = pd.read_csv( - stream, - dtype={"state_code": str, "hhs_region_number": str}, - ) elif (from_code, to_code) == ("zip", "state"): self.crosswalks[from_code][to_code] = pd.read_csv( stream, @@ -192,7 +201,7 @@ def _load_crosswalk(self, from_code, to_code): @staticmethod def convert_fips_to_mega(data, fips_col="fips", mega_col="megafips"): - """convert fips string to a megafips string""" + """Convert fips string to a megafips string.""" data = data.copy() data[mega_col] = data[fips_col].astype(str).str.zfill(5) data[mega_col] = data[mega_col].str.slice_replace(start=2, stop=5, repl="000") @@ -208,7 +217,7 @@ def megacounty_creation( date_col="date", mega_col="megafips", ): - """create megacounty column + """Create megacounty column. Parameters --------- @@ -257,11 +266,11 @@ def add_geocode( """Add a new geocode column to a dataframe. Currently supported conversions: - - fips -> state_code, state_id, state_name, zip, msa, hrr, nation - - zip -> state_code, state_id, state_name, fips, msa, hrr, nation + - fips -> state_code, state_id, state_name, zip, msa, hrr, nation, hhs + - zip -> state_code, state_id, state_name, fips, msa, hrr, nation, hhs - jhu_uid -> fips - state_x -> state_y, where x and y are in {code, id, name} - - state_code -> hhs_region_number + - state_code -> hhs Parameters --------- @@ -270,7 +279,7 @@ def add_geocode( from_code: {'fips', 'zip', 'jhu_uid', 'state_code', 'state_id', 'state_name'} Specifies the geocode type of the data in from_col. new_code: {'fips', 'zip', 'state_code', 'state_id', 'state_name', 'hrr', 'msa', - 'hhs_region_number'} + 'hhs'} Specifies the geocode type in new_col. from_col: str, default None Name of the column in dataframe containing from_code. If None, then the name @@ -301,7 +310,7 @@ def add_geocode( df[from_col] = df[from_col].astype(str) # Assuming that the passed-in records are all United States data, at the moment - if (from_code, new_code) in [("fips", "nation"), ("zip", "nation")]: + if (from_code, new_code) in [("fips", "nation"), ("zip", "nation")]: # pylint: disable=no-else-return df[new_col] = df[from_col].apply(lambda x: "us") return df elif new_code == "nation": @@ -360,7 +369,7 @@ def replace_geocode( - zip -> state_code, state_id, state_name, fips, msa, hrr, nation - jhu_uid -> fips - state_x -> state_y, where x and y are in {code, id, name} - - state_code -> hhs_region_number + - state_code -> hhs Parameters --------- @@ -373,7 +382,7 @@ def replace_geocode( new_col: str Name of the new column to add to data. new_code: {'fips', 'zip', 'state_code', 'state_id', 'state_name', 'hrr', 'msa', - 'hhs_region_number'} + 'hhs'} Specifies the geocode type of the data in new_col. date_col: str or None, default "date" Specify which column contains the date values. Used for value aggregation. @@ -413,9 +422,11 @@ def replace_geocode( df = df.groupby([new_col]).sum().reset_index() return df - def add_population_column(self, data, geocode_type, geocode_col=None): + def add_population_column(self, data, geocode_type, geocode_col=None, dropna=True): """ - Appends a population column to a dateframe, based on the FIPS or ZIP code. + Append a population column to a dataframe, based on the FIPS or ZIP code. + + If no dataframe is provided, the full crosswalk from geocode to population is returned. Parameters --------- @@ -433,6 +444,7 @@ def add_population_column(self, data, geocode_type, geocode_col=None): A dataframe with a population column appended. """ geocode_col = geocode_type if geocode_col is None else geocode_col + data = data.copy() if geocode_type not in ["fips", "zip"]: raise ValueError( @@ -440,17 +452,18 @@ def add_population_column(self, data, geocode_type, geocode_col=None): For other codes, aggregate those." ) + pop_df = self._load_crosswalk(from_code=geocode_type, to_code="pop") + if not is_string_dtype(data[geocode_col]): data[geocode_col] = data[geocode_col].astype(str).str.zfill(5) - pop_df = self._load_crosswalk(from_code=geocode_type, to_code="pop") - + merge_type = "inner" if dropna else "left" data_with_pop = ( - data.copy() - .merge(pop_df, left_on=geocode_col, right_on=geocode_type, how="inner") + data + .merge(pop_df, left_on=geocode_col, right_on=geocode_type, how=merge_type) .rename(columns={"pop": "population"}) ) - data_with_pop["population"] = data_with_pop["population"].astype(int) + return data_with_pop @staticmethod @@ -464,7 +477,7 @@ def fips_to_megacounty( mega_col="megafips", count_cols=None, ): - """Convert and aggregate from FIPS to megaFIPS + """Convert and aggregate from FIPS to megaFIPS. Parameters --------- @@ -505,816 +518,42 @@ def fips_to_megacounty( data = data.reset_index().groupby([date_col, mega_col]).sum() return data.reset_index() - ### DEPRECATED FUNCTIONS BELOW - - def convert_fips_to_state_code( - self, data, fips_col="fips", state_code_col="state_code" - ): - """DEPRECATED - Add a state_code column to a dataframe with fips column. - - Parameters - --------- - data: pd.DataFrame - Input dataframe. - fips_col: str - Name of FIPS column to convert in data. - state_code_col: str - Name of State Code column to convert in data. - - Return - --------- - data: pd.DataFrame - A copy of the dataframe with a state code column added. + def get_geo_values(self, geo_type): """ - warnings.warn( - "Use the function add_geocode(df, 'fips', 'state_code', ...) instead.", - DeprecationWarning, - ) + Return a set of all values for a given geography type. - data = data.copy() - - if not is_string_dtype(data[fips_col]): - data[fips_col] = data[fips_col].astype(str).str.zfill(5) + Uses the same caching paradigm as _load_crosswalks, storing the value from previous calls + and not re-reading the CSVs if the same geo type is requested multiple times. Does not + share the same crosswalk cache to keep complexity down. - # Take the first two digits of the FIPS code - data[state_code_col] = data[fips_col].str[:2] - - return data - - def fips_to_state_code( - self, - data, - fips_col="fips", - date_col="date", - count_cols=None, - state_code_col="state_code", - ): - """DEPRECATED - Translate dataframe from fips to state. + Reads the FIPS crosswalk files by default for reference data since those have mappings to + all other geos. Exceptions are nation, which has no mapping file and is hard-coded as 'us', + and state, which uses the state codes table since the fips/state mapping doesn't include + all territories. Parameters - --------- - data: pd.DataFrame - Input data. - fips_col: str - Name of dataframe column containing fips codes. - date_col: str - Name of dataframe column containing the dates. - count_cols: str - Name of dataframe column containing the data. If None (default) - all non fips/date are used. - state_id_col: str - Name of dataframe column to contain the state codes. - - Return - --------- - data: pd.DataFrame - A new dataframe with fips converted to state. - """ - warnings.warn( - "Use the function replace_geocode(df, 'fips', 'state_code', ...) instead.", - DeprecationWarning, - ) - - if count_cols: - data = data[[fips_col, date_col] + count_cols].copy() - data = self.convert_fips_to_state_code( - data, fips_col=fips_col, state_code_col=state_code_col - ) - data = data.groupby([date_col, state_code_col]).sum() - return data.reset_index() - - def convert_fips_to_state_id(self, data, fips_col="fips", state_id_col="state_id"): - """DEPRECATED - Create State ID column from FIPS column. - - Parameters - --------- - data: pd.DataFrame - Input dataframe. - fips_col: str - Name of FIPS column to convert in data. - state_id_col: str - Name of State ID column to convert in data. - - Return - --------- - data: pd.DataFrame - A copy of the dataframe with a state code column added. - """ - warnings.warn( - "Use the function add_geocode(df, 'fips', 'state_id', ...) instead.", - DeprecationWarning, - ) - - data = self.convert_fips_to_state_code(data, fips_col=fips_col) - data = self.convert_state_code_to_state_id(data, state_id_col=state_id_col) - return data - - def convert_fips_to_msa( - self, data, fips_col="fips", msa_col="msa", create_mega=False - ): - """DEPRECATED - Translate dataframe from fips to msa. - - Parameters - --------- - data: pd.DataFrame - Input data. - fips_col: str - Name of dataframe column containing fips codes. - date_col: str - Name of dataframe column containing the dates. - count_cols: str - Name of dataframe column containing the data. If None (default) all - non fips/date are used. - msa_col: str - Name of dataframe column to contain the msa codes. - - Return - --------- - data: pd.DataFrame - A new dataframe with fips converted to msa. - """ - warnings.warn( - "Use the function add_geocode(df, 'fips', 'msa', ...) instead.", - DeprecationWarning, - ) - - df = self._load_crosswalk(from_code="fips", to_code="msa") - data = data.copy() - - if not is_string_dtype(data[fips_col]): - data[fips_col] = data[fips_col].astype(str).str.zfill(5) - - msa_table = df.rename(columns={"msa": msa_col}) - data = data.merge(msa_table, left_on=fips_col, right_on="fips", how="left") - - # Megacounty codes are 1, followed by up to 4 leading zeros, and ending with - # two digits of the state's FIPS code.= - if create_mega: - data_st = data.loc[data[msa_col].isna(), fips_col] - data.loc[data[msa_col].isna(), msa_col] = "1" + data_st.astype(str).str[ - :2 - ].str.zfill(4) - - return data - - def convert_fips_to_zip( - self, data, fips_col="fips", zip_col="zip", weight_col="weight" - ): - """DEPRECATED - Create ZIP column from FIPS column. - - Parameters - --------- - data: pd.DataFrame - Input data. - fips_col: str - Name of dataframe column containing fips codes. - zip_col: str - Name of dataframe column containing zip codes. - weight_col: str - Name of dataframe weight column to create. - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function add_geocode(df, 'fips', 'zip', ...) instead.", - DeprecationWarning, - ) - - df = self._load_crosswalk(from_code="fips", to_code="zip") - data = data.copy() - - if not is_string_dtype(data[fips_col]): - data[fips_col] = data[fips_col].astype(str).str.zfill(5) - - cross = df.rename(columns={"zip": zip_col, "weight": weight_col}) - data = data.merge(cross, left_on=fips_col, right_on="fips", how="left").dropna( - subset=[zip_col] - ) - return data - - def convert_state_code_to_state_id( - self, data, state_code_col="state_code", state_id_col="state_id" - ): - """DEPRECATED - create state_id column from state_code column - - Parameters - --------- - data: pd.DataFrame input data - state_code_col: state_code column to convert - state_id_col: state_id column to create - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function add_geocode(df, 'state_code', 'state_id', ...) instead.", - DeprecationWarning, - ) - - state_table = self._load_crosswalk(from_code="state", to_code="state") - state_table = state_table[["state_code", "state_id"]].rename( - columns={"state_id": state_id_col} - ) - data = data.copy() - - data = data.merge( - state_table, left_on=state_code_col, right_on="state_code", how="left" - ) - return data - - def convert_zip_to_fips( - self, data, zip_col="zip", fips_col="fips", weight_col="weight" - ): - """DEPRECATED - Create FIPS column from ZIP column. - - Parameters - --------- - data: pd.DataFrame input data - zip_col: zip5 column to convert - fips_col: fips column to create - weight_col: weight (pop) column to create - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function add_geocode(df, 'zip', 'fips', ...) instead.", - DeprecationWarning, - ) - - df = self._load_crosswalk(from_code="zip", to_code="fips") - data = data.copy() - - if not is_string_dtype(data[zip_col]): - data[zip_col] = data[zip_col].astype(str).str.zfill(5) - - zip_table = df.rename(columns={"fips": fips_col, "weight": weight_col}) - data = data.merge(zip_table, left_on=zip_col, right_on="zip", how="left") - return data - - def convert_zip_to_hrr(self, data, zip_col="zip", hrr_col="hrr"): - """DEPRECATED - create hrr column from zip column - - Parameters - --------- - data: pd.DataFrame input data - zip_col: zip column to convert - hrr_col: hrr column to create - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function add_geocode(df, 'zip', 'hrr', ...) instead.", - DeprecationWarning, - ) - - df = self._load_crosswalk(from_code="zip", to_code="hrr") - data = data.copy() - - if not is_string_dtype(data[zip_col]): - data[zip_col] = data[zip_col].astype(str).str.zfill(5) - - hrr_table = df.rename(columns={"hrr": hrr_col}) - data = data.merge(hrr_table, left_on=zip_col, right_on="zip", how="left") - return data - - def convert_zip_to_msa( - self, data, zip_col="zip", msa_col="msa", date_col="date", count_cols=None - ): - """DEPRECATED.""" - warnings.warn( - "Use the function add_geocode(df, 'zip', 'msa', ...) instead.", - DeprecationWarning, - ) - - zip_to_msa_cross = self._load_crosswalk(from_code="zip", to_code="msa") - data = data.copy() - - if count_cols: - data = data[[zip_col, date_col] + count_cols].copy() - - if not is_string_dtype(data[zip_col]): - data[zip_col] = data[zip_col].astype(str).str.zfill(5) - - data = data.merge(zip_to_msa_cross, left_on="zip", right_on="zip", how="left") - return data - - def zip_to_msa( - self, data, zip_col="zip", msa_col="msa", date_col="date", count_cols=None - ): - """DEPRECATED.""" - warnings.warn( - "Use the function replace_geocode(df, 'zip', 'msa', ...) instead.", - DeprecationWarning, - ) - - data = self.convert_zip_to_msa( - data, - zip_col=zip_col, - msa_col=msa_col, - date_col=date_col, - count_cols=count_cols, - ) - data.drop(columns="zip", inplace=True) - - if count_cols is None: - count_cols = list(set(data.columns) - {date_col, msa_col, "weight"}) - - data[count_cols] = data[count_cols].multiply(data["weight"], axis=0) - data.drop("weight", axis=1, inplace=True) - data = data.groupby([date_col, msa_col], dropna=False).sum() - return data.reset_index() - - def convert_zip_to_state_code( - self, - data, - zip_col="zip", - state_code_col="state_code", - date_col="date", - count_cols=None, - ): - """DEPRECATED.""" - warnings.warn( - "Use the function add_geocode(df, 'zip', 'state_code', ...) instead.", - DeprecationWarning, - ) - - zip_to_state_cross = self._load_crosswalk(from_code="zip", to_code="state") - zip_to_state_cross = zip_to_state_cross.drop( - columns=["state_id", "state_name"] - ).rename({"state_code": state_code_col}) - - if count_cols: - data = data[[zip_col, date_col] + count_cols].copy() - else: - data = data.copy() - - if not is_string_dtype(data[zip_col]): - data[zip_col] = data[zip_col].astype(str).str.zfill(5) - - data = data.merge(zip_to_state_cross, left_on="zip", right_on="zip", how="left") - return data - - def zip_to_state_code( - self, - data, - zip_col="zip", - state_code_col="state_code", - date_col="date", - count_cols=None, - ): - """DEPRECATED.""" - warnings.warn( - "Use the function replace_geocode(df, 'zip', 'state_code', ...) instead.", - DeprecationWarning, - ) + ---------- + geo_type: str + One of "zip", "fips", "hrr", "state_id", "state_code", "state_name", "hhs", "msa", + and "nation" - data = self.convert_zip_to_state_code( - data, - zip_col=zip_col, - state_code_col=state_code_col, - date_col=date_col, - count_cols=count_cols, - ) - data.drop(columns="zip", inplace=True) - - if count_cols is None: - count_cols = list(set(data.columns) - {date_col, state_code_col, "weight"}) - - data[count_cols] = data[count_cols].multiply(data["weight"], axis=0) - data.drop("weight", axis=1, inplace=True) - data = data.groupby([date_col, state_code_col], dropna=False).sum() - return data.reset_index() - - def convert_zip_to_state_id( - self, - data, - zip_col="zip", - state_id_col="state_id", - date_col="date", - count_cols=None, - ): - """DEPRECATED.""" - warnings.warn( - "Use the function add_geocode(df, 'zip', 'state_id', ...) instead.", - DeprecationWarning, - ) - - zip_to_state_cross = self._load_crosswalk(from_code="zip", to_code="state") - zip_to_state_cross = zip_to_state_cross.drop( - columns=["state_code", "state_name"] - ).rename({"state_id": state_id_col}) - - if count_cols: - data = data[[zip_col, date_col] + count_cols].copy() - else: - data = data.copy() - - if not is_string_dtype(data[zip_col]): - data[zip_col] = data[zip_col].astype(str).str.zfill(5) - - data = data.merge(zip_to_state_cross, left_on="zip", right_on="zip", how="left") - return data - - def zip_to_state_id( - self, - data, - zip_col="zip", - state_id_col="state_id", - date_col="date", - count_cols=None, - ): - """DEPRECATED""" - warnings.warn( - "Use the function replace_geocode(df, 'zip', 'state_id', ...) instead.", - DeprecationWarning, - ) - - data = self.convert_zip_to_state_id( - data, - zip_col=zip_col, - state_id_col=state_id_col, - date_col=date_col, - count_cols=count_cols, - ) - data.drop(columns="zip", inplace=True) - - if count_cols is None: - count_cols = list(set(data.columns) - {date_col, state_id_col, "weight"}) - - data[count_cols] = data[count_cols].multiply(data["weight"], axis=0) - data.drop("weight", axis=1, inplace=True) - data = data.groupby([date_col, state_id_col], dropna=False).sum() - return data.reset_index() - - def fips_to_state_id( - self, - data, - fips_col="fips", - date_col="date", - count_cols=None, - state_id_col="state_id", - ): - """DEPRECATED - Translate dataframe from fips to state. - - Parameters - --------- - data: pd.DataFrame - Input data. - fips_col: str - Name of dataframe column containing fips codes. - date_col: str - Name of dataframe column containing the dates. - count_cols: str - Name of dataframe column containing the data. If None (default) - all non fips/date are used. - state_id_col: str - Name of dataframe column to contain the state codes. - - Return - --------- - data: pd.DataFrame - A new dataframe with fips converted to state. - """ - warnings.warn( - "Use the function replace_geocode(df, 'fips', 'state_id', ...) instead.", - DeprecationWarning, - ) - - if count_cols: - data = data[[fips_col, date_col] + count_cols].copy() - data = self.convert_fips_to_state_id( - data, fips_col=fips_col, state_id_col=state_id_col - ) - # data.drop([fips_col, "state_code"], axis=1, inplace=True) - data = data.groupby([date_col, state_id_col]).sum() - return data.reset_index() - - def fips_to_msa( - self, - data, - fips_col="fips", - date_col="date", - count_cols=None, - create_mega=False, - msa_col="msa", - ): - """DEPRECATED - Translate dataframe from fips to metropolitan statistical area (msa). - - The encoding we use is based on the most recent Census Bureau release of CBSA (March 2020) - All counties not mapped to MSAs have msa encoded as 000XX where XX is the fips state code - To see how the crosswalk table is derived look at _delphi_utils_python/data_proc/geomap/* - - Parameters - --------- - data: pd.DataFrame - Input data. - fips_col: str - Name of dataframe column containing fips codes. - date_col: str - Name of dataframe column containing the dates. - count_cols: str - Name of dataframe column containing the data. If None (default) - all non fips/date are used. - msa_col: str - Name of dataframe column to contain the msa codes. - - Return - --------- - data: pd.DataFrame - A new dataframe with fips converted to msa. - """ - warnings.warn( - "Use the function replace_geocode(df, 'fips', 'msa', ...) instead.", - DeprecationWarning, - ) - - if count_cols: - data = data[[fips_col, date_col] + count_cols].copy() - data = self.convert_fips_to_msa( - data, fips_col=fips_col, msa_col=msa_col, create_mega=create_mega - ) - data.drop(fips_col, axis=1, inplace=True) - data.dropna(axis=0, subset=[msa_col], inplace=True) - if date_col: - data = data.groupby([date_col, msa_col]).sum() - else: - data = data.groupby(msa_col).sum() - return data.reset_index() - - def zip_to_fips( - self, data, zip_col="zip", fips_col="fips", date_col="date", count_cols=None - ): - """DEPRECATED - Convert and aggregate from ZIP to FIPS. - - Parameters - --------- - data: pd.DataFrame input data - zip_col: zip column to convert - fips_col: fips (county) column to create - date_col: date column (is not aggregated, groupby), if None then no dates - count_cols: the count data columns to aggregate, if None (default) all non data/geo are used - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function replace_geocode(df, 'zip', 'fips', ...) instead.", - DeprecationWarning, - ) - - if date_col: - assert date_col in data.columns, f"{date_col} not in data.columns" - assert zip_col in data.columns, f"{zip_col} not in data.columns" - if not count_cols: - count_cols = list(set(data.columns) - {date_col, zip_col}) - else: - count_cols = list(count_cols) - if date_col: - data = data[[zip_col, date_col] + count_cols].copy() - else: - data = data[[zip_col] + count_cols].copy() - data = self.convert_zip_to_fips(data, zip_col=zip_col, fips_col=fips_col) - data[count_cols] = data[count_cols].multiply(data["weight"], axis=0) - data.drop([zip_col, "weight"], axis=1, inplace=True) - - if date_col: - data = data.groupby([date_col, fips_col]).sum() - else: - data = data.groupby(fips_col).sum() - return data.reset_index() - - def zip_to_hrr( - self, data, zip_col="zip", hrr_col="hrr", date_col="date", count_cols=None - ): - """DEPRECATED - Convert and aggregate from ZIP to FIPS. - - Parameters - --------- - data: pd.DataFrame input data - zip_col: zip column to convert - hrr_col: hrr column to create - date_col: date column (is not aggregated, groupby) - count_cols: the count data columns to aggregate, if None (default) all - non data/geo are used - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function replace_geocode(df, 'zip', 'hrr', ...) instead.", - DeprecationWarning, - ) - - if count_cols is None: - count_cols = list(set(data.columns) - {date_col, zip_col}) - else: - count_cols = list(count_cols) - - data = data[[zip_col, date_col] + count_cols].copy() - data = self.convert_zip_to_hrr(data, zip_col=zip_col, hrr_col=hrr_col) - data = data.groupby([date_col, hrr_col]).sum() - return data.reset_index() - - def convert_jhu_uid_to_fips( - self, data, jhu_col="jhu_uid", fips_col="fips", weight_col="weight" - ): - """DEPRECATED - create fips (county) column from jhu uid column - - Parameters - --------- - data: pd.DataFrame input data - jhu_col: int, JHU uid column to convert - fips_col: str, fips column to create - weight_col: weight (pop) column to create - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function add_geocode(df, 'jhu_uid', 'fips', ...) instead.", - DeprecationWarning, - ) - - df = self._load_crosswalk(from_code="jhu_uid", to_code="fips") - data = data.copy() - - if not is_string_dtype(data[jhu_col]): - data[jhu_col] = data[jhu_col].astype(str) - - jhu_table = df.rename(columns={"fips": fips_col, "weight": weight_col}) - data = data.merge(jhu_table, left_on=jhu_col, right_on="jhu_uid", how="left") - if jhu_col != "jhu_uid": - data.drop(columns=["jhu_uid"], inplace=True) - return data - - def jhu_uid_to_fips( - self, data, jhu_col="jhu_uid", fips_col="fips", date_col="date", count_cols=None - ): - """DEPRECATED - Convert and aggregate from zip to fips - - Parameters - --------- - data: pd.DataFrame input data - jhu_col: jhu uid column to convert - fips_col: fips (county) column to create - date_col: date column (is not aggregated, groupby) - count_cols: the count data columns to aggregate, if None (default) all non - data/geo are used - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function replace_geocode(df, 'jhu_uid', 'fips', ...) instead.", - DeprecationWarning, - ) - - assert date_col in data.columns, f"{date_col} not in data.columns" - assert jhu_col in data.columns, f"{jhu_col} not in data.columns" - if not count_cols: - count_cols = list(set(data.columns) - {date_col, jhu_col}) - else: - count_cols = list(count_cols) - data = data[[jhu_col, date_col] + count_cols].copy() - data = self.convert_jhu_uid_to_fips(data, jhu_col=jhu_col, fips_col=fips_col) - data.dropna(subset=[fips_col], axis=0, inplace=True) - data[count_cols] = data[count_cols].multiply(data["weight"], axis=0) - data.drop([jhu_col, "weight"], axis=1, inplace=True) - data = data.groupby([date_col, fips_col]).sum() - return data.reset_index() - - def fips_to_zip( - self, data, fips_col="fips", date_col="date", count_cols=None, zip_col="zip" - ): - """DEPRECATED - Convert and aggregate from fips to zip - - Parameters - --------- - data: pd.DataFrame input data - fips_col: fips (county) column to convert - date_col: date column (is not aggregated) - count_cols: the count data columns to aggregate, if None (default) all non data/geo are used - zip_col: msa column to create - - Return - --------- - data: copy of dataframe + Returns + ------- + Set of geo values, all in string format. """ - warnings.warn( - "Use the function replace_geocode(df, 'fips', 'zip', ...) instead.", - DeprecationWarning, - ) - - if not count_cols: - count_cols = list(set(data.columns) - {date_col, fips_col}) + if self.geo_lists[geo_type]: + return self.geo_lists[geo_type] else: - count_cols = list(count_cols) - data = self.convert_fips_to_zip(data, fips_col=fips_col, zip_col=zip_col) - data.drop(fips_col, axis=1, inplace=True) - data[count_cols] = data[count_cols].multiply(data["weight"], axis=0) - data.drop("weight", axis=1, inplace=True) - data = data.groupby([date_col, zip_col]).sum() - return data.reset_index() - - def convert_fips_to_hrr(self, data, fips_col="fips", hrr_col="hrr"): - """DEPRECATED - convert and aggregate from fips to hrr - - Parameters - --------- - data: pd.DataFrame input data - fips_col: fips (county) column to convert - date_col: date column (is not aggregated) - count_cols: the count data columns to aggregate, if None (default) all - non data/geo are used - hrr_col: hrr column to create - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function add_geocode(df, 'fips', 'hrr', ...) instead.", - DeprecationWarning, - ) - - data = self.convert_fips_to_zip( - data, - fips_col=fips_col, - zip_col="zip", - ) - data = self.convert_zip_to_hrr( - data, - zip_col="zip", - hrr_col=hrr_col, - ) - data.drop(columns="zip") - data = data.groupby([fips_col, hrr_col]).sum().reset_index() - return data - - def fips_to_hrr( - self, data, fips_col="fips", date_col="date", count_cols=None, hrr_col="hrr" - ): - """DEPRECATED - convert and aggregate from fips to hrr - - Parameters - --------- - data: pd.DataFrame input data - fips_col: fips (county) column to convert - date_col: date column (is not aggregated) - count_cols: the count data columns to aggregate, if None (default) all - non data/geo are used - hrr_col: hrr column to create - - Return - --------- - data: copy of dataframe - """ - warnings.warn( - "Use the function replace_geocode(df, 'fips', 'hrr', ...) instead.", - DeprecationWarning, - ) - - zip_col = "_zip_col_temp" - data = self.fips_to_zip( - data, - fips_col=fips_col, - zip_col=zip_col, - date_col=date_col, - count_cols=count_cols, - ) - data = self.zip_to_hrr( - data, - zip_col=zip_col, - date_col=date_col, - count_cols=count_cols, - hrr_col=hrr_col, - ) - return data + from_code = "fips" + if geo_type.startswith("state"): + to_code = from_code = "state" + elif geo_type == "fips": + to_code = "pop" + else: + to_code = geo_type + stream = pkg_resources.resource_stream( + __name__, self.crosswalk_filepaths[from_code][to_code] + ) + crosswalk = pd.read_csv(stream, dtype=str) + self.geo_lists[geo_type] = set(crosswalk[geo_type]) + return self.geo_lists[geo_type] diff --git a/_delphi_utils_python/delphi_utils/logger.py b/_delphi_utils_python/delphi_utils/logger.py new file mode 100644 index 000000000..07382eaf0 --- /dev/null +++ b/_delphi_utils_python/delphi_utils/logger.py @@ -0,0 +1,69 @@ +"""Structured logger utility for creating JSON logs in Delphi pipelines.""" +import logging +import structlog + +def get_structured_logger(name = __name__, filename = None): + """Create a new structlog logger. + + Use the logger returned from this in indicator code using the standard + wrapper calls, e.g.: + + logger = get_structured_logger(__name__) + logger.warning("Error", type="Signal too low"). + + The output will be rendered as JSON which can easily be consumed by logs + processors. + + See the structlog documentation for details. + + Parameters + --------- + name: Name to use for logger (included in log lines), __name__ from caller + is a good choice. + filename: An (optional) file to write log output. + """ + # Configure the underlying logging configuration + handlers = [logging.StreamHandler()] + if filename: + handlers.append(logging.FileHandler(filename)) + + logging.basicConfig( + format="%(message)s", + level=logging.INFO, + handlers=handlers + ) + + # Configure structlog. This uses many of the standard suggestions from + # the structlog documentation. + structlog.configure( + processors=[ + # Filter out log levels we are not tracking. + structlog.stdlib.filter_by_level, + # Include logger name in output. + structlog.stdlib.add_logger_name, + # Include log level in output. + structlog.stdlib.add_log_level, + # Allow formatting into arguments e.g., logger.info("Hello, %s", + # name) + structlog.stdlib.PositionalArgumentsFormatter(), + # Add timestamps. + structlog.processors.TimeStamper(fmt="iso"), + # Match support for exception logging in the standard logger. + structlog.processors.StackInfoRenderer(), + structlog.processors.format_exc_info, + # Decode unicode characters + structlog.processors.UnicodeDecoder(), + # Render as JSON + structlog.processors.JSONRenderer() + ], + # Use a dict class for keeping track of data. + context_class=dict, + # Use a standard logger for the actual log call. + logger_factory=structlog.stdlib.LoggerFactory(), + # Use a standard wrapper class for utilities like log.warning() + wrapper_class=structlog.stdlib.BoundLogger, + # Cache the logger + cache_logger_on_first_use=True, + ) + + return structlog.get_logger(name) diff --git a/_delphi_utils_python/delphi_utils/signal.py b/_delphi_utils_python/delphi_utils/signal.py new file mode 100644 index 000000000..298c87bc3 --- /dev/null +++ b/_delphi_utils_python/delphi_utils/signal.py @@ -0,0 +1,56 @@ +"""Functions for understanding and creating signal names.""" +import covidcast + +def add_prefix(signal_names, wip_signal, prefix="wip_"): + """Add prefix to signal if there is a WIP signal. + + Parameters + ---------- + signal_names: List[str] + Names of signals to be exported + prefix : "wip_" + prefix for new/non public signals + wip_signal : List[str] or bool + a list of wip signals: [], OR + all signals in the registry: True OR + only signals that have never been published: False + Returns + ------- + List of signal names + wip/non wip signals for further computation + """ + if wip_signal is True: + return [prefix + signal for signal in signal_names] + if isinstance(wip_signal, list): + make_wip = set(wip_signal) + return [ + (prefix if signal in make_wip else "") + signal + for signal in signal_names + ] + if wip_signal in {False, ""}: + return [ + signal if public_signal(signal) + else prefix + signal + for signal in signal_names + ] + raise ValueError("Supply True | False or '' or [] | list()") + + +def public_signal(signal): + """Check if the signal name is already public using COVIDcast. + + Parameters + ---------- + signal : str + Name of the signal + Returns + ------- + bool + True if the signal is present + False if the signal is not present + """ + epidata_df = covidcast.metadata() + for index in range(len(epidata_df)): + if epidata_df["signal"][index] == signal: + return True + return False diff --git a/_delphi_utils_python/delphi_utils/slack_notifier.py b/_delphi_utils_python/delphi_utils/slack_notifier.py new file mode 100644 index 000000000..069b73459 --- /dev/null +++ b/_delphi_utils_python/delphi_utils/slack_notifier.py @@ -0,0 +1,17 @@ +"""Slack notifier for use in Delphi indicators.""" +from slack import WebClient + +class SlackNotifier: + """Notifies slack channels of messages.""" + + def __init__(self, slack_channel, slack_token): + """Initialize with a slack channel and token.""" + self.slack_channel = slack_channel + self.client = WebClient(token = slack_token) + + def post_message(self, blocks): + """Post a message to the slack channel.""" + self.client.chat_postMessage( + channel=self.slack_channel, + blocks=blocks + ) diff --git a/_delphi_utils_python/delphi_utils/smooth.py b/_delphi_utils_python/delphi_utils/smooth.py new file mode 100644 index 000000000..3590a5bc4 --- /dev/null +++ b/_delphi_utils_python/delphi_utils/smooth.py @@ -0,0 +1,492 @@ +"""Smoother utility. + +This file contains the smoothing utility functions. We have a number of +possible smoothers to choose from: windowed average, local weighted regression, +and a causal Savitzky-Golay filter. + +Code is courtesy of Dmitry Shemetov, Maria Jahja, and Addison Hu. + +These smoothers are all functions that take a 1D numpy array and return a smoothed +1D numpy array of the same length (with a few np.nans in the beginning). See the +docstrings for details. +""" + +from typing import Union +import warnings + +import numpy as np +import pandas as pd + + +class Smoother: # pylint: disable=too-many-instance-attributes + """Smoother class. + + This is the smoothing utility class. This class holds the parameter settings for its smoother + methods and provides reasonable defaults. Basic usage can be found in the examples below. + + The smoother function takes numpy arrays or pandas Series as input, expecting the values to be + on a regularly-spaced time grid. NANs are ok, as long as the array does not begin with a NAN. + The rest of the NANs will be handled via imputation by default, though this can be turned off. + + Parameters + ---------- + smoother_name: {'savgol', 'moving_average', 'identity', 'left_gauss_linear'} + This variable specifies the smoother. We have four smoothers, currently: + * 'savgol' or a Savtizky-Golay smoother (default) + * 'moving_average' or a moving window average smoother + * 'identity' or the trivial smoother (no smoothing) + * 'left_gauss_linear' or a Gaussian-weight linear regression smoother + Descriptions of the smoothers are available in the doc strings. Full mathematical + details are in: https://github.com/cmu-delphi/covidcast-modeling/ in the folder + 'indicator_smoother'. + poly_fit_degree: int + A parameter for the 'savgol' smoother which sets the degree of the polynomial fit. + window_length: int + The length of the fitting window for 'savgol' and the averaging window 'moving_average'. + This value is in the units provided by the data, which are likely to be days for Delphi. + Note that if window_length is smaller than the length of the signal, then only the + imputation method is run on the signal. + gaussian_bandwidth: float or None + If float, all regression is done with Gaussian weights whose variance is + half the gaussian_bandwidth. If None, performs unweighted regression. (Applies + to 'left_gauss_linear' and 'savgol'.) + Here are some reference values (the given bandwidth produces a 95% weighting on + the data of length time window into the past): + time window | bandwidth + 7 36 + 14 144 + 21 325 + 28 579 + 35 905 + 42 1303 + impute: {'savgol', 'zeros', None} + If 'savgol' (default), will fill nan values with a savgol fit on the largest available time + window prior (up to window_length). If 'zeros', will fill nan values with zeros. + If None, leaves the nans in place. + minval: float or None + The smallest value to allow in a signal. If None, there is no smallest value. + Currently only implemented for 'left_gauss_linear'. This should probably not be in the scope + of the smoothing utility. + boundary_method: {'shortened_window', 'identity', 'nan'} + Determines how the 'savgol' method handles smoothing at the (left) boundary, where the past + data length is shorter than the window_length parameter. If 'shortened_window', it uses the + maximum window available; at the very edge (generally up to poly_fit_degree) it keeps the + same value as the raw signal. If 'identity', it just keeps the raw signal. If 'nan', it + writes nans. For the other smoothing methods, 'moving_average' writes nans and + 'left_gauss_linear' uses a shortened window. + + Methods + ---------- + smooth: np.ndarray or pd.Series + Takes a 1D signal and returns a smoothed version. + The input and the output have the same length and type. + + Example Usage + ------------- + Example 1. Apply a rolling average smoother with a window of length 10. + >>> smoother = Smoother(smoother_name='moving_average', window_length=10) + >>> smoothed_signal = smoother.smooth(signal) + + Example 2. Smooth a dataframe column. + >>> smoother = Smoother(smoother_name='savgol') + >>> df[col] = df[col].transform(smoother.smooth) + + Example 3. Apply a rolling weighted average smoother, with 95% weight on the recent 2 weeks and + a sharp cutoff after 4 weeks. + >>> smoother = Smoother(smoother_name='savgol', poly_fit_degree=0, window_length=28, + gaussian_bandwidth=144) + >>> smoothed_signal = smoother.smooth(signal) + + Example 4. Apply a local linear regression smoother (essentially equivalent to + `left_gauss_linear`), with 95% weight on the recent week and a sharp + cutoff after 3 weeks. + >>> smoother = Smoother(smoother_name='savgol', poly_fit_degree=1, window_length=21, + gaussian_bandwidth=36) + >>> smoothed_signal = smoother.smooth(signal) + + Example 5. Apply the identity function (simplifies code that iterates through smoothers _and_ + expects a copy of the raw data). + >>> smoother = Smoother(smoother_name='identity') + >>> smoothed_signal = smoother.smooth(signal) + """ + + def __init__( + self, + smoother_name="savgol", + poly_fit_degree=2, + window_length=28, + gaussian_bandwidth=144, # a ~2 week window + impute_method="savgol", + minval=None, + boundary_method="shortened_window", + ): + """See class docstring.""" + self.smoother_name = smoother_name + self.poly_fit_degree = poly_fit_degree + self.window_length = window_length + self.gaussian_bandwidth = gaussian_bandwidth + self.impute_method = impute_method + self.minval = minval + self.boundary_method = boundary_method + + valid_smoothers = {"savgol", "left_gauss_linear", "moving_average", "identity"} + valid_impute_methods = {"savgol", "zeros", None} + valid_boundary_methods = {"shortened_window", "identity", "nan"} + if self.smoother_name not in valid_smoothers: + raise ValueError("Invalid smoother_name given.") + if self.impute_method not in valid_impute_methods: + raise ValueError("Invalid impute_method given.") + if self.boundary_method not in valid_boundary_methods: + raise ValueError("Invalid boundary_method given.") + if self.window_length <= 1: + raise ValueError("Window length is too short.") + + if smoother_name == "savgol": + # The polynomial fitting is done on a past window of size window_length + # including the current day value. + self.coeffs = self.savgol_coeffs( + -self.window_length + 1, 0, self.poly_fit_degree + ) + else: + self.coeffs = None + + def smooth( + self, signal: Union[np.ndarray, pd.Series], impute_order=2 + ) -> Union[np.ndarray, pd.Series]: + """Apply a smoother to a signal. + + The major workhorse smoothing function. Imputes the nans and then applies + a smoother to the signal. + + Parameters + ---------- + signal: np.ndarray or pd.Series + A 1D signal to be smoothed. + impute_order: int + The polynomial order of the fit used for imputation. By default, this is set to + 2. + + Returns + ---------- + signal_smoothed: np.ndarray or pd.Series + A smoothed 1D signal. Returns an array of the same type and length as + the input. + """ + # If all nans, pass through + if np.all(np.isnan(signal)): + return signal + + is_pandas_series = isinstance(signal, pd.Series) + pandas_index = signal.index if is_pandas_series else None + signal = signal.to_numpy() if is_pandas_series else signal + + # Find where the first non-nan value is located and truncate the initial nans + ix = np.where(~np.isnan(signal))[0][0] + signal = signal[ix:] + + # Don't smooth in certain edge cases + if len(signal) < self.poly_fit_degree or len(signal) == 1: + signal_smoothed = signal.copy() + else: + # Impute + signal = self.impute(signal, impute_order=impute_order) + + # Smooth + if self.smoother_name == "savgol": + signal_smoothed = self.savgol_smoother(signal) + elif self.smoother_name == "left_gauss_linear": + signal_smoothed = self.left_gauss_linear_smoother(signal) + elif self.smoother_name == "moving_average": + signal_smoothed = self.moving_average_smoother(signal) + elif self.smoother_name == "identity": + signal_smoothed = signal + + # Append the nans back, since we want to preserve length + signal_smoothed = np.hstack([np.nan * np.ones(ix), signal_smoothed]) + # Convert back to pandas if necessary + if is_pandas_series: + signal_smoothed = pd.Series(signal_smoothed) + signal_smoothed.index = pandas_index + return signal_smoothed + + def impute(self, signal, impute_order=2): + """Impute the nan values in the signal. + + See the class docstring for an explanation of the impute methods. + + Parameters + ---------- + signal: np.ndarray + 1D signal to be imputed. + impute_order: int + The polynomial order of the fit used for imputation. + + Returns + ------- + imputed_signal: np.ndarray + Imputed signal. + """ + if self.impute_method == "savgol": + # We cannot impute if the signal begins with a NaN (there is no information to go by). + # To preserve input-output array lengths, this util will not drop NaNs for you. + if np.isnan(signal[0]): + raise ValueError("The signal should not begin with a nan value.") + imputed_signal = self.savgol_impute(signal, impute_order) + elif self.impute_method == "zeros": + imputed_signal = np.nan_to_num(signal) + elif self.impute_method is None: + imputed_signal = np.copy(signal) + + return imputed_signal + + def moving_average_smoother(self, signal): + """Compute a moving average on the signal. + + Parameters + ---------- + signal: np.ndarray + Input array. + + Returns + ------- + signal_smoothed: np.ndarray + An array with the same length as arr, but the first window_length-1 + entries are np.nan. + """ + if not isinstance(self.window_length, int): + raise ValueError("k must be int.") + + signal_padded = np.append(np.nan * np.ones(self.window_length - 1), signal) + signal_smoothed = ( + np.convolve( + signal_padded, np.ones(self.window_length, dtype=int), mode="valid" + ) + / self.window_length + ) + + return signal_smoothed + + def left_gauss_linear_smoother(self, signal): + """Smooth the y-values using a local linear regression with Gaussian weights. + + DEPRECATED: This method is available to help sanity check the 'savgol' method. + Use 'savgol' with poly_fit_degree=1 and the appropriate gaussian_bandwidth instead. + + At each time t, we use the data from times 1, ..., t-dt, weighted + using the Gaussian kernel, to produce the estimate at time t. + + Parameters + ---------- + signal: np.ndarray + A 1D signal. + + Returns + ---------- + signal_smoothed: np.ndarray + A smoothed 1D signal. + """ + warnings.warn( + "Use the savgol smoother with poly_fit_degree=1 instead.", + DeprecationWarning, + ) + n = len(signal) + signal_smoothed = np.zeros_like(signal) + # A is the regression design matrix + A = np.vstack([np.ones(n), np.arange(n)]).T # pylint: disable=invalid-name + for idx in range(n): + weights = np.exp( + -((np.arange(idx + 1) - idx) ** 2) / self.gaussian_bandwidth + ) + AwA = np.dot( # pylint: disable=invalid-name + A[: (idx + 1), :].T * weights, A[: (idx + 1), :] + ) + Awy = np.dot( # pylint: disable=invalid-name + A[: (idx + 1), :].T * weights, signal[: (idx + 1)].reshape(-1, 1) + ) + try: + beta = np.linalg.solve(AwA, Awy) + signal_smoothed[idx] = np.dot(A[: (idx + 1), :], beta)[-1] + except np.linalg.LinAlgError: + signal_smoothed[idx] = ( + signal[idx] # pylint: disable=using-constant-test + if self.impute + else np.nan + ) + if self.minval is not None: + signal_smoothed[signal_smoothed <= self.minval] = self.minval + return signal_smoothed + + def savgol_predict(self, signal, poly_fit_degree, nr): + """Predict a single value using the savgol method. + + Fits a polynomial through the values given by the signal and returns the value + of the polynomial at the right-most signal-value. More precisely, for a signal of length + n, fits a poly_fit_degree polynomial through the points signal[-n+1+nr], signal[-n+2+nr], + ..., signal[nr], and returns the evaluation of the polynomial at signal[0]. Hence, if + nr=0, then the last value of the signal is smoothed, and if nr=-1, then the value after + the last signal value is anticipated. + + Parameters + ---------- + signal: np.ndarray + A 1D signal to smooth. + poly_fit_degree: int + The degree of the polynomial fit. + nr: int + An integer that determines the position of the predicted value relative to the signal. + + Returns + ---------- + predicted_value: float + The anticipated value that comes after the end of the signal based on a polynomial fit. + """ + coeffs = self.savgol_coeffs(-len(signal) + 1 + nr, nr, poly_fit_degree) + predicted_value = signal @ coeffs + return predicted_value + + def savgol_coeffs(self, nl, nr, poly_fit_degree): + """Solve for the Savitzky-Golay coefficients. + + Solves for the Savitzky-Golay coefficients. The coefficients c_i + give a filter so that + y = sum_{i=-{n_l}}^{n_r} c_i x_i + is the value at 0 (thus the constant term) of the polynomial fit + through the points {x_i}. The coefficients are c_i are calculated as + c_i = ((A.T @ A)^(-1) @ (A.T @ e_i))_0 + where A is the design matrix of the polynomial fit and e_i is the standard + basis vector i. This is currently done via a full inversion, which can be + optimized. + + Parameters + ---------- + nl: int + The left window bound for the polynomial fit, inclusive. + nr: int + The right window bound for the polynomial fit, inclusive. + poly_fit_degree: int + The degree of the polynomial to be fit. + + Returns + ---------- + coeffs: np.ndarray + A vector of coefficients of length nr - nl + 1 that determines the savgol + convolution filter. + """ + if nl >= nr: + raise ValueError("The left window bound should be less than the right.") + if nr > 0: + warnings.warn("The filter is no longer causal.") + + A = np.vstack( # pylint: disable=invalid-name + [np.arange(nl, nr + 1) ** j for j in range(poly_fit_degree + 1)] + ).T + + if self.gaussian_bandwidth is None: + mat_inverse = np.linalg.inv(A.T @ A) @ A.T + else: + weights = np.exp(-((np.arange(nl, nr + 1)) ** 2) / self.gaussian_bandwidth) + mat_inverse = np.linalg.inv((A.T * weights) @ A) @ (A.T * weights) + window_length = nr - nl + 1 + coeffs = np.zeros(window_length) + for i in range(window_length): + basis_vector = np.zeros(window_length) + basis_vector[i] = 1.0 + coeffs[i] = (mat_inverse @ basis_vector)[0] + return coeffs + + def savgol_smoother(self, signal): # pylint: disable=inconsistent-return-statements + """Smooth signal with the savgol smoother. + + Returns a convolution of the 1D signal with the Savitzky-Golay coefficients, respecting + boundary effects. For an explanation of boundary effects methods, see the class docstring. + + Parameters + ---------- + signal: np.ndarray + A 1D signal. + + Returns + ---------- + signal_smoothed: np.ndarray + A smoothed 1D signal of same length as signal. + """ + # Reverse because np.convolve reverses the second argument + temp_reversed_coeffs = np.array(list(reversed(self.coeffs))) + + # Smooth the part of the signal away from the boundary first + signal_padded = np.append(np.nan * np.ones(len(self.coeffs) - 1), signal) + signal_smoothed = np.convolve(signal_padded, temp_reversed_coeffs, mode="valid") + + # This section handles the smoothing behavior at the (left) boundary: + # - shortened_window (default) applies savgol with a smaller window to do the fit + # - identity keeps the original signal (doesn't smooth) + # - nan writes nans + if self.boundary_method == "shortened_window": # pylint: disable=no-else-return + for ix in range(min(len(self.coeffs), len(signal))): + if ix == 0: + signal_smoothed[ix] = signal[ix] + else: + # At the very edge, the design matrix is often singular, in which case + # we just fall back to the raw signal + try: + signal_smoothed[ix] = self.savgol_predict( + signal[: ix + 1], self.poly_fit_degree, 0 + ) + except np.linalg.LinAlgError: # for small ix, the design matrix is singular + signal_smoothed[ix] = signal[ix] + return signal_smoothed + elif self.boundary_method == "identity": + for ix in range(min(len(self.coeffs), len(signal))): + signal_smoothed[ix] = signal[ix] + return signal_smoothed + elif self.boundary_method == "nan": + return signal_smoothed + + def savgol_impute(self, signal, impute_order): + """Impute the nan values in signal using savgol. + + This method fills the nan values in the signal with polynomial interpolation + on a rolling window of the immediate past up to window_length data points. + + A number of boundary cases are handled involving nan filling close to the boundary. + + Note that in the case of many adjacent nans, the method will use previously + imputed values to do the fitting for later values. + + Parameters + ---------- + signal: np.ndarray + A 1D signal to be imputed. + impute_order: int + The polynomial order of the fit used for imputation. + + Returns + ---------- + signal_imputed: np.ndarray + An imputed 1D signal. + """ + if impute_order > self.window_length: + raise ValueError("Impute order must be smaller than window length.") + + signal_imputed = np.copy(signal) + for ix in np.where(np.isnan(signal_imputed))[0]: + # Boundary cases + if ix < self.window_length: + # At the boundary, a single value should just be extended + if ix == 1: + signal_imputed[ix] = signal_imputed[ix - 1] + # Otherwise, use savgol fitting on the largest window prior, + # reduce the polynomial degree if needed (can't fit if the + # imputation order is larger than the available data) + else: + signal_imputed[ix] = self.savgol_predict( + signal_imputed[:ix], min(ix - 1, impute_order), -1 + ) + # Away from the boundary, use savgol fitting on a fixed window + else: + signal_imputed[ix] = self.savgol_predict( + signal_imputed[ix - self.window_length : ix], + impute_order, + -1, + ) + return signal_imputed diff --git a/_delphi_utils_python/delphi_utils/utils.py b/_delphi_utils_python/delphi_utils/utils.py index c6023e4a4..9baa4f85b 100644 --- a/_delphi_utils_python/delphi_utils/utils.py +++ b/_delphi_utils_python/delphi_utils/utils.py @@ -1,10 +1,11 @@ +"""Read parameter files containing configuration information.""" # -*- coding: utf-8 -*- from json import load from os.path import exists from shutil import copyfile def read_params(): - """Reads a file named 'params.json' in the current working directory. + """Read a file named 'params.json' in the current working directory. If the file does not exist, it copies the file 'params.json.template' to 'param.json' and then reads the file. diff --git a/_delphi_utils_python/setup.py b/_delphi_utils_python/setup.py index f3e428088..28793d4e4 100644 --- a/_delphi_utils_python/setup.py +++ b/_delphi_utils_python/setup.py @@ -3,18 +3,23 @@ required = [ "boto3", + "covidcast", "gitpython", "moto", "numpy", "pandas>=1.1.0", + "pydocstyle", + "pylint", "pytest", "pytest-cov", + "slackclient", + "structlog", "xlrd" ] setup( name="delphi_utils", - version="0.0.1", + version="0.1.0", description="Shared Utility Functions for Indicators", author="", author_email="", diff --git a/_delphi_utils_python/tests/test_dir/.gitignore b/_delphi_utils_python/tests/test_dir/.gitignore new file mode 100644 index 000000000..afed0735d --- /dev/null +++ b/_delphi_utils_python/tests/test_dir/.gitignore @@ -0,0 +1 @@ +*.csv diff --git a/_delphi_utils_python/tests/test_export.py b/_delphi_utils_python/tests/test_export.py index 1bde9c060..b72799d28 100644 --- a/_delphi_utils_python/tests/test_export.py +++ b/_delphi_utils_python/tests/test_export.py @@ -1,5 +1,4 @@ -import pytest - +"""Tests for exporting CSV files.""" from datetime import datetime from os import listdir, remove from os.path import join @@ -7,41 +6,210 @@ import pandas as pd from delphi_utils import create_export_csv +def _clean_directory(directory): + """Clean files out of a directory.""" + for fname in listdir(directory): + if fname.startswith("."): + continue + remove(join(directory, fname)) + + +def _non_ignored_files_set(directory): + """List all files in a directory not preceded by a '.' and store them in a set.""" + out = set() + for fname in listdir(directory): + if fname.startswith("."): + continue + out.add(fname) + return out + class TestExport: - def test_export_csv(self): + """Tests for exporting CSVs.""" + # List of times for data points. + TIMES = [ + datetime.strptime(x, "%Y-%m-%d") + for x in ["2020-02-15", "2020-02-15", "2020-03-01", "2020-03-15"] + ] + + # A sample data frame. + DF = pd.DataFrame( + { + "geo_id": ["51093", "51175", "51175", "51620"], + "timestamp": TIMES, + "val": [3.6, 2.1, 2.2, 2.6], + "se": [0.15, 0.22, 0.20, 0.34], + "sample_size": [100, 100, 101, 100], + } + ) + + # Directory in which to store tests. + TEST_DIR = "test_dir" + + def test_export_with_metric(self): + """Test that exporting CSVs with the `metrics` argument yields the correct files.""" # Clean receiving directory - for fname in listdir("test_dir"): - remove(join("test_dir", fname)) - - times = [ - datetime.strptime(x, "%Y-%m-%d") - for x in ["2020-02-15", "2020-02-15", "2020-03-01", "2020-03-15"] - ] - df = pd.DataFrame( - { - "geo_id": ["51093", "51175", "51175", "51620"], - "timestamp": times, - "val": [3.6, 2.1, 2.2, 2.6], - "se": [0.15, 0.22, 0.20, 0.34], - "sample_size": [100, 100, 101, 100], - } - ) + _clean_directory(self.TEST_DIR) create_export_csv( - df=df, + df=self.DF, start_date=datetime.strptime("2020-02-15", "%Y-%m-%d"), - export_dir="test_dir", + export_dir=self.TEST_DIR, metric="deaths", geo_res="county", sensor="test", ) - assert set(listdir("test_dir")) == set( + assert _non_ignored_files_set(self.TEST_DIR) == set( [ "20200215_county_deaths_test.csv", "20200301_county_deaths_test.csv", "20200315_county_deaths_test.csv", ] ) + + def test_export_without_metric(self): + """Test that exporting CSVs without the `metrics` argument yields the correct files.""" + + # Clean receiving directory + _clean_directory(self.TEST_DIR) + + create_export_csv( + df=self.DF, + start_date=datetime.strptime("2020-02-15", "%Y-%m-%d"), + export_dir=self.TEST_DIR, + geo_res="county", + sensor="test", + ) + + assert _non_ignored_files_set(self.TEST_DIR) == set( + [ + "20200215_county_test.csv", + "20200301_county_test.csv", + "20200315_county_test.csv", + ] + ) + + def test_export_with_limiting_start_date(self): + """Test that the `start_date` prevents earlier dates from being exported.""" + + # Clean receiving directory + _clean_directory(self.TEST_DIR) + + create_export_csv( + df=self.DF, + start_date=datetime.strptime("2020-02-20", "%Y-%m-%d"), + export_dir=self.TEST_DIR, + geo_res="county", + sensor="test", + ) + + assert _non_ignored_files_set(self.TEST_DIR) == set( + [ + "20200301_county_test.csv", + "20200315_county_test.csv", + ] + ) + + def test_export_with_limiting_end_date(self): + """Test that the `end_date` prevents later dates from being exported.""" + + # Clean receiving directory + _clean_directory(self.TEST_DIR) + + create_export_csv( + df=self.DF, + end_date=datetime.strptime("2020-03-07", "%Y-%m-%d"), + export_dir=self.TEST_DIR, + geo_res="county", + sensor="test", + ) + + assert _non_ignored_files_set(self.TEST_DIR) == set( + [ + "20200215_county_test.csv", + "20200301_county_test.csv", + ] + ) + + def test_export_with_no_dates(self): + """Test that omitting the `start_date` and `end_date` exports all dates.""" + + # Clean receiving directory + _clean_directory(self.TEST_DIR) + + create_export_csv( + df=self.DF, + export_dir=self.TEST_DIR, + geo_res="state", + sensor="test", + ) + + assert _non_ignored_files_set(self.TEST_DIR) == set( + [ + "20200215_state_test.csv", + "20200301_state_test.csv", + "20200315_state_test.csv", + ] + ) + + def test_export_with_null_removal(self): + """Test that `remove_null_samples = True` removes entries with null samples.""" + _clean_directory(self.TEST_DIR) + + df_with_nulls = self.DF.copy().append({ + "geo_id": "66666", + "timestamp": datetime(2020, 6, 6), + "val": 10, + "se": 0.2, + "sample_size": pd.NA}, + ignore_index=True) + + create_export_csv( + df=df_with_nulls, + export_dir=self.TEST_DIR, + geo_res="state", + sensor="test", + remove_null_samples=True + ) + + assert _non_ignored_files_set(self.TEST_DIR) == set( + [ + "20200215_state_test.csv", + "20200301_state_test.csv", + "20200315_state_test.csv", + "20200606_state_test.csv" + ] + ) + assert pd.read_csv(join(self.TEST_DIR, "20200606_state_test.csv")).size == 0 + + def test_export_without_null_removal(self): + """Test that `remove_null_samples = False` does not remove entries with null samples.""" + _clean_directory(self.TEST_DIR) + + df_with_nulls = self.DF.copy().append({ + "geo_id": "66666", + "timestamp": datetime(2020, 6, 6), + "val": 10, + "se": 0.2, + "sample_size": pd.NA}, + ignore_index=True) + + create_export_csv( + df=df_with_nulls, + export_dir=self.TEST_DIR, + geo_res="state", + sensor="test", + remove_null_samples=False + ) + + assert _non_ignored_files_set(self.TEST_DIR) == set( + [ + "20200215_state_test.csv", + "20200301_state_test.csv", + "20200315_state_test.csv", + "20200606_state_test.csv" + ] + ) + assert pd.read_csv(join(self.TEST_DIR, "20200606_state_test.csv")).size > 0 diff --git a/_delphi_utils_python/tests/test_geomap.py b/_delphi_utils_python/tests/test_geomap.py index 71a619351..36ae4b85a 100644 --- a/_delphi_utils_python/tests/test_geomap.py +++ b/_delphi_utils_python/tests/test_geomap.py @@ -137,6 +137,9 @@ def test_crosswalks(self): # assert cw.groupby("zip")["weight"].sum().round(5).eq(1.0).all() cw = gmpr._load_crosswalk(from_code="zip", to_code="state") assert cw.groupby("zip")["weight"].sum().round(5).eq(1.0).all() + cw = gmpr._load_crosswalk(from_code="zip", to_code="hhs") + assert cw.groupby("zip")["weight"].sum().round(5).eq(1.0).all() + def test_load_zip_fips_table(self): gmpr = GeoMapper() @@ -160,7 +163,7 @@ def test_load_fips_msa_table(self): def test_load_jhu_uid_fips_table(self): gmpr = GeoMapper() jhu_data = gmpr._load_crosswalk(from_code="jhu_uid", to_code="fips") - assert (jhu_data.groupby("jhu_uid").sum() == 1).all()[0] + assert np.allclose(jhu_data.groupby("jhu_uid").sum(), 1.0) def test_load_zip_hrr_table(self): gmpr = GeoMapper() @@ -168,48 +171,6 @@ def test_load_zip_hrr_table(self): assert pd.api.types.is_string_dtype(zip_data["zip"]) assert pd.api.types.is_string_dtype(zip_data["hrr"]) - def test_convert_fips_to_state_code(self): - gmpr = GeoMapper() - new_data = gmpr.convert_fips_to_state_code(self.fips_data) - assert new_data["state_code"].dtype == "O" - assert new_data.loc[1, "state_code"] == new_data.loc[1, "fips"][:2] - - def test_fips_to_state_code(self): - gmpr = GeoMapper() - new_data = gmpr.fips_to_state_code(self.fips_data_3) - assert np.allclose(new_data["count"].sum(), self.fips_data_3["count"].sum()) - - def test_convert_state_code_to_state_id(self): - gmpr = GeoMapper() - new_data = gmpr.convert_fips_to_state_code(self.fips_data) - new_data = gmpr.convert_state_code_to_state_id(new_data) - assert new_data["state_id"].isnull()[2] - assert new_data["state_id"][3] == "in" - assert len(pd.unique(new_data["state_id"])) == 4 - - def test_fips_to_state_id(self): - gmpr = GeoMapper() - new_data = gmpr.fips_to_state_id(self.fips_data_2) - assert new_data["state_id"][2] == "in" - assert new_data.shape[0] == 3 - assert new_data["count"].sum() == self.fips_data_2["count"].sum() - - def test_fips_to_msa(self): - gmpr = GeoMapper() - new_data = gmpr.fips_to_msa(self.fips_data_3) - assert new_data.shape[0] == 2 - assert new_data["msa"][0] == "10180" - new_data = gmpr.fips_to_msa(self.fips_data_3, create_mega=True) - assert new_data[["count"]].sum()[0] == self.fips_data_3["count"].sum() - - def test_zip_to_fips(self): - gmpr = GeoMapper() - new_data = gmpr.zip_to_fips(self.zip_data) - assert new_data.shape[0] == 10 - assert ( - new_data[["count", "total"]].sum() - self.zip_data[["count", "total"]].sum() - ).sum() < 1e-3 - def test_megacounty(self): gmpr = GeoMapper() new_data = gmpr.fips_to_megacounty(self.mega_data, 6, 50) @@ -228,53 +189,6 @@ def test_megacounty(self): new_data[["count"]].sum() - self.mega_data[["count"]].sum() ).sum() < 1e-3 - def test_zip_to_hrr(self): - gmpr = GeoMapper() - new_data = gmpr.zip_to_hrr(self.zip_data) - assert len(pd.unique(new_data["hrr"])) == 2 - assert np.allclose( - new_data[["count", "total"]].sum(), self.zip_data[["count", "total"]].sum() - ) - - def test_jhu_uid_to_fips(self): - gmpr = GeoMapper() - new_data = gmpr.jhu_uid_to_fips(self.jhu_uid_data) - assert not (new_data["fips"].astype(int) > 90000).any() - assert new_data["total"].sum() == self.jhu_uid_data["total"].sum() - - def test_fips_to_zip(self): - gmpr = GeoMapper() - new_data = gmpr.fips_to_zip(self.fips_data_4) - assert new_data["count"].sum() == self.fips_data_4["count"].sum() - - def test_fips_to_hrr(self): - gmpr = GeoMapper() - data = gmpr.convert_fips_to_hrr(self.fips_data_3) - ind = self.fips_data_3["fips"].isin(data["fips"]) - data = self.fips_data_3[ind] - new_data = gmpr.fips_to_hrr(self.fips_data_3) - assert new_data.shape == (2, 4) - assert new_data["count"].sum() == data["count"].sum() - - def test_zip_to_msa(self): - gmpr = GeoMapper() - new_data = gmpr.zip_to_msa(self.zip_data) - assert new_data["msa"][2] == "46700" - assert new_data.shape[0] == 6 - assert np.allclose(new_data["count"].sum(), self.zip_data["count"].sum()) - - def test_zip_to_state_code(self): - gmpr = GeoMapper() - new_data = gmpr.zip_to_state_code(self.zip_data) - assert new_data.shape[0] == 4 - assert np.allclose(new_data["count"].sum(), self.zip_data["count"].sum()) - - def test_zip_to_state_id(self): - gmpr = GeoMapper() - new_data = gmpr.zip_to_state_id(self.zip_data) - assert new_data.shape[0] == 4 - assert np.allclose(new_data["count"].sum(), self.zip_data["count"].sum()) - def test_add_population_column(self): gmpr = GeoMapper() new_data = gmpr.add_population_column(self.fips_data_3, "fips") @@ -289,100 +203,10 @@ def test_add_population_column(self): def test_add_geocode(self): gmpr = GeoMapper() - # fips -> zip - new_data = gmpr.fips_to_zip(self.fips_data_3) - new_data2 = gmpr.replace_geocode(self.fips_data_3, "fips", "zip") - assert new_data.equals(new_data2) - - # fips -> hrr - new_data = gmpr.fips_to_hrr(self.fips_data_3) - new_data2 = gmpr.replace_geocode(self.fips_data_3, "fips", "hrr") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # fips -> msa - new_data = gmpr.fips_to_msa(self.fips_data_3) - new_data2 = gmpr.replace_geocode(self.fips_data_3, "fips", "msa") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # fips -> state_id - new_data = gmpr.fips_to_state_id(self.fips_data_4) - new_data2 = gmpr.replace_geocode(self.fips_data_4, "fips", "state_id") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # fips -> state_code - new_data = gmpr.fips_to_state_code(self.fips_data_4) - new_data2 = gmpr.replace_geocode(self.fips_data_4, "fips", "state_code") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # fips -> state_code (again, mostly to cover the test case of when fips - # codes aren't all strings) - new_data = gmpr.fips_to_state_code(self.fips_data_5) - new_data2 = gmpr.replace_geocode(self.fips_data_5, "fips", "state_code") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # zip -> fips - new_data = gmpr.zip_to_fips(self.zip_data) - new_data2 = gmpr.replace_geocode(self.zip_data, "zip", "fips") - new_data2 = new_data2[new_data.columns] - assert new_data.equals(new_data2) - - # zip -> hrr - new_data = gmpr.zip_to_hrr(self.zip_data) - new_data2 = gmpr.replace_geocode(self.zip_data, "zip", "hrr") - new_data2 = new_data2[new_data.columns] - assert new_data.equals(new_data2) - - # zip -> msa - new_data = gmpr.zip_to_msa(self.zip_data) - new_data2 = gmpr.replace_geocode(self.zip_data, "zip", "msa") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # zip -> state_id - new_data = gmpr.zip_to_state_id(self.zip_data) - new_data2 = gmpr.replace_geocode(self.zip_data, "zip", "state_id") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # zip -> state_code - new_data = gmpr.zip_to_state_code(self.zip_data) - new_data2 = gmpr.replace_geocode(self.zip_data, "zip", "state_code") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - - # jhu_uid -> fips - new_data = gmpr.jhu_uid_to_fips(self.jhu_uid_data) - new_data2 = gmpr.replace_geocode(self.jhu_uid_data, "jhu_uid", "fips") - new_data2 = new_data2[new_data.columns] - assert np.allclose( - new_data[["count", "total"]].values, new_data2[["count", "total"]].values - ) - # state_code -> hhs new_data = gmpr.add_geocode(self.zip_data, "zip", "state_code") - new_data2 = gmpr.add_geocode(new_data, "state_code", "hhs_region_number") - assert new_data2["hhs_region_number"].unique().size == 2 + new_data2 = gmpr.add_geocode(new_data, "state_code", "hhs") + assert new_data2["hhs"].unique().size == 2 # state_name -> state_id new_data = gmpr.replace_geocode(self.zip_data, "zip", "state_name") @@ -393,7 +217,8 @@ def test_add_geocode(self): # fips -> nation new_data = gmpr.replace_geocode(self.fips_data_5, "fips", "nation", new_col="NATION") - assert new_data.equals( + pd.testing.assert_frame_equal( + new_data, pd.DataFrame().from_dict( { "date": {0: pd.Timestamp("2018-01-01 00:00:00")}, @@ -406,7 +231,8 @@ def test_add_geocode(self): # zip -> nation new_data = gmpr.replace_geocode(self.zip_data, "zip", "nation") - assert new_data.equals( + pd.testing.assert_frame_equal( + new_data, pd.DataFrame().from_dict( { "date": { @@ -431,7 +257,8 @@ def test_add_geocode(self): # fips -> zip (date_col=None chech) new_data = gmpr.replace_geocode(self.fips_data_5.drop(columns=["date"]), "fips", "hrr", date_col=None) - assert new_data.equals( + pd.testing.assert_frame_equal( + new_data, pd.DataFrame().from_dict( { 'hrr': {0: '1', 1: '183', 2: '184', 3: '382', 4: '7'}, @@ -440,3 +267,41 @@ def test_add_geocode(self): } ) ) + + # fips -> hhs + new_data = gmpr.replace_geocode(self.fips_data_3.drop(columns=["date"]), + "fips", "hhs", date_col=None) + pd.testing.assert_frame_equal( + new_data, + pd.DataFrame().from_dict( + { + "hhs": {0: "2", 1: "6"}, + "count": {0: 12, 1: 6}, + "total": {0: 111, 1: 13} + } + ) + ) + + # zip -> hhs + new_data = gmpr.replace_geocode(self.zip_data, "zip", "hhs") + new_data = new_data.round(10) # get rid of a floating point error with 99.00000000000001 + pd.testing.assert_frame_equal( + new_data, + pd.DataFrame().from_dict( + { + "date": {0: pd.Timestamp("2018-01-01"), 1: pd.Timestamp("2018-01-01"), + 2: pd.Timestamp("2018-01-03"), 3: pd.Timestamp("2018-01-03")}, + "hhs": {0: "5", 1: "9", 2: "5", 3: "9"}, + "count": {0: 99.0, 1: 801.0, 2: 100.0, 3: 786.0}, + "total": {0: 198.0, 1: 1602.0, 2: 200.0, 3: 1572.0} + } + ) + ) + + def test_get_geos(self): + gmpr = GeoMapper() + assert gmpr.get_geo_values("nation") == {"us"} + assert gmpr.get_geo_values("hhs") == set(str(i) for i in range(1, 11)) + assert len(gmpr.get_geo_values("fips")) == 3274 + assert len(gmpr.get_geo_values("state_id")) == 60 + assert len(gmpr.get_geo_values("zip")) == 32976 diff --git a/_delphi_utils_python/tests/test_signal.py b/_delphi_utils_python/tests/test_signal.py new file mode 100644 index 000000000..d95d4bfb2 --- /dev/null +++ b/_delphi_utils_python/tests/test_signal.py @@ -0,0 +1,40 @@ +"""Tests for delphi_utils.signal.""" +from unittest.mock import patch + +import pandas as pd +import pytest +from delphi_utils.signal import add_prefix, public_signal + +# Constants for mocking out the call to `covidcast.metadata` within `public_signal()`. +PUBLIC_SIGNALS = ["sig1", "sig2", "sig3"] +PUBLIC_SIGNALS_FRAME = pd.DataFrame(data={"signal": PUBLIC_SIGNALS}) + +class TestSignal: + """Tests for signal.py.""" + + def test_add_prefix_to_all(self): + """Tests that `add_prefix()` derives work-in-progress names for all input signals.""" + assert add_prefix(["sig1", "sig3"], True, prefix="wip_") == ["wip_sig1", "wip_sig3"] + + def test_add_prefix_to_specified(self): + """Tests that `add_prefix()` derives work-in-progress names for specified signals.""" + assert add_prefix(["sig1", "sig2", "sig3"], ["sig2"], prefix="wip_") ==\ + ["sig1", "wip_sig2", "sig3"] + + def test_invalid_prefix_input(self): + """Tests that `add_prefix()` raises a ValueError when invalid input is given.""" + with pytest.raises(ValueError): + add_prefix(None, None) + + @patch("covidcast.metadata") + def test_add_prefix_to_non_public(self, metadata): + """Tests that `add_prefix()` derives work-in-progress names for non-public signals.""" + metadata.return_value = PUBLIC_SIGNALS_FRAME + assert add_prefix(["sig0", "sig1"], False, prefix="wip_") == ["wip_sig0", "sig1"] + + @patch("covidcast.metadata") + def test_public_signal(self, metadata): + """Tests that `public_signal()` identifies public vs. private signals.""" + metadata.return_value = PUBLIC_SIGNALS_FRAME + assert not public_signal("sig0") + assert public_signal("sig2") diff --git a/_delphi_utils_python/tests/test_smooth.py b/_delphi_utils_python/tests/test_smooth.py new file mode 100644 index 000000000..56bdc4349 --- /dev/null +++ b/_delphi_utils_python/tests/test_smooth.py @@ -0,0 +1,298 @@ +""" +Tests for the smoothing utility. +Authors: Dmitry Shemetov, Addison Hu, Maria Jahja +""" +from numpy.lib.polynomial import poly +import pytest + +import numpy as np +import pandas as pd +from delphi_utils import Smoother + + +class TestSmoothers: + def test_bad_inputs(self): + with pytest.raises(ValueError): + Smoother(smoother_name="hamburger") + with pytest.raises(ValueError): + Smoother(impute_method="hamburger") + with pytest.raises(ValueError): + Smoother(boundary_method="hamburger") + with pytest.raises(ValueError): + Smoother(window_length=1) + + def test_identity_smoother(self): + signal = np.arange(30) + np.random.rand(30) + assert np.allclose(signal, Smoother(smoother_name="identity").smooth(signal)) + + def test_moving_average_smoother(self): + # Test non-integer window-length + with pytest.raises(ValueError): + signal = np.array([1, 1, 1]) + Smoother(smoother_name="window_average", window_length=5.5).smooth(signal) + + # The raw and smoothed lengths should match + signal = np.ones(30) + smoother = Smoother(smoother_name="moving_average") + smoothed_signal = smoother.smooth(signal) + assert len(signal) == len(smoothed_signal) + + # The raw and smoothed arrays should be identical on constant data + # modulo the nans + signal = np.ones(30) + window_length = 10 + smoother = Smoother(smoother_name="moving_average", window_length=window_length) + smoothed_signal = smoother.smooth(signal) + assert np.allclose( + signal[window_length - 1 :], smoothed_signal[window_length - 1 :] + ) + + def test_left_gauss_linear_smoother(self): + # The raw and smoothed lengths should match + signal = np.ones(30) + smoother = Smoother(smoother_name="left_gauss_linear") + smoothed_signal = smoother.smooth(signal) + assert len(signal) == len(smoothed_signal) + # The raw and smoothed arrays should be identical on constant data + # modulo the nans + assert np.allclose(signal[1:], smoothed_signal[1:]) + + # The smoother should basically be the identity when the Gaussian kernel + # is set to weigh the present value overwhelmingly + signal = np.arange(1, 30) + np.random.normal(0, 1, 29) + smoother = Smoother(smoother_name="left_gauss_linear", gaussian_bandwidth=0.1) + assert np.allclose(smoother.smooth(signal)[1:], signal[1:]) + + def test_causal_savgol_coeffs(self): + # The coefficients should return standard average weights for M=0 + nl, nr = -10, 0 + window_length = nr - nl + 1 + smoother = Smoother( + smoother_name="savgol", + window_length=window_length, + poly_fit_degree=0, + gaussian_bandwidth=None, + ) + assert np.allclose(smoother.coeffs, np.ones(window_length) / window_length) + + def test_causal_savgol_smoother(self): + # The raw and smoothed lengths should match + signal = np.ones(30) + window_length = 10 + smoother = Smoother( + smoother_name="savgol", window_length=window_length, poly_fit_degree=0 + ) + smoothed_signal = smoother.smooth(signal) + assert len(signal) == len(smoothed_signal) + # The raw and smoothed arrays should be identical on constant data + # modulo the nans, when M >= 0 + assert np.allclose( + signal[window_length - 1 :], smoothed_signal[window_length - 1 :] + ) + + # The raw and smoothed arrays should be identical on linear data + # modulo the nans, when M >= 1 + signal = np.arange(30) + smoother = Smoother( + smoother_name="savgol", window_length=window_length, poly_fit_degree=1 + ) + smoothed_signal = smoother.smooth(signal) + assert np.allclose( + signal[window_length - 1 :], smoothed_signal[window_length - 1 :] + ) + + # The raw and smoothed arrays should be identical on quadratic data + # modulo the nans, when M >= 2 + signal = np.arange(30) ** 2 + smoother = Smoother( + smoother_name="savgol", window_length=window_length, poly_fit_degree=2 + ) + smoothed_signal = smoother.smooth(signal) + assert np.allclose( + signal[window_length - 1 :], smoothed_signal[window_length - 1 :] + ) + + # The savgol method should match the linear regression method on the first + # window_length-many values of the signal, if the savgol_weighting is set to true, + # and the polynomial fit degree is set to 1. Beyond that, there will be very small + # differences between the signals (due to "left_gauss_linear" not having a window_length + # cutoff). + window_length = 50 + signal = np.arange(window_length) + np.random.randn(window_length) + smoother = Smoother(smoother_name="left_gauss_linear") + smoothed_signal1 = smoother.smooth(signal) + smoother = Smoother( + smoother_name="savgol", window_length=window_length, poly_fit_degree=1, + ) + smoothed_signal2 = smoother.smooth(signal) + assert np.allclose(smoothed_signal1, smoothed_signal2) + + # Test the all nans case + signal = np.nan * np.ones(10) + smoother = Smoother(window_length=9) + smoothed_signal = smoother.smooth(signal) + assert np.all(np.isnan(smoothed_signal)) + + # Test the case where the signal is length 1 + signal = np.ones(1) + smoother = Smoother() + smoothed_signal = smoother.smooth(signal) + assert np.allclose(smoothed_signal, signal) + + # Test the case where the signal length is less than polynomial_fit_degree + signal = np.ones(2) + smoother = Smoother(poly_fit_degree=3) + smoothed_signal = smoother.smooth(signal) + assert np.allclose(smoothed_signal, signal) + + # Test an edge fitting case + signal = np.array([np.nan, 1, np.nan]) + smoother = Smoother(poly_fit_degree=1, window_length=2) + smoothed_signal = smoother.smooth(signal) + assert np.allclose(smoothed_signal, np.array([np.nan, 1, 1]), equal_nan=True) + + # Test a range of cases where the signal size following a sequence of nans is returned + for i in range(10): + signal = np.hstack([[np.nan, np.nan, np.nan], np.ones(i)]) + smoother = Smoother(poly_fit_degree=0, window_length=5) + smoothed_signal = smoother.smooth(signal) + assert np.allclose(smoothed_signal, signal, equal_nan=True) + + # test window_length > len(signal) and boundary_method="identity" + signal = np.arange(20) + smoother = Smoother(boundary_method="identity", window_length=30) + smoothed_signal = smoother.smooth(signal) + assert np.allclose(signal, smoothed_signal) + + def test_impute(self): + # test front nan error + with pytest.raises(ValueError): + Smoother().impute(signal=np.array([np.nan, 1, 1])) + + # test the nan imputer + signal = np.array([i if i % 3 else np.nan for i in range(1, 40)]) + assert np.allclose(Smoother(impute_method=None).impute(signal), signal, equal_nan=True) + + # test the zeros imputer + signal = np.array([i if i % 3 else np.nan for i in range(1, 40)]) + assert np.allclose( + Smoother(impute_method="zeros").impute(signal), + np.array([i if i % 3 else 0.0 for i in range(1, 40)]) + ) + + # make a signal with periodic nans to test the imputer + signal = np.array([i if i % 3 else np.nan for i in range(1, 40)]) + # test that the non-nan values are unchanged + not_nans_ixs = np.bitwise_xor(np.isnan(signal, where=True), np.full(len(signal), True)) + smoothed_signal = Smoother().impute(signal) + assert np.allclose(signal[not_nans_ixs], smoothed_signal[not_nans_ixs]) + # test that the imputer is close to the true line + assert np.allclose(range(1, 40), smoothed_signal, atol=0.5) + + # should impute the next value in a linear progression with M>=1 + signal = np.hstack([np.arange(10), [np.nan], np.arange(10)]) + window_length = 10 + smoother = Smoother( + window_length=window_length, poly_fit_degree=1 + ) + imputed_signal = smoother.impute(signal) + assert np.allclose(imputed_signal, np.hstack([np.arange(11), np.arange(10)])) + smoother = Smoother( + window_length=window_length, poly_fit_degree=2 + ) + imputed_signal = smoother.impute(signal) + assert np.allclose(imputed_signal, np.hstack([np.arange(11), np.arange(10)])) + + # if there are nans on the boundary, should dynamically change window + signal = np.hstack( + [np.arange(5), [np.nan], np.arange(20), [np.nan], np.arange(5)] + ) + smoother = Smoother( + window_length=window_length, poly_fit_degree=2 + ) + imputed_signal = smoother.impute(signal) + assert np.allclose( + imputed_signal, np.hstack([np.arange(6), np.arange(21), np.arange(5)]), + ) + + # if the array begins with np.nan, we should tell the user to peel it off before sending + signal = np.hstack([[np.nan], np.arange(20), [np.nan], np.arange(5)]) + smoother = Smoother( + window_length=window_length, poly_fit_degree=2 + ) + with pytest.raises(ValueError): + imputed_signal = smoother.impute(signal) + + # test the boundary methods + signal = np.arange(20) + smoother = Smoother(poly_fit_degree=0, + boundary_method="identity", window_length=10) + smoothed_signal = smoother.impute(signal) + assert np.allclose(smoothed_signal, signal) + + # test that we don't hit a matrix inversion error when there are + # nans on less than window_length away from the boundary + signal = np.hstack([[1], np.nan*np.ones(12), np.arange(5)]) + smoother = Smoother(smoother_name="savgol", poly_fit_degree=2, + boundary_method="identity", window_length=10) + smoothed_signal = smoother.impute(signal) + assert np.allclose(smoothed_signal, np.hstack([[1], np.ones(12), np.arange(5)])) + + # test the impute_order argument + signal = np.hstack([[1, np.nan, np.nan, 2], np.arange(5)]) + smoother = Smoother() + smoothed_signal = smoother.impute(signal, impute_order=1) + assert np.allclose(smoothed_signal, np.hstack([[1, 1, 1, 2], np.arange(5)])) + + + def test_pandas_series_input(self): + # The savgol method should match the linear regression method on the first + # window_length-many values of the signal, if the savgol_weighting is set to true, + # and the polynomial fit degree is set to 1. Beyond that, there will be very small + # differences between the signals (due to "left_gauss_linear" not having a window_length + # cutoff). + window_length = 50 + signal = pd.Series(np.arange(window_length) + np.random.randn(window_length)) + smoother = Smoother(smoother_name="left_gauss_linear") + smoothed_signal1 = smoother.smooth(signal) + smoother = Smoother( + smoother_name="savgol", window_length=window_length, poly_fit_degree=1, + ) + smoothed_signal2 = smoother.smooth(signal) + + assert np.allclose(smoothed_signal1, smoothed_signal2) + + window_length = 50 + signal = pd.Series(np.arange(window_length) + np.random.randn(window_length)) + smoother = Smoother(smoother_name="left_gauss_linear") + smoothed_signal1 = signal.transform(smoother.smooth) + smoother = Smoother( + smoother_name="savgol", window_length=window_length, poly_fit_degree=1, + ) + smoothed_signal2 = signal.transform(smoother.smooth) + + assert np.allclose(smoothed_signal1, smoothed_signal2) + + # The raw and smoothed lengths should match + signal = pd.Series(np.ones(30)) + smoother = Smoother(smoother_name="moving_average") + smoothed_signal = signal.transform(smoother.smooth) + assert len(signal) == len(smoothed_signal) + + # The raw and smoothed arrays should be identical on constant data + # modulo the nans + signal = pd.Series(np.ones(30)) + window_length = 10 + smoother = Smoother(smoother_name="moving_average", window_length=window_length) + smoothed_signal = signal.transform(smoother.smooth) + assert np.allclose( + signal[window_length - 1 :], smoothed_signal[window_length - 1 :] + ) + + # Test that the index of the series gets preserved + signal = pd.Series(np.ones(30), index=np.arange(50, 80)) + smoother = Smoother(smoother_name="moving_average", window_length=10) + smoothed_signal = signal.transform(smoother.smooth) + ix1 = signal.index + ix2 = smoothed_signal.index + assert ix1.equals(ix2) diff --git a/doctor_visits/delphi_doctor_visits/config.py b/doctor_visits/delphi_doctor_visits/config.py index 5d787efb3..14395f0fc 100644 --- a/doctor_visits/delphi_doctor_visits/config.py +++ b/doctor_visits/delphi_doctor_visits/config.py @@ -21,17 +21,17 @@ class Config: CLI_COLS = ["Covid_like", "Flu_like", "Mixed"] FLU1_COL = ["Flu1"] COUNT_COLS = CLI_COLS + FLU1_COL + ["Denominator"] - DATE_COL = "ServiceDate" - GEO_COL = "PatCountyFIPS" - AGE_COL = "PatAgeGroup" - HRR_COLS = ["Pat HRR Name", "Pat HRR ID"] + DATE_COL = "ServiceDate" #"servicedate" + GEO_COL = "PatCountyFIPS" #"patCountyFIPS" + AGE_COL = "PatAgeGroup" #"patAgeGroup" + HRR_COLS = ["Pat HRR Name", "Pat HRR ID"]#["patHRRname", "patHRRid"] ID_COLS = [DATE_COL] + [GEO_COL] + [AGE_COL] + HRR_COLS FILT_COLS = ID_COLS + COUNT_COLS - DTYPES = {"ServiceDate": str, "PatCountyFIPS": str, + DTYPES = {DATE_COL: str, GEO_COL: str, "Denominator": int, "Flu1": int, "Covid_like": int, "Flu_like": int, - "Mixed": int, "PatAgeGroup": str, - "Pat HRR Name": str, "Pat HRR ID": float} + "Mixed": int, AGE_COL: str, + HRR_COLS[0]: str, HRR_COLS[1]: float} SMOOTHER_BANDWIDTH = 100 # bandwidth for the linear left Gaussian filter MAX_BACKFILL_WINDOW = 7 # maximum number of days used to average a backfill correction @@ -39,3 +39,6 @@ class Config: RECENT_LENGTH = 7 # number of days to sum over for sparsity threshold MIN_RECENT_VISITS = 100 # min numbers of visits needed to include estimate MIN_RECENT_OBS = 3 # minimum days needed to produce an estimate for latest time + + SENSOR_WINDOW_START = None # 7 # start of training window for sensorization + SENSOR_WINDOW_END = 42 # end of training window for sensorization diff --git a/doctor_visits/delphi_doctor_visits/geo_maps.py b/doctor_visits/delphi_doctor_visits/geo_maps.py index b7fc0fbb1..9f1aab0b4 100644 --- a/doctor_visits/delphi_doctor_visits/geo_maps.py +++ b/doctor_visits/delphi_doctor_visits/geo_maps.py @@ -40,10 +40,10 @@ def county_to_msa(self, data): data = self.gmpr.add_geocode(data, "fips", "msa", - from_col="PatCountyFIPS", + from_col=Config.GEO_COL, new_col="cbsa_id") - data.drop(columns="PatCountyFIPS", inplace=True) - data = data.groupby(["ServiceDate", "cbsa_id"]).sum().reset_index() + data.drop(columns=Config.GEO_COL, inplace=True) + data = data.groupby([Config.DATE_COL, "cbsa_id"]).sum().reset_index() return data.groupby("cbsa_id"), "cbsa_id" @@ -58,9 +58,9 @@ def county_to_state(self, data): data = self.gmpr.add_geocode(data, "fips", "state_id", - from_col="PatCountyFIPS") - data.drop(columns="PatCountyFIPS", inplace=True) - data = data.groupby(["ServiceDate", "state_id"]).sum().reset_index() + from_col=Config.GEO_COL) + data.drop(columns=Config.GEO_COL, inplace=True) + data = data.groupby([Config.DATE_COL, "state_id"]).sum().reset_index() return data.groupby("state_id"), "state_id" @@ -81,11 +81,11 @@ def county_to_hrr(self, data): data = self.gmpr.add_geocode(data, "fips", "hrr", - from_col="PatCountyFIPS") - data.drop(columns="PatCountyFIPS", inplace=True) + from_col=Config.GEO_COL) + data.drop(columns=Config.GEO_COL, inplace=True) ## do a weighted sum by the wpop column to get each HRR's contribution - tmp = data.groupby(["ServiceDate", "hrr"]) + tmp = data.groupby([Config.DATE_COL, "hrr"]) wtsum = lambda g: g["weight"].values @ g[Config.COUNT_COLS] data = tmp.apply(wtsum).reset_index() @@ -101,14 +101,15 @@ def county_to_megacounty(self, data, threshold_visits, threshold_len): Returns: tuple of dataframe at the daily-state resolution, and geo_id column name """ + all_data = self.gmpr.fips_to_megacounty(data, threshold_visits, threshold_len, - fips_col="PatCountyFIPS", + fips_col=Config.GEO_COL, thr_col="Denominator", - date_col="ServiceDate") - all_data.rename({"megafips": "PatCountyFIPS"}, axis=1, inplace=True) - megacounties = all_data[all_data.PatCountyFIPS.str.endswith("000")] + date_col=Config.DATE_COL) + all_data.rename({"megafips": Config.GEO_COL}, axis=1, inplace=True) + megacounties = all_data[all_data[Config.GEO_COL].str.endswith("000")] data = pd.concat([data, megacounties]) - return data.groupby("PatCountyFIPS"), "PatCountyFIPS" + return data.groupby(Config.GEO_COL), Config.GEO_COL diff --git a/doctor_visits/delphi_doctor_visits/run.py b/doctor_visits/delphi_doctor_visits/run.py index 63840e4ef..99931dd2b 100644 --- a/doctor_visits/delphi_doctor_visits/run.py +++ b/doctor_visits/delphi_doctor_visits/run.py @@ -15,6 +15,7 @@ # first party from .update_sensor import update_sensor +from .config import Config def run_module(): @@ -25,10 +26,10 @@ def run_module(): ## get end date from input file # the filename is expected to be in the format: - # "EDI_AGG_OUTPATIENT_DDMMYYYY_HHMM{timezone}.csv.gz" + # "EDI_AGG_OUTPATIENT_YYYYMMDD_HHMM{timezone}.csv.gz" if params["drop_date"] == "": dropdate_dt = datetime.strptime( - Path(params["input_file"]).name.split("_")[3], "%d%m%Y" + Path(params["input_file"]).name.split("_")[3], "%Y%m%d" ) else: dropdate_dt = datetime.strptime(params["drop_date"], "%Y-%m-%d") @@ -38,7 +39,8 @@ def run_module(): n_backfill_days = params["n_backfill_days"] # produce estimates for n_backfill_days n_waiting_days = params["n_waiting_days"] # most recent n_waiting_days won't be est enddate_dt = dropdate_dt - timedelta(days=n_waiting_days) - startdate_dt = enddate_dt - timedelta(days=n_backfill_days) + startdate_dt = max(enddate_dt - timedelta(days=n_backfill_days),\ + Config.FIRST_DATA_DATE + timedelta(days=1) + Config.DAY_SHIFT) enddate = str(enddate_dt.date()) startdate = str(startdate_dt.date()) logging.info(f"drop date:\t\t{dropdate}") @@ -53,30 +55,33 @@ def run_module(): ## print out other vars logging.info("outpath:\t\t%s", params["export_dir"]) logging.info("parallel:\t\t%s", params["parallel"]) - logging.info(f"weekday:\t\t%s", params["weekday"]) - logging.info(f"write se:\t\t%s", params["se"]) - logging.info(f"obfuscated prefix:\t%s", params["obfuscated_prefix"]) + logging.info("weekday:\t\t%s", params["weekday"]) + logging.info("write se:\t\t%s", params["se"]) + logging.info("obfuscated prefix:\t%s", params["obfuscated_prefix"]) ## start generating for geo in geos: for weekday in params["weekday"]: - if weekday: - logging.info("starting %s, weekday adj", geo) - else: - logging.info("starting %s, no adj", geo) - update_sensor( - filepath=params["input_file"], - outpath=params["export_dir"], - staticpath=params["static_file_dir"], - startdate=startdate, - enddate=enddate, - dropdate=dropdate, - geo=geo, - parallel=params["parallel"], - weekday=weekday, - se=params["se"], - prefix=params["obfuscated_prefix"] - ) + for sensorize in params["sensorize"]: + if weekday: + logging.info("starting %s, weekday adj", geo) + else: + logging.info("starting %s, no adj", geo) + update_sensor( + filepath=params["input_file"], + outpath=params["export_dir"], + staticpath=params["static_file_dir"], + startdate=startdate, + enddate=enddate, + dropdate=dropdate, + geo=geo, + parallel=params["parallel"], + weekday=weekday, + se=params["se"], + sensorize=sensorize, + global_sensor_fit=params["global_sensor_fit"], + prefix=params["obfuscated_prefix"] + ) logging.info("finished %s", geo) logging.info("finished all") diff --git a/doctor_visits/delphi_doctor_visits/sensor.py b/doctor_visits/delphi_doctor_visits/sensor.py index f1bc3f26d..3acec32e6 100644 --- a/doctor_visits/delphi_doctor_visits/sensor.py +++ b/doctor_visits/delphi_doctor_visits/sensor.py @@ -180,7 +180,7 @@ def fit(y_data, Returns: dictionary of results """ - y_data.set_index("ServiceDate", inplace=True) + y_data.set_index(Config.DATE_COL, inplace=True) y_data = DoctorVisitsSensor.fill_dates(y_data, fit_dates) sensor_idxs = np.where(y_data.index >= sensor_dates[0])[0] n_dates = y_data.shape[0] diff --git a/doctor_visits/delphi_doctor_visits/sensorize.py b/doctor_visits/delphi_doctor_visits/sensorize.py new file mode 100644 index 000000000..fffba298f --- /dev/null +++ b/doctor_visits/delphi_doctor_visits/sensorize.py @@ -0,0 +1,290 @@ +""" +Sensorizer class to convert an indicator into a sensor. +Uses univariate linear regression from indicator to target. + +Author: Aaron Rumack +Created: 2020-11-11 + +""" + +import logging +from datetime import timedelta + +# third party +import numpy as np +import pandas as pd + +# first party +from .config import Config + +class Sensorizer: + """Sensorizer class to convert indicator into a sensor. + """ + + @staticmethod + def linear_regression_coefs( + grouped_df, + signal_col="signal", + target_col="target", + fit_intercept=True): + """ + Calculate linear regression coefficients for a grouped df. + For each group, calculate slope as cov(x,y)/var(x) and + intercept as mean(y) - slope*mean(x). + + Args: + grouped_df: Grouped DataFrame, calculation to be done + for each group + signal_col: column name of covariate + target_col: column name of response + fit_intercept: boolean whether to fit intercept + + Returns: + DataFrame with columns for groups, and + "b1": slope of regression, and + "b0": intercept of regression + """ + + signal_mean = signal_col + "_mean" + target_mean = target_col + "_mean" + signal_var = signal_col + "_var" + + if fit_intercept: + cov_df = grouped_df.apply( + lambda x: x[signal_col].cov(x[target_col],ddof=0)) + cov_df = cov_df.reset_index().rename(columns={0: "cov"}) + + var_df = grouped_df.apply(np.var).reset_index() + var_df = var_df.rename( + columns={signal_col:signal_var}).drop(target_col,axis=1) + means_df = grouped_df.agg(np.mean).reset_index() + means_df = means_df.rename( + columns={signal_col: signal_mean, target_col:target_mean}) + + fit_df = cov_df.merge(var_df).merge(means_df) + fit_df["b1"] = fit_df["cov"] / fit_df[signal_var] + fit_df["b0"] = \ + fit_df[target_mean]-fit_df["b1"]*fit_df[signal_mean] + fit_df = fit_df.drop( + ["cov",signal_mean,target_mean,signal_var],axis=1) + else: + # No intercept, b1 = sum(x_i * y_i)/sum(x_i^2) + fit_df = grouped_df.apply(lambda x: + np.sum(x[signal_col]*x[target_col])/np.sum(x[signal_col]**2)) + fit_df = fit_df.reset_index().rename(columns={0: "b1"}) + fit_df["b0"] = 0 + return fit_df + + + @staticmethod + def weighted_moments(df, data_col, wt_col): + """ + Calculate weighted mean and std for a DataFrame + + Args: + df: pd.DataFrame + data_col: column name of data + wt_col: column name of weight + + Returns: + Tuple: (weighted mean, weighted_std) + """ + + w_mean = (df[data_col] * df[wt_col]).sum() / df[wt_col].sum() + w_var = (np.power(df[data_col]-w_mean,2) * df[wt_col]).sum() + w_var = w_var / df[wt_col].sum() + return (w_mean, np.sqrt(w_var)) + + + @staticmethod + def sensorize( + signal, + target, + signal_geo_col, + signal_time_col, + signal_val_col, + target_geo_col, + target_time_col, + target_val_col, + window_start=Config.SENSOR_WINDOW_START, + window_end=Config.SENSOR_WINDOW_END, + global_weights=None, + global_sensor_fit="intercept"): + """ + Sensorize a signal to correct for spatial heterogeneity. For each + date, use linear regression to fit target to signal globally (f) and + fit signal to target locally (g_i). Output sensorized signal as + f(g_i(signal)). + + Args: + signal: DataFrame with signal values to be sensorized + target: DataFrame with target observations for sensor fit + {signal,target}_geo_col: name of location column in DataFrame + {signal,target}_time_col: name of time column in DataFrame + {signal,target}_val_col: name of value column in DataFrame + window_start: Number of days before fit date at which to begin + training, inclusive + Positive integer or None for static sensorization + window_end: Number of days before fit date at which to end + training, exclusive + Positive integer, greater than window_start + global_weights: DataFrame with weights to create global signal + and global target, with columns signal_geo_col + and "weight" + global_sensor_fit: + Method to fit/rescale sensorized signal to original scale. + - "intercept": Fits linear regression with slope and intercept + from target to signal over training period + - "no-intercept": Fits linear regression with slope only + from target to signal over training period + - "norm": Rescales sensorized signal so that it has the same + mean and st. dev. for each day as the original signal + + Returns: + DataFrame with signal_geo_col, signal_time_col, and signal_val_col + Values in signal_val_col are sensorized + + """ + + logging.info("Sensorizing") + assert np.isin(global_sensor_fit,["intercept","no-intercept","norm"]),\ + "%s is invalid global_sensor_fit" % (global_sensor_fit) + target = target.rename(columns={ + target_time_col:signal_time_col, + target_geo_col:signal_geo_col, + target_val_col:"target"}) + signal = signal.rename(columns={signal_val_col:"signal"}) + merged = pd.merge(signal,target,on=[signal_time_col,signal_geo_col],how="left") + merged = merged.sort_values([signal_geo_col,signal_time_col]).reset_index() + + sensor_time_col = "sensor" + signal_time_col + if window_start is not None: + unique_times = pd.to_datetime(merged[signal_time_col].unique()) + sliding_window_df = 0 + flag = False + for i in range(len(unique_times)): + if not flag: + sliding_window_df = pd.DataFrame(data={ + signal_time_col:pd.date_range(unique_times[i]-timedelta(days=window_end-1), + unique_times[i]-timedelta(days=window_start)), + sensor_time_col:unique_times[i]}) + flag = True + else: + tmp_df = pd.DataFrame(data={ + signal_time_col:pd.date_range(unique_times[i]-timedelta(days=window_end-1), + unique_times[i]-timedelta(days=window_start)), + sensor_time_col:unique_times[i]}) + sliding_window_df = sliding_window_df.append(tmp_df) + + sliding_window_df = pd.merge(merged,sliding_window_df) + # Local fits from signal (covariate) to target (response) + grouped = sliding_window_df[[signal_geo_col,sensor_time_col,"signal","target"]] + grouped = grouped.groupby([signal_geo_col,sensor_time_col]) + else: + grouped = merged[[signal_geo_col,"signal","target"]].groupby(signal_geo_col) + + local_fit_df = Sensorizer.linear_regression_coefs(grouped, "signal", "target") + local_fit_df = local_fit_df.reset_index(drop=True) + local_fit_df = local_fit_df.rename( + columns={"b1":"local_b1","b0":"local_b0",sensor_time_col:signal_time_col}) + + if global_weights is None: + global_weights = pd.DataFrame(data={ + signal_geo_col:merged[signal_geo_col].unique(), + "weight":1}) + global_weights.weight = global_weights.weight/global_weights.weight.sum() + + if global_sensor_fit != "norm": + # Global fits from target (covariate) to signal (response) + + if window_start is not None: + grouped = sliding_window_df[[ + signal_geo_col,signal_time_col,sensor_time_col,"signal","target"]] + grouped = grouped.merge(global_weights) + grouped["signal_wt"] = grouped.weight * grouped.signal + grouped["target_wt"] = grouped.weight * grouped.target + + grouped = grouped.groupby([signal_time_col,sensor_time_col]) + grouped = grouped.agg(np.sum).reset_index() + grouped = grouped[[signal_time_col,sensor_time_col,"signal_wt","target_wt"]] + grouped = grouped.rename(columns={"signal_wt":"signal","target_wt":"target"}) + grouped = grouped.groupby(sensor_time_col) + else: + grouped = merged[[signal_geo_col,signal_time_col,"signal","target"]] + grouped = grouped.merge(global_weights) + grouped["signal_wt"] = grouped.weight * grouped.signal + grouped["target_wt"] = grouped.weight * grouped.target + + grouped = grouped.groupby([signal_time_col]).agg(np.sum).reset_index() + grouped = grouped[[signal_time_col,"signal_wt","target_wt"]] + grouped = grouped.rename( + columns={"signal_wt":"signal","target_wt":"target"} + ) + # Need to group to call linear_regression_coefs but only want 1 group + grouped["g"] = "1" + grouped = grouped.groupby("g") + + fit_intercept = (global_sensor_fit == "intercept") + global_fit_df = Sensorizer.linear_regression_coefs(grouped, "target", "signal", + fit_intercept=fit_intercept) + global_fit_df = global_fit_df.reset_index(drop=True) + global_fit_df = global_fit_df.rename( + columns={"b1":"global_b1","b0":"global_b0",sensor_time_col:signal_time_col}) + else: + grouped = merged[[signal_geo_col,signal_time_col,"signal","target"]] + grouped = grouped.merge(global_weights) + signal_moments = grouped.drop(signal_geo_col,axis=1).groupby(signal_time_col) + signal_moments = signal_moments.apply( + Sensorizer.weighted_moments, "signal", "weight").reset_index() + signal_moments[["signal_mean","signal_std"]] = pd.DataFrame( + signal_moments[0].tolist(),index=signal_moments.index) + signal_moments = signal_moments[[signal_time_col,"signal_mean","signal_std"]] + + sensor_df = pd.merge(grouped,local_fit_df) + sensor_df["sensor"] = sensor_df["signal"]*sensor_df["local_b1"]\ + + sensor_df["local_b0"] + sensor_moments = sensor_df.drop(signal_geo_col,axis=1).groupby(signal_time_col) + sensor_moments = sensor_moments.apply( + Sensorizer.weighted_moments, "sensor", "weight").reset_index() + sensor_moments[["sensor_mean","sensor_std"]] = pd.DataFrame( + sensor_moments[0].tolist(),index=sensor_moments.index) + sensor_moments = sensor_moments[[signal_time_col,"sensor_mean","sensor_std"]] + fit_df = pd.merge(signal_moments, sensor_moments) + fit_df["global_b0"] = fit_df["signal_mean"] - \ + fit_df["sensor_mean"] * (fit_df["signal_std"]/fit_df["sensor_std"]) + fit_df["global_b1"] = fit_df["signal_std"]/fit_df["sensor_std"] + global_fit_df = fit_df[[signal_time_col,"global_b1","global_b0"]] + + # If static sensorization, global_fit_df has one row + # Pandas doesn't allow join without common columns + if window_start is None and global_sensor_fit != "norm": + combined_df = merged.copy(deep=True) + combined_df["global_b0"] = global_fit_df["global_b0"].values[0] + combined_df["global_b1"] = global_fit_df["global_b1"].values[0] + else: + combined_df = pd.merge(merged,global_fit_df,how="left") + combined_df = pd.merge(combined_df,local_fit_df,how="left") + + # First, calculate estimate in target space + combined_df["sensor"] = combined_df["signal"]*combined_df["local_b1"]\ + + combined_df["local_b0"] + # Second, scale back into signal space + combined_df["sensor"] = combined_df["sensor"]*combined_df["global_b1"]\ + + combined_df["global_b0"] + # Where we could not fit regression coefficients, use original signal + combined_df.sensor = combined_df["sensor"].fillna(combined_df["signal"]) + bad_fit = (combined_df.sensor < 0) | (combined_df.sensor > 0.9) + combined_df.loc[bad_fit,"sensor"] = combined_df.loc[bad_fit,"signal"] + + sensor_df = combined_df[[signal_geo_col,signal_time_col,"sensor"]] + sensor_df = sensor_df.rename(columns={"sensor":signal_val_col}) + + combined_df["b1"] = combined_df["global_b1"]*combined_df["local_b1"] + combined_df["b0"] = combined_df["global_b1"]*combined_df["local_b0"]+combined_df["global_b0"] + bad_fit = np.isnan(combined_df["b1"]) | np.isnan(combined_df["b0"]) + combined_df["b1"][bad_fit] = 1 + combined_df["b0"][bad_fit] = 0 + coef_df = combined_df[[signal_geo_col,signal_time_col,"b1","b0"]] + if window_start is None and global_sensor_fit != "norm": + coef_df = coef_df.drop(signal_time_col, axis=1).drop_duplicates() + return sensor_df, coef_df diff --git a/doctor_visits/delphi_doctor_visits/update_sensor.py b/doctor_visits/delphi_doctor_visits/update_sensor.py index 5a257ad68..3329ea8d0 100644 --- a/doctor_visits/delphi_doctor_visits/update_sensor.py +++ b/doctor_visits/delphi_doctor_visits/update_sensor.py @@ -6,6 +6,7 @@ Modified: - 2020-04-30: Aaron Rumack (add megacounty code) - 2020-05-06: Aaron and Maria (weekday effects/other adjustments) + - 2020-11-12: Aaron Rumack (add sensorization) """ # standard packages @@ -17,11 +18,16 @@ import numpy as np import pandas as pd +# third party +import covidcast + # first party from .config import Config from .geo_maps import GeoMaps from .sensor import DoctorVisitsSensor +from .sensorize import Sensorizer from .weekday import Weekday +from delphi_utils import GeoMapper def write_to_csv(output_dict, se, out_name, output_path="."): @@ -75,10 +81,42 @@ def write_to_csv(output_dict, se, out_name, output_path="."): out_n += 1 logging.debug(f"wrote {out_n} rows for {len(geo_ids)} {geo_level}") +def write_coefs_to_csv(output_dict, out_name, output_path="."): + """Write sensorization coefficients to csv. + Only supported for static sensorization, since this can save memory. + + Args: + output_dict: dictionary containing sensorization coefficients and unique geo_id + out_name: name of the output file + output_path: outfile path to write the csv (default is current directory) + """ + + geo_level = output_dict["geo_level"] + geo_ids = output_dict["geo_ids"] + all_coefs = output_dict["coefs"] + + out_n = 0 + filename = "%s/%s_%s.csv" % (output_path, + geo_level, + out_name) + + with open(filename, "w") as outfile: + outfile.write("geo_id,b1,b0\n") + + for geo_id in geo_ids: + b1 = all_coefs[geo_id][1] + b0 = all_coefs[geo_id][0] * 100 # report percentage + assert not np.isnan(b1), "sensorization coef b1 is nan, check pipeline" + assert not np.isnan(b0), "sensorization coef b0 is nan, check pipeline" + + outfile.write("%s,%f,%f\n" % (geo_id, b1, b0)) + out_n += 1 + logging.debug(f"wrote {out_n} rows for {len(geo_ids)} {geo_level}") + -def update_sensor( +def update_sensor( # pylint: disable=too-many-branches filepath, outpath, staticpath, startdate, enddate, dropdate, geo, parallel, - weekday, se, prefix=None + weekday, se, sensorize, global_sensor_fit="intercept", prefix=None ): """Generate sensor values, and write to csv format. @@ -93,6 +131,8 @@ def update_sensor( parallel: boolean to run the sensor update in parallel weekday: boolean to adjust for weekday effects se: boolean to write out standard errors, if true, use an obfuscated name + sensorize: boolean to sensorize signal + global_sensor_fit: method to fit/rescale sensorized signal prefix: string to prefix to output files (used for obfuscation in producing SEs) """ @@ -139,9 +179,6 @@ def update_sensor( # handle if we need to adjust by weekday params = Weekday.get_params(data) if weekday else None - # handle explicitly if we need to use Jeffreys estimate for binomial proportions - jeffreys = True if se else False - # get right geography geo_map = GeoMaps() if geo.lower() == "county": @@ -176,7 +213,7 @@ def update_sensor( geo_id, Config.MIN_RECENT_VISITS, Config.MIN_RECENT_OBS, - jeffreys + se # use Jeffreys estimate ) sensor_rates[geo_id] = res["rate"][final_sensor_idxs] sensor_se[geo_id] = res["se"][final_sensor_idxs] @@ -203,7 +240,7 @@ def update_sensor( geo_id, Config.MIN_RECENT_VISITS, Config.MIN_RECENT_OBS, - jeffreys, + se, # use Jeffreys estimate ), ) ) @@ -225,12 +262,92 @@ def update_sensor( "include": sensor_include, } + if sensorize: + + # Convert data in dict of dicts format to single pd.DataFrame + signal_dfs = [] + for geo_id in unique_geo_ids: + incl = output_dict["include"][geo_id] + geo_df = pd.DataFrame( + data={"signal":output_dict["rates"][geo_id][incl], + "time": burn_in_dates[final_sensor_idxs][incl]}) + geo_df["geo"] = geo_id + signal_dfs.append(geo_df) + signal_df = pd.concat(signal_dfs) + + # Load target (7-day averaged case incidence proportion) + target_df = covidcast.signal("jhu-csse", + "confirmed_7dav_incidence_prop", + geo_type = geo.lower(), + start_day = pd.to_datetime(sensor_dates[0]), + end_day = pd.to_datetime(sensor_dates[-1])) + target_df = target_df[["geo_value","time_value","value"]] + + geomapper = GeoMapper() + fips_pop = pd.DataFrame({"fips":sorted(list(geomapper.get_geo_values("fips")))}) + fips_pop = fips_pop[fips_pop.fips.str.slice(start=2) != "000"] + fips_pop = geomapper.add_population_column(fips_pop,"fips",geocode_col="fips") + if geo.lower() == "state": + geo_weights = geomapper.replace_geocode( + fips_pop,"fips","state_id",from_col="fips",new_col="geo",date_col=None + ) + elif geo.lower() == "hrr": + geo_weights = geomapper.replace_geocode( + fips_pop,"fips","hrr",from_col="fips",new_col="geo",date_col=None + ) + elif geo.lower() == "msa": + geo_weights = geomapper.replace_geocode( + fips_pop,"fips","msa",from_col="fips",new_col="geo",date_col=None + ) + elif geo.lower() == "county": + geo_weights = fips_pop.rename(columns={"fips":"geo"}) + geo_weights = geo_weights.rename(columns={"population":"weight"}) + + # Sensorize! + sensorized_df, coef_df = Sensorizer.sensorize( + signal_df, + target_df, + "geo","time","signal", + "geo_value","time_value","value", + global_weights=geo_weights, + global_sensor_fit=global_sensor_fit) + + # Use sensorized_df to fill in sensorized rates + for geo_id in unique_geo_ids: + incl = output_dict["include"][geo_id] + output_dict["rates"][geo_id][incl] = sensorized_df[ + sensorized_df["geo"] == geo_id + ]["signal"] + + # Eventually need to update SEs also + + # Coef dict + coef_dict = { + "coefs": {}, + "geo_ids": unique_geo_ids, + "geo_level": geo, + } + for geo_id in unique_geo_ids: + idx = np.where(coef_df["geo"] == geo_id)[0][0] + coef_dict["coefs"][geo_id] = [coef_df["b0"].values[idx], coef_df["b1"].values[idx]] + # write out results - out_name = "smoothed_adj_cli" if weekday else "smoothed_cli" + out_name = ["smoothed"] + if weekday: + out_name.append("adj") + if sensorize: + out_name.append("scaled") + out_name.append("cli") + out_name = "_".join(out_name) if se: assert prefix is not None, "template has no obfuscated prefix" out_name = prefix + "_" + out_name write_to_csv(output_dict, se, out_name, outpath) + + if sensorize: + out_name = "coefficients" + write_coefs_to_csv(coef_dict, out_name, outpath) + logging.debug(f"wrote files to {outpath}") return True diff --git a/doctor_visits/params.json.template b/doctor_visits/params.json.template index f18e0b696..bfc354f0d 100644 --- a/doctor_visits/params.json.template +++ b/doctor_visits/params.json.template @@ -1,12 +1,14 @@ { "static_file_dir": "./static", "export_dir": "./receiving", - "input_file": "./input/SYNEDI_AGG_OUTPATIENT_18052020_1455CDT.csv.gz", + "input_file": "./input/EDI_AGG_OUTPATIENT_20201112_1452CDT.csv.gz", "drop_date": "", "n_backfill_days": 60, "n_waiting_days": 3, "weekday": [true, false], "se": false, + "sensorize": [true, false], + "global_sensor_fit": "intercept", "obfuscated_prefix": "wip_XXXXX", "parallel": false } diff --git a/doctor_visits/setup.py b/doctor_visits/setup.py index c9e5c6c4f..71c0cf3a0 100644 --- a/doctor_visits/setup.py +++ b/doctor_visits/setup.py @@ -9,7 +9,8 @@ "pytest", "pytest-cov", "pylint", - "delphi-utils" + "delphi-utils", + "covidcast" ] setup( diff --git a/doctor_visits/tests/test_sensorize.py b/doctor_visits/tests/test_sensorize.py new file mode 100644 index 000000000..860491463 --- /dev/null +++ b/doctor_visits/tests/test_sensorize.py @@ -0,0 +1,68 @@ +import pytest + +# third party +import numpy as np +import pandas as pd +from datetime import timedelta + +# first party +from delphi_doctor_visits.sensorize import Sensorizer + +class TestSensorizer: + + def test_regression(self): + toy_grouped = pd.DataFrame(data={ + "geo":['1','1','1','1','2','2','2','2'], + "time":['1','2','3','4','1','2','3','4'], + "signal":[1,2,3,4,11,12,13,14], + "target":[1,2,3,4,1,2,3,4]}) + toy_grouped = toy_grouped.groupby("geo") + coef_df = Sensorizer.linear_regression_coefs(toy_grouped) + + coefs_b1 = np.array([1,1]) + coefs_b0 = np.array([0,-10]) + assert np.allclose(coef_df["b1"].values, coefs_b1) + assert np.allclose(coef_df["b0"].values, coefs_b0) + + coef_df = Sensorizer.linear_regression_coefs( + toy_grouped,fit_intercept=False) + coefs_b1 = np.array([1,0.20634920634920634]) + coefs_b0 = np.array([0,0]) + assert np.allclose(coef_df["b1"].values, coefs_b1) + assert np.allclose(coef_df["b0"].values, coefs_b0) + + + def test_sensorize(self): + toy_df = pd.DataFrame(data={ + "geo":["a","a","a","a","a","b","b","b","b","b"], + "time":pd.to_datetime(np.tile(pd.date_range("2020-07-01","2020-07-05"),2)), + "signal":np.array([1,2,3,5,5,2,3,3,5,6])*0.01, + "target":np.array([1,2,3,4,5,2,3,4,5,6])*0.01}) + signal_df = toy_df[["geo","time","signal"]] + target_df = toy_df[["geo","time","target"]] + + # Test dynamic sensorization + coef_df = Sensorizer.sensorize(signal_df,target_df, + "geo","time","signal","geo","time","target", + window_start=1,window_end=3) + + local_b1 = np.array([np.nan,np.nan,1,1,0.5,np.nan,np.nan,1,np.nan,0.5]) + local_b0 = np.array([np.nan,np.nan,0,0,1.5,np.nan,np.nan,0,np.nan,2.5])*0.01 + global_b1 = np.tile(np.array([np.nan,np.nan,1,0.5,2]),2) + global_b0 = np.tile(np.array([np.nan,np.nan,0,1.25,-4])*0.01,2) + sensor_values = global_b1*(local_b1*signal_df.signal.values+local_b0)+global_b0 + sensor_values[np.isnan(sensor_values)] = signal_df.signal.values[np.isnan(sensor_values)] + + assert np.allclose(sensor_values, coef_df.signal.values) + + # Test static sensorization + coef_df = Sensorizer.sensorize(signal_df,target_df, + "geo","time","signal","geo","time","target", + window_start=None,window_end=None) + + local_b1 = np.concatenate((np.repeat([11/12.8],5),np.repeat([10/10.8],5))) + local_b0 = np.concatenate((np.repeat([0.25],5),np.repeat([4-10*3.8/10.8],5)))*0.01 + global_b1 = np.repeat([1.05],10) + global_b0 = np.repeat([-0.175],10)*0.01 + sensor_values = global_b1*(local_b1*signal_df.signal.values+local_b0)+global_b0 + assert np.allclose(sensor_values, coef_df.signal.values) \ No newline at end of file