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Delete unused files

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JulioV 2020-09-02 14:12:53 -04:00
parent 98ebf9bd13
commit 1ed85f1180
8 changed files with 0 additions and 166 deletions
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36
src/features/applications_foreground_features.py
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import pandas as pd
from applications_foreground.applications_foreground_base import base_applications_foreground_features
apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"], encoding="ISO-8859-1")
day_segment = snakemake.params["day_segment"]
single_categories = snakemake.params["single_categories"]
multiple_categories_with_genres = snakemake.params["multiple_categories"]
single_apps = snakemake.params["single_apps"]
excluded_categories = snakemake.params["excluded_categories"]
excluded_apps = snakemake.params["excluded_apps"]
requested_features = snakemake.params["features"]
apps_features = pd.DataFrame(columns=["local_date"])
single_categories = list(set(single_categories) - set(excluded_categories))
multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories))
apps = list(set(single_apps) - set(excluded_apps))
type_count = len(single_categories) + len(multiple_categories) + len(apps)
params = {}
params["multiple_categories_with_genres"] = multiple_categories_with_genres
params["single_categories"] = single_categories
params["multiple_categories"] = multiple_categories
params["apps"] = apps
# exclude categories in the excluded_categories list
if "system_apps" in excluded_categories:
apps_data = apps_data[apps_data["is_system_app"] == 0]
apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)]
# exclude apps in the excluded_apps list
apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)]
apps_features = apps_features.merge(base_applications_foreground_features(apps_data, day_segment, requested_features, params), on="local_date", how="outer")
assert len(requested_features) * type_count + 1 == apps_features.shape[1], "The number of features in the output dataframe (=" + str(apps_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your application foreground feature extraction functions"
apps_features.to_csv(snakemake.output[0], index=False)

20
src/features/bluetooth_features.R
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source("renv/activate.R")
source("src/features/bluetooth/bluetooth_base.R")
library(dplyr)
library(tidyr)
bluetooth_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
day_segments <- read.csv(snakemake@input[["day_segments"]], stringsAsFactors = FALSE)
requested_features <- snakemake@params[["features"]]
features = data.frame(local_date = character(), stringsAsFactors = FALSE)
day_segments <- day_segments %>% distinct(label) %>% pull(label)
# Compute base bluetooth features
for (day_segment in day_segments)
features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE)
if(ncol(features) != (length(requested_features)) * length(day_segments) + 1)
stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your bluetooth feature extraction functions"))
write.csv(features, snakemake@output[[1]], row.names = FALSE)

0
src/features/build_features.py
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23
src/features/call_features.R
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source("renv/activate.R")
source("src/features/call/call_base.R")
library(dplyr)
calls <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]])
requested_features <- snakemake@params[["features"]]
call_type <- snakemake@params[["call_type"]]
features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
day_segments <- day_segments_labels %>% pull(label)
for (day_segment in day_segments)
features <- merge(features, base_call_features(calls, call_type, day_segment, requested_features), all = TRUE)
if(ncol(features) != length(requested_features) + 1)
stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Call feature extraction functions"))
features <- features %>% separate(col = local_segment,
into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"),
sep = "#",
remove = FALSE)
write.csv(features, snakemake@output[[1]], row.names = FALSE)

15
src/features/conversation_features.py
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import pandas as pd
from conversation.conversation_base import base_conversation_features
conversation_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
day_segment = snakemake.params["day_segment"]
requested_features = snakemake.params["features"]
recordingMinutes = snakemake.params["recordingMinutes"]
pausedMinutes = snakemake.params["pausedMinutes"]
expectedMinutes = 1440 / (recordingMinutes + pausedMinutes)
conversation_features = pd.DataFrame(columns=["local_date"])
conversation_features = conversation_features.merge(base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes), on="local_date", how="outer")
assert len(requested_features) + 1 == conversation_features.shape[1], "The number of features in the output dataframe (=" + str(conversation_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your conversation feature extraction functions"
conversation_features.to_csv(snakemake.output[0], index=False)

13
src/features/light_features.py
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import pandas as pd
from light.light_base import base_light_features
light_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
day_segment = snakemake.params["day_segment"]
requested_features = snakemake.params["features"]
light_features = pd.DataFrame(columns=["local_date"])
light_features = light_features.merge(base_light_features(light_data, day_segment, requested_features), on="local_date", how="outer")
assert len(requested_features) + 1 == light_features.shape[1], "The number of features in the output dataframe (=" + str(light_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your light feature extraction functions"
light_features.to_csv(snakemake.output[0], index=False)

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src/features/messages_features.R
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# If you want to implement extra features, source(..) a new file and duplicate the line "features <- merge(...)", then
# swap base_sms_features(...) for your own function
source("renv/activate.R")
source("src/features/messages/messages_base.R")
library("dplyr", warn.conflicts = FALSE)
messages <- read.csv(snakemake@input[[1]])
day_segments_labels <- read.csv(snakemake@input[["day_segments_labels"]])
requested_features <- snakemake@params[["features"]]
messages_type <- snakemake@params[["messages_type"]]
features <- data.frame(local_segment = character(), stringsAsFactors = FALSE)
day_segments <- day_segments_labels %>% pull(label)
for (day_segment in day_segments)
features <- merge(features, base_messages_features(messages, messages_type, day_segment, requested_features), all = TRUE)
if(ncol(features) != length(requested_features) + 1)
stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Messages (SMS) feature extraction functions"))
features <- features %>% separate(col = local_segment,
into = c("local_segment_label", "local_start_date", "local_start_time", "local_end_date", "local_end_time"),
sep = "#",
remove = FALSE)
write.csv(features, snakemake@output[[1]], row.names = FALSE)

33
src/features/wifi_features.R
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source("renv/activate.R")
source("src/features/wifi/wifi_base.R")
library("dplyr")
if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@input[["connected_access_points"]])){
wifi_data <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE)
wifi_data <- wifi_data %>% mutate(connected = 0)
} else if(is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){
wifi_data <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE)
wifi_data <- wifi_data %>% mutate(connected = 1)
} else if(!is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){
visible_access_points <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE)
visible_access_points <- visible_access_points %>% mutate(connected = 0)
connected_access_points <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE)
connected_access_points <- connected_access_points %>% mutate(connected = 1)
wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp)
}
wifi_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
day_segments <- read.csv(snakemake@input[["day_segments"]])
requested_features <- snakemake@params[["features"]]
features = data.frame(local_date = character(), stringsAsFactors = FALSE)
day_segments <- day_segments %>% distinct(label) %>% pull(label)
# Compute base wifi features
for (day_segment in day_segments)
features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE)
if(ncol(features) != (length(requested_features)) * length(day_segments) + 1)
stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your wifi feature extraction functions"))
write.csv(features, snakemake@output[[1]], row.names = FALSE)