Deploy validation fixes to production

_delphi_utils_python/delphi_utils/runner.py

@@ -17,6 +17,12 @@ def run_indicator_pipeline(indicator_fn:  Callable[[Params], None],
                            archiver_fn:  Callable[[Params], Optional[ArchiveDiffer]] = NULL_FN):
     """Run an indicator with its optional validation and archiving.
 
+    Each argument to this function should itself be a function that will be passed a common set of
+    parameters (see details below).  This parameter dictionary should have four subdictionaries
+    keyed as "indicator", "validation", "archive", and "common" corresponding to parameters to be
+    used in `indicator_fn`, `validator_fn`, `archiver_fn`, and shared across functions,
+    respectively.
+
     Arguments
     ---------
     indicator_fn: Callable[[Params], None]
@@ -43,7 +49,9 @@ def run_indicator_pipeline(indicator_fn:  Callable[[Params], None],
     parser.add_argument("indicator_name",
                         type=str,
                         help="Name of the Python package containing the indicator.  This package "
-                             "must export a `run_module(params)` function.")
+                             "must export a `run.run_module(params)` function.")
     args = parser.parse_args()
     indicator_module = importlib.import_module(args.indicator_name)
-    run_indicator_pipeline(indicator_module.run_module, validator_from_params, archiver_from_params)
+    run_indicator_pipeline(indicator_module.run.run_module,
+                           validator_from_params,
+                           archiver_from_params)

_delphi_utils_python/delphi_utils/validator/datafetcher.py

@@ -5,6 +5,7 @@
 import threading
 from os import listdir
 from os.path import isfile, join
+import warnings
 import pandas as pd
 import numpy as np
 
@@ -114,9 +115,6 @@ def get_geo_signal_combos(data_source):
     geo_signal_combos = list(map(tuple,
                                  source_meta[["geo_type", "signal"]].to_records(index=False)))
 
-    print("Number of expected geo region-signal combinations:",
-          len(geo_signal_combos))
-
     return geo_signal_combos
 
 
@@ -126,8 +124,10 @@ def fetch_api_reference(data_source, start_date, end_date, geo_type, signal_type
 
     Formatting is changed to match that of source data CSVs.
     """
-    api_df = covidcast.signal(
-        data_source, signal_type, start_date, end_date, geo_type)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        api_df = covidcast.signal(
+            data_source, signal_type, start_date, end_date, geo_type)
 
     if not isinstance(api_df, pd.DataFrame):
         custom_msg = "Error fetching data from " + str(start_date) + \

_delphi_utils_python/delphi_utils/validator/report.py

@@ -90,13 +90,19 @@ def log(self):
         for warning in self.raised_warnings:
             logger.warning(str(warning))
 
-    def print_and_exit(self):
-        """Print results and, if any unsuppressed exceptions were raised, exit with non-0 status."""
+    def print_and_exit(self, die_on_failures=True):
+        """Print results and exit.
+
+        Arguments
+        ---------
+        die_on_failures: bool
+            Whether to return non-zero status if any failures were encountered.
+        """
         print(self.summary())
         self.log()
         if self.success():
             sys.exit(0)
-        else:
+        elif die_on_failures:
             sys.exit(1)
 
     def success(self):

_delphi_utils_python/delphi_utils/validator/run.py

@@ -2,16 +2,21 @@
 """Functions to call when running the tool.
 
 This module should contain a function called `run_module`, that is executed
-when the module is run with `python -m delphi_validator`.
+when the module is run with `python -m delphi_utils.validator`.
 """
+import argparse as ap
 from .. import read_params
 from .validate import Validator
 
 
 def run_module():
     """Run the validator as a module."""
+    parser = ap.ArgumentParser()
+    parser.add_argument("--dry_run", action="store_true", help="When provided, return zero exit"
+                        " status irrespective of the number of failures")
+    args = parser.parse_args()
     validator = Validator(read_params())
-    validator.validate().print_and_exit()
+    validator.validate().print_and_exit(not args.dry_run)
 
 
 def validator_from_params(params):

_template_python/delphi_NAME/__main__.py

@@ -6,6 +6,7 @@
 no need to change this template.
 """
 
+from delphi_utils import read_params
 from .run import run_module  # pragma: no cover
 
-run_module()  # pragma: no cover
+run_module(read_params())  # pragma: no cover

_template_python/delphi_NAME/constants.py

@@ -3,11 +3,11 @@
 """
 
 
-## example: 
+## example:
 # FULL_TIME = "full_time_work_prop"
 # PART_TIME = "part_time_work_prop"
 # COVIDNET = "covidnet"
-# 
+#
 # SIGNALS = [
 #     FULL_TIME,
 #     PART_TIME,

_template_python/delphi_NAME/run.py

@@ -2,20 +2,34 @@
 """Functions to call when running the function.
 
 This module should contain a function called `run_module`, that is executed
-when the module is run with `python -m MODULE_NAME`.
+when the module is run with `python -m MODULE_NAME`.  `run_module`'s lone argument should be a
+nested dictionary of parameters loaded from the params.json file.  We expect the `params` to have
+the following structure:
+    - "common":
+        - "export_dir": str, directory to which the results are exported
+        - "log_filename": (optional) str, path to log file
+    - "indicator": (optional)
+        - "wip_signal": (optional) Any[str, bool], list of signals that are works in progress, or
+            True if all signals in the registry are works in progress, or False if only
+            unpublished signals are.  See `delphi_utils.add_prefix()`
+        - Any other indicator-specific settings
 """
-from delphi_utils import read_params
-from .handle_wip_signal import add_prefix
+from delphi_utils import add_prefix
 from .constants import SIGNALS
 
-def run_module():
+def run_module(params):
     """
-    Calls the method for handling the wip signals
+    Runs the indicator
+
+    Arguments
+    --------
+    params:  Dict[str, Any]
+        Nested dictionary of parameters.
+
     Returns
     -------
     prints the updated signal names
     """
-    params = read_params()
-    wip_signal = params["wip_signal"]
+    wip_signal = params["indicator"]["wip_signal"]
     signal_names = add_prefix(SIGNALS, wip_signal, prefix="wip_")
     print(signal_names)

facebook/delphiFacebook/R/binary.R

@@ -167,6 +167,12 @@ get_binary_indicators <- function() {
     "smoothed_wdontneed_reason_not_beneficial", "weight", "v_dontneed_reason_not_beneficial", 6, compute_binary_response, jeffreys_binary,
     "smoothed_dontneed_reason_other", "weight_unif", "v_dontneed_reason_other", 6, compute_binary_response, jeffreys_binary,
     "smoothed_wdontneed_reason_other", "weight", "v_dontneed_reason_other", 6, compute_binary_response, jeffreys_binary,
+
+    # schooling
+    "smoothed_inperson_school_fulltime", "weight_unif", "s_inperson_school_fulltime", 6, compute_binary_response, jeffreys_binary,
+    "smoothed_winperson_school_fulltime", "weight", "s_inperson_school_fulltime", 6, compute_binary_response, jeffreys_binary,
+    "smoothed_inperson_school_parttime", "weight_unif", "s_inperson_school_parttime", 6, compute_binary_response, jeffreys_binary,
+    "smoothed_winperson_school_parttime", "weight", "s_inperson_school_parttime", 6, compute_binary_response, jeffreys_binary,
   )
 
 
@@ -210,22 +216,42 @@ compute_binary_response <- function(response, weight, sample_size)
 #' @return Updated data frame.
 #' @importFrom dplyr mutate
 jeffreys_binary <- function(df) {
-  return(mutate(df,
-                val = jeffreys_percentage(.data$val, .data$sample_size),
-                se = binary_se(.data$val, .data$sample_size)))
+  return( jeffreys_multinomial_factory(2)(df) )
+}
+
+#' Generate function that applies Jeffreys correction to multinomial estimates.
+#'
+#' @param k Number of groups.
+#' 
+#' @return Function to apply multinomial Jeffreys correction.
+#' @importFrom dplyr mutate
+jeffreys_multinomial_factory <- function(k) {
+  # Apply a Jeffreys correction to multinomial estimates and their standard errors.
+  #
+  # Param df: Data frame
+  # Returns: Updated data frame.
+  jeffreys_multinomial <- function(df) {
+    return(mutate(df,
+                  val = jeffreys_percentage(.data$val, .data$sample_size, k),
+                  se = binary_se(.data$val, .data$sample_size)))
+  }
+
+  return(jeffreys_multinomial)
 }
 
-#' Adjust a percentage estimate to use the Jeffreys method.
+#' Adjust a multinomial percentage estimate using the Jeffreys method.
 #'
-#' Takes a previously estimated percentage (calculated with num_yes / total *
+#' Takes a previously estimated percentage (calculated with num_group1 / total *
 #' 100) and replaces it with the Jeffreys version, where one pseudo-observation
-#' with 50% yes is inserted.
+#' with 1/k mass in each group is inserted.
 #'
 #' @param percentage Vector of percentages to adjust.
 #' @param sample_size Vector of corresponding sample sizes.
+#' @param k Number of groups.
+#' 
 #' @return Vector of adjusted percentages.
-jeffreys_percentage <- function(percentage, sample_size) {
-  return((percentage * sample_size + 50) / (sample_size + 1))
+jeffreys_percentage <- function(percentage, sample_size, k) {
+  return((percentage * sample_size + 100/k) / (sample_size + 1))
 }
 
 #' Calculate the standard error for a binary proportion (as a percentage)

facebook/delphiFacebook/R/contingency_aggregate.R

@@ -254,11 +254,11 @@ summarize_aggs <- function(df, crosswalk_data, aggregations, geo_level, params)
 
   group_vars <- aggregations$group_by[[1]]
 
-  if ( !all(groupby_vars %in% names(df)) ) {
+  if ( !all(group_vars %in% names(df)) ) {
     msg_plain(
       sprintf(
         "not all of grouping columns %s available in data; skipping aggregation",
-        paste(groupby_vars, collapse=", ")
+        paste(group_vars, collapse=", ")
       ))
     return( list( ))
   }
@@ -267,11 +267,11 @@ summarize_aggs <- function(df, crosswalk_data, aggregations, geo_level, params)
   # Keep rows with missing values initially so that we get the correct column
   # names. Explicitly drop groups with missing values in second step.
   unique_groups_counts <- as.data.frame(
-    table(df[, groupby_vars, with=FALSE], exclude=NULL, dnn=groupby_vars), 
+    table(df[, group_vars, with=FALSE], exclude=NULL, dnn=group_vars), 
     stringsAsFactors=FALSE
   )
   unique_groups_counts <- unique_groups_counts[
-    complete.cases(unique_groups_counts[, groupby_vars]),
+    complete.cases(unique_groups_counts[, group_vars]),
   ]
 
   # Drop groups with less than threshold sample size.
@@ -283,7 +283,7 @@ summarize_aggs <- function(df, crosswalk_data, aggregations, geo_level, params)
   ## Convert col type in unique_groups to match that in data.
   # Filter on data.table in `calculate_group` requires that columns and filter
   # values are of the same type.
-  for (col_var in groupby_vars) {
+  for (col_var in group_vars) {
     if ( class(df[[col_var]]) != class(unique_groups_counts[[col_var]]) ) {
       class(unique_groups_counts[[col_var]]) <- class(df[[col_var]])
     }
@@ -295,10 +295,10 @@ summarize_aggs <- function(df, crosswalk_data, aggregations, geo_level, params)
   setindexv(df, group_vars)
 
   calculate_group <- function(ii) {
-    target_group <- unique_groups_counts[ii, groupby_vars, drop=FALSE]
+    target_group <- unique_groups_counts[ii, group_vars, drop=FALSE]
     # Use data.table's index to make this filter efficient
     out <- summarize_aggregations_group(
-      df[as.list(target_group), on=groupby_vars],
+      df[as.list(target_group), on=group_vars],
       aggregations,
       target_group,
       geo_level,

facebook/delphiFacebook/R/responses.R

@@ -159,6 +159,7 @@ load_response_one <- function(input_filename, params) {
   input_data <- code_testing(input_data)
   input_data <- code_activities(input_data)
   input_data <- code_vaccines(input_data)
+  input_data <- code_schooling(input_data)
 
   # create testing variables
 

facebook/delphiFacebook/R/variables.R

@@ -471,3 +471,32 @@ code_vaccines <- function(input_data) {
 
   return(input_data)
 }
+
+#' Schooling
+#'
+#' @param input_data input data frame of raw survey data
+#' @return data frame augmented with `hh_number_total`
+code_schooling <- function(input_data) {
+  if ("E2_1" %in% names(input_data)) {
+    # Coded as 2 = "Yes", 3 = "No", 4 = "I don't know"
+    input_data$s_inperson_school_fulltime <- case_when(
+      input_data$E2_1 == 2 ~ 1,
+      input_data$E2_1 == 3 ~ 0,
+      TRUE ~ NA_real_
+    )
+  } else {
+    input_data$s_inperson_school_fulltime <- NA_real_
+  }
+
+  if ("E2_2" %in% names(input_data)) {
+    # Coded as 2 = "Yes", 3 = "No", 4 = "I don't know"
+    input_data$s_inperson_school_parttime <- case_when(
+      input_data$E2_2 == 2 ~ 1,
+      input_data$E2_2 == 3 ~ 0,
+      TRUE ~ NA_real_
+    )
+  } else {
+    input_data$s_inperson_school_parttime <- NA_real_
+  }
+  return(input_data)
+}