stress_at_work_analysis/features/communication.py

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from collections.abc import Collection
import pandas as pd
from config.models import SMS, Call, Participant
from setup import db_engine, session
call_types = {1: "incoming", 2: "outgoing", 3: "missed"}
sms_types = {1: "received", 2: "sent"}
FILL_NA_CALLS = {
"no_calls_all": 0,
"no_" + call_types.get(1): 0,
"no_" + call_types.get(2): 0,
"no_" + call_types.get(3): 0,
"duration_total_" + call_types.get(1): 0,
"duration_total_" + call_types.get(2): 0,
"duration_max_" + call_types.get(1): 0,
"duration_max_" + call_types.get(2): 0,
"no_" + call_types.get(1) + "_ratio": 1 / 3, # Three different types
"no_" + call_types.get(2) + "_ratio": 1 / 3,
"no_contacts_calls": 0,
}
FEATURES_CALLS = list(FILL_NA_CALLS.keys())
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# FEATURES_CALLS =
# ["no_calls_all",
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# "no_incoming", "no_outgoing", "no_missed",
# "duration_total_incoming", "duration_total_outgoing",
# "duration_max_incoming", "duration_max_outgoing",
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# "no_incoming_ratio", "no_outgoing_ratio",
# "no_contacts_calls"]
FILL_NA_SMS = {
"no_sms_all": 0,
"no_" + sms_types.get(1): 0,
"no_" + sms_types.get(2): 0,
"no_" + sms_types.get(1) + "_ratio": 1 / 2, # Two different types
"no_" + sms_types.get(2) + "_ratio": 1 / 2,
"no_contacts_sms": 0,
}
FEATURES_SMS = list(FILL_NA_SMS.keys())
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# FEATURES_SMS =
# ["no_sms_all",
# "no_received", "no_sent",
# "no_received_ratio", "no_sent_ratio",
# "no_contacts_sms"]
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FEATURES_CALLS_SMS_PROP = [
"proportion_calls_all",
"proportion_calls_incoming",
"proportion_calls_outgoing",
"proportion_calls_contacts",
"proportion_calls_missed_sms_received",
]
FILL_NA_CALLS_SMS_PROP = {
key: 1 / 2 for key in FEATURES_CALLS_SMS_PROP
} # All of the form of a / (a + b).
FEATURES_CALLS_SMS_ALL = FEATURES_CALLS + FEATURES_SMS + FEATURES_CALLS_SMS_PROP
FILL_NA_CALLS_SMS_ALL = FILL_NA_CALLS | FILL_NA_SMS | FILL_NA_CALLS_SMS_PROP
# As per PEP-584 a union for dicts was implemented in Python 3.9.0.
def get_call_data(usernames: Collection) -> pd.DataFrame:
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"""
Read the data from the calls table and return it in a dataframe.
Parameters
----------
usernames: Collection
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A list of usernames to put into the WHERE condition.
Returns
-------
df_calls: pd.DataFrame
A dataframe of call data.
"""
query_calls = (
session.query(Call, Participant.username)
.filter(Participant.id == Call.participant_id)
.filter(Participant.username.in_(usernames))
)
with db_engine.connect() as connection:
df_calls = pd.read_sql(query_calls.statement, connection)
return df_calls
def get_sms_data(usernames: Collection) -> pd.DataFrame:
"""
Read the data from the sms table and return it in a dataframe.
Parameters
----------
usernames: Collection
A list of usernames to put into the WHERE condition.
Returns
-------
df_sms: pd.DataFrame
A dataframe of call data.
"""
query_sms = (
session.query(SMS, Participant.username)
.filter(Participant.id == SMS.participant_id)
.filter(Participant.username.in_(usernames))
)
with db_engine.connect() as connection:
df_sms = pd.read_sql(query_sms.statement, connection)
return df_sms
def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame:
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"""
Count contacts (callers, senders) and enumerate them by their frequency.
Parameters
----------
comm_df: pd.DataFrame
A dataframe of calls or SMSes.
Returns
-------
comm_df: pd.DataFrame
The altered dataframe with the column contact_id, arranged by frequency.
"""
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contact_counts = (
comm_df.groupby(
["participant_id", "trace"]
) # We want to count rows by participant_id and trace
.size() # Count rows
.reset_index() # Make participant_id a regular column.
.rename(columns={0: "freq"})
.sort_values(["participant_id", "freq"], ascending=False)
# First sort by participant_id and then by call frequency.
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)
# We now have a frequency table of different traces (contacts) *within* each participant_id.
# Next, enumerate these contacts.
# In other words, recode the contacts into integers from 0 to n_contacts,
# so that the first one is contacted the most often.
contact_ids = (
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# Group again for enumeration.
contact_counts.groupby("participant_id")
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.cumcount() # Enumerate (count) rows *within* participants.
.to_frame("contact_id")
)
contact_counts = contact_counts.join(contact_ids)
# Add these contact_ids to the temporary (grouped) data frame.
comm_df = comm_df.merge(contact_counts, on=["participant_id", "trace"])
# Add these contact_ids to the original data frame.
return comm_df
def count_comms(comm_df: pd.DataFrame, group_by=None) -> pd.DataFrame:
"""
Calculate frequencies (and duration) of messages (or calls), grouped by their types.
Parameters
----------
comm_df: pd.DataFrame
A dataframe of calls or SMSes.
group_by: list
A list of strings, specifying by which parameters to group.
By default, the features are calculated per participant, but could be "date_lj" etc.
Returns
-------
comm_features: pd.DataFrame
A list of communication features for every participant.
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These are:
* the number of calls by type (incoming, outgoing missed) and in total,
* the ratio of incoming and outgoing calls to the total number of calls,
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* the total and maximum duration of calls by type,
* the number of messages by type (received, sent), and
* the number of communication contacts by type.
"""
if group_by is None:
group_by = []
if "call_type" in comm_df:
data_type = "calls"
comm_counts = (
comm_df.value_counts(subset=group_by + ["participant_id", "call_type"])
.unstack(level="call_type", fill_value=0)
.rename(columns=call_types)
.add_prefix("no_")
)
# Count calls by type.
comm_counts["no_calls_all"] = comm_counts.sum(axis=1)
# Add a total count of calls.
comm_counts = comm_counts.assign(
no_incoming_ratio=lambda x: x.no_incoming / x.no_calls_all,
no_outgoing_ratio=lambda x: x.no_outgoing / x.no_calls_all,
)
# Ratio of incoming and outgoing calls to all calls.
comm_duration_total = (
comm_df.groupby(group_by + ["participant_id", "call_type"])
.sum()["call_duration"]
.unstack(level="call_type", fill_value=0)
.rename(columns=call_types)
.add_prefix("duration_total_")
)
# Total call duration by type.
comm_duration_max = (
comm_df.groupby(group_by + ["participant_id", "call_type"])
.max()["call_duration"]
.unstack(level="call_type", fill_value=0)
.rename(columns=call_types)
.add_prefix("duration_max_")
)
# Max call duration by type
comm_features = comm_counts.join(comm_duration_total)
comm_features = comm_features.join(comm_duration_max)
try:
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comm_features.drop(columns="duration_total_" + call_types[3], inplace=True)
comm_features.drop(columns="duration_max_" + call_types[3], inplace=True)
# The missed calls are always of 0 duration.
except KeyError:
pass
# If there were no missed calls, this exception is raised.
# But we are dropping the column anyway, so no need to deal with the exception.
elif "message_type" in comm_df:
data_type = "sms"
comm_counts = (
comm_df.value_counts(subset=group_by + ["participant_id", "message_type"])
.unstack(level="message_type", fill_value=0)
.rename(columns=sms_types)
.add_prefix("no_")
)
comm_counts["no_sms_all"] = comm_counts.sum(axis=1)
# Add a total count of messages.
comm_features = comm_counts.assign(
no_received_ratio=lambda x: x.no_received / x.no_sms_all,
no_sent_ratio=lambda x: x.no_sent / x.no_sms_all,
)
# Ratio of incoming and outgoing messages to all messages.
else:
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raise KeyError("The dataframe contains neither call_type or message_type")
comm_contacts_counts = (
enumerate_contacts(comm_df)
.groupby(group_by + ["participant_id"])
.nunique()["contact_id"]
.rename("no_contacts_" + data_type)
)
# Number of communication contacts
comm_features = comm_features.join(comm_contacts_counts)
return comm_features
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def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
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"""
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For each participant and for each of his contacts, this function
counts the number of communications (by type) between them. If the
argument passed is a dataframe with calls data, it additionally counts
the total duration of calls between every pair (participant, contact).
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Parameters
----------
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comm_df: pd.DataFrame
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A dataframe of calls or SMSes.
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Returns
-------
comm_df: pd.DataFrame
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A new dataframe with a row for each pair (participant, contact).
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"""
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df_enumerated = enumerate_contacts(comm_df)
contacts_count = (
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df_enumerated.groupby(["participant_id", "contact_id"]).size().reset_index()
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)
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# Check whether df contains calls or SMS data since some
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# features we want to calculate are type-specific
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if "call_duration" in df_enumerated:
# Add a column with the total duration of calls between two people
duration_count = (
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df_enumerated.groupby(["participant_id", "contact_id"])
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# For each participant and for each caller, sum durations of their calls
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["call_duration"]
.sum()
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.reset_index() # Make index (which is actually the participant id) a normal column
.rename(columns={"call_duration": "total_call_duration"})
)
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contacts_count = contacts_count.merge(
duration_count, on=["participant_id", "contact_id"]
)
contacts_count.rename(columns={0: "no_calls"}, inplace=True)
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else:
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contacts_count.rename(columns={0: "no_sms"}, inplace=True)
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# TODO:Determine work vs non-work contacts by work hours heuristics
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return contacts_count
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def calls_sms_features(
df_calls: pd.DataFrame, df_sms: pd.DataFrame, group_by=None
) -> pd.DataFrame:
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"""
Calculates additional features relating calls and sms data.
Parameters
----------
df_calls: pd.DataFrame
A dataframe of calls (return of get_call_data).
df_sms: pd.DataFrame
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A dataframe of SMSes (return of get_sms_data).
group_by: list
A list of strings, specifying by which parameters to group.
By default, the features are calculated per participant, but could be "date_lj" etc.
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Returns
-------
df_calls_sms: pd.DataFrame
The list of features relating calls and sms data for every participant.
These are:
* proportion_calls_all:
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proportion of calls in total number of communications
* proportion_calls_incoming:
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proportion of incoming calls in total number of incoming/received communications
* proportion_calls_outgoing:
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proportion of outgoing calls in total number of outgoing/sent communications
* proportion_calls_missed_sms_received:
proportion of missed calls to the number of received messages
* proportion_calls_contacts:
proportion of calls contacts in total number of communication contacts
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"""
if group_by is None:
group_by = []
count_calls = count_comms(df_calls, group_by)
count_sms = count_comms(df_sms, group_by)
count_joined = (
count_calls.merge(
count_sms,
how="outer",
left_index=True,
right_index=True,
validate="one_to_one",
)
.fillna(0, downcast="infer")
.assign(
proportion_calls_all=(
lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
),
proportion_calls_incoming=(
lambda x: x.no_incoming / (x.no_incoming + x.no_received)
),
proportion_calls_missed_sms_received=(
lambda x: x.no_missed / (x.no_missed + x.no_received)
),
proportion_calls_outgoing=(
lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
),
proportion_calls_contacts=(
lambda x: x.no_contacts_calls
/ (x.no_contacts_calls + x.no_contacts_sms)
)
# Calculate new features and create additional columns
)
.fillna(0.5, downcast="infer")
)
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return count_joined