Refactor PHONE_CALLS RAPIDS provider to compute features based on call episodes or events

Snakefile

@@ -45,7 +45,12 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
 for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
     if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
         files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"]))
+        if (provider == "RAPIDS") and (config["PHONE_CALLS"]["PROVIDERS"][provider]["FEATURES_TYPE"] == "EPISODES"):
+            files_to_compute.extend(expand("data/interim/{pid}/phone_calls_episodes.csv", pid=config["PIDS"]))
+            files_to_compute.extend(expand("data/interim/{pid}/phone_calls_episodes_resampled.csv", pid=config["PIDS"]))
+            files_to_compute.extend(expand("data/interim/{pid}/phone_calls_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
+        else:
+            files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=get_script_language(config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))

config.yaml

@@ -181,6 +181,7 @@ PHONE_CALLS:
   PROVIDERS:
     RAPIDS:
       COMPUTE: False
+      FEATURES_TYPE: EPISODES # EVENTS or EPISODES
       CALL_TYPES: [missed, incoming, outgoing]
       FEATURES:
         missed:  [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]

docs/change-log.md

@@ -1,4 +1,6 @@
 # Change Log
+## v1.6.0
+- Refactor PHONE_CALLS RAPIDS provider to compute features based on call episodes or events
 ## v1.5.0
 - Update Barnett location features with faster Python implementation
 - Fix rounding bug in data yield features

docs/features/phone-calls.md

@@ -26,6 +26,7 @@ Parameters description for `[PHONE_CALLS][PROVIDERS][RAPIDS]`:
 | Key                               | Description |
 |-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 |`[COMPUTE]`| Set to `True` to extract `PHONE_CALLS` features from the `RAPIDS` provider|
+|`[FEATURES_TYPE]`| Set to `EPISODES` to extract features based on call episodes or `EVENTS` to extract features based on events.|
 | `[CALL_TYPES]`   | The particular call_type that will be analyzed. The options for this parameter are incoming, outgoing or missed.                                                                                                                                                 |
 | `[FEATURES]`    | Features to be computed for `outgoing`, `incoming`, and `missed` calls. Note that the same features are available for both incoming and outgoing calls, while missed calls has its own set of features. See the tables below. |
 
@@ -60,4 +61,4 @@ Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` missed calls:
 !!! note "Assumptions/Observations"
     1. Traces for iOS calls are unique even for the same contact calling a participant more than once which renders `countmostfrequentcontact` meaningless and `distinctcontacts` equal to the total number of traces. 
     2. `[CALL_TYPES]` and `[FEATURES]` keys in `config.yaml` need to match. For example, `[CALL_TYPES]` `outgoing` matches the `[FEATURES]` key `outgoing`
-    3. iOS calls data is transformed to match Android calls data format. See our [algorithm](algorithms/phone-algorithms.md#phone-calls)
+    3. iOS calls data is transformed to match Android calls data format.

rules/common.smk

@@ -27,6 +27,12 @@ def get_locations_python_input(wildcards):
     else:
         return "data/interim/{pid}/phone_locations_processed_with_datetime.csv"
 
+def get_calls_input(wildcards):
+    if (wildcards.provider_key.upper() == "RAPIDS") and (config["PHONE_CALLS"]["PROVIDERS"]["RAPIDS"]["FEATURES_TYPE"] == "EPISODES"):
+        return "data/interim/{pid}/phone_calls_episodes_resampled_with_datetime.csv"
+    else:
+        return "data/raw/{pid}/phone_calls_with_datetime.csv"
+
 def find_features_files(wildcards):
     feature_files = []
     for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items():

rules/features.smk

@@ -264,9 +264,17 @@ rule phone_bluetooth_r_features:
     script:
         "../src/features/entry.R"
 
-rule calls_python_features:
+rule calls_episodes:
     input:
-        sensor_data = "data/raw/{pid}/phone_calls_with_datetime.csv",
+        calls = "data/raw/{pid}/phone_calls_raw.csv"
+    output:
+        "data/interim/{pid}/phone_calls_episodes.csv"
+    script:
+        "../src/features/phone_calls/episodes/calls_episodes.py"
+
+rule phone_calls_python_features:
+    input:
+        sensor_data = get_calls_input,
         time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
     params:
         provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()],
@@ -277,9 +285,9 @@ rule calls_python_features:
     script:
         "../src/features/entry.py"
 
-rule calls_r_features:
+rule phone_calls_r_features:
     input:
-        sensor_data = "data/raw/{pid}/phone_calls_with_datetime.csv",
+        sensor_data = get_calls_input,
         time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
     params:
         provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()],

src/features/phone_calls/episodes/calls_episodes.py

@@ -0,0 +1,7 @@
+import pandas as pd
+
+calls = pd.read_csv(snakemake.input["calls"]).rename(columns={"timestamp": "start_timestamp"})
+calls["end_timestamp"] = calls["start_timestamp"] + calls["call_duration"] * 1000
+calls["episode_id"] = calls.index
+
+calls[["episode_id", "device_id", "call_type", "trace", "start_timestamp", "end_timestamp"]].to_csv(snakemake.output[0], index=False)

src/features/phone_calls/rapids/main.R

@@ -7,7 +7,7 @@ Mode <- function(v) {
   uniqv[which.max(tabulate(match(v, uniqv)))]
 }
 
-call_features_of_type <- function(calls, call_type, time_segment, requested_features){
+call_features_of_type <- function(calls, features_type, call_type, time_segment, requested_features){
     # Output dataframe
     features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
 
@@ -22,6 +22,15 @@ call_features_of_type <- function(calls, call_type, time_segment, requested_feat
     if(nrow(calls) < 1)
         return(cbind(features, read.csv(text = paste(paste(call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE)))
 
+    if(features_type == "EPISODES"){
+        calls <- calls %>% 
+            mutate(call_duration = (end_timestamp - start_timestamp) / 1000) %>% 
+            separate(local_start_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>%
+            separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>%
+            mutate(local_hour = as.numeric(local_hour),
+                local_minute = as.numeric(local_minute))
+    }
+
     for(feature_name in features_to_compute){
         if(feature_name == "countmostfrequentcontact"){
             # Get the number of messages for the most frequent contact throughout the study
@@ -62,6 +71,8 @@ call_features_of_type <- function(calls, call_type, time_segment, requested_feat
 rapids_features <- function(sensor_data_files, time_segment, provider){
     calls_data <-  read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
     calls_data <- calls_data %>% filter_data_by_segment(time_segment)
+
+    features_type <- provider[["FEATURES_TYPE"]]
     call_types = provider[["CALL_TYPES"]]
     call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment"))
 
@@ -74,7 +85,7 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
         requested_features <- provider[["FEATURES"]][[call_type]]
         calls_of_type <- calls_data %>% filter(call_type == call_type_label)
 
-        features <- call_features_of_type(calls_of_type, call_type, time_segment, requested_features)
+        features <- call_features_of_type(calls_of_type, features_type, call_type, time_segment, requested_features)
         call_features <- merge(call_features, features, all=TRUE)
     }
     call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0)))

src/features/utils/utils.R

@@ -1,4 +1,5 @@
 library("stringr")
+library('purrr')
 
 rapids_log_tag <- "RAPIDS:"
 

tools/config.schema.yaml

@@ -489,6 +489,9 @@ properties:
             allOf:
                 - $ref: "#/definitions/PROVIDER"
                 - properties:
+                    FEATURES_TYPE:
+                      type: string
+                      enum: [EVENTS, EPISODES]
                     CALL_TYPES:
                       type: array
                       items: