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Revert "Start support for phone_keyboard" 

This reverts commit dd95b4f941.
pull/108/head
JulioV 2020-12-20 16:29:40 -05:00
parent 5e87db9952
commit 51c7739bfc
4 changed files with 0 additions and 84 deletions
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9
Snakefile
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@ -100,15 +100,6 @@ for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
for provider in config["PHONE_KEYBOARD"]["PROVIDERS"].keys():
if config["PHONE_KEYBOARD"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_keyboard_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_keyboard_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_keyboard_features/phone_keyboard_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_KEYBOARD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_keyboard.csv", pid=config["PIDS"],))
files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys():
if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"]))

9
config.yaml
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@ -189,15 +189,6 @@ PHONE_DATA_YIELD:
SRC_LANGUAGE: "r"
SRC_FOLDER: "rapids" # inside src/features/phone_data_yield
PHONE_KEYBOARD:
TABLE: keyboard
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: []
SRC_FOLDER: "rapids" # inside src/features/phone_keyboard
SRC_LANGUAGE: "python"
# See https://www.rapids.science/latest/features/phone-light/
PHONE_LIGHT:
TABLE: light

26
rules/features.smk
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@ -262,32 +262,6 @@ rule phone_light_r_features:
script:
"../src/features/entry.R"
rule phone_keyboard_python_features:
input:
sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",
time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
params:
provider = lambda wildcards: config["PHONE_KEYBOARD"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "phone_keyboard"
output:
"data/interim/{pid}/phone_keyboard_features/phone_keyboard_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule phone_keyboard_r_features:
input:
sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",
time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
params:
provider = lambda wildcards: config["PHONE_KEYBOARD"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "phone_keyboard"
output:
"data/interim/{pid}/phone_keyboard_features/phone_keyboard_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule phone_locations_python_features:
input:
sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv",

40
src/features/phone_keyboard/rapids/main.py
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@ -1,40 +0,0 @@
import pandas as pd
import numpy as np
def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
# I copied this from light, modify it to make it work for keyboard
light_data = pd.read_csv(sensor_data_files["sensor_data"])
print(light_data)
raise ValueError("Test")
requested_features = provider["FEATURES"]
# name of the features this function can compute
base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
# the subset of requested features this function can compute
features_to_compute = list(set(requested_features) & set(base_features_names))
light_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
if not light_data.empty:
light_data = filter_data_by_segment(light_data, time_segment)
if not light_data.empty:
light_features = pd.DataFrame()
if "count" in features_to_compute:
light_features["count"] = light_data.groupby(["local_segment"]).count()["timestamp"]
# get light ambient luminance related features
if "maxlux" in features_to_compute:
light_features["maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max()
if "minlux" in features_to_compute:
light_features["minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min()
if "avglux" in features_to_compute:
light_features["avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean()
if "medianlux" in features_to_compute:
light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
if "stdlux" in features_to_compute:
light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
light_features = light_features.reset_index()
return light_features