313 lines
11 KiB
Python
313 lines
11 KiB
Python
from collections.abc import Collection
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import pandas as pd
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from config.models import SMS, Call, Participant
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from setup import db_engine, session
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call_types = {1: "incoming", 2: "outgoing", 3: "missed"}
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sms_types = {1: "received", 2: "sent"}
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def get_call_data(usernames: Collection) -> pd.DataFrame:
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"""
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Read the data from the calls table and return it in a dataframe.
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Parameters
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----------
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usernames: Collection
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A list of usernames to put into the WHERE condition.
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Returns
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-------
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df_calls: pd.DataFrame
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A dataframe of call data.
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"""
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query_calls = (
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session.query(Call, Participant.username)
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.filter(Participant.id == Call.participant_id)
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.filter(Participant.username.in_(usernames))
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)
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with db_engine.connect() as connection:
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df_calls = pd.read_sql(query_calls.statement, connection)
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return df_calls
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def get_sms_data(usernames: Collection) -> pd.DataFrame:
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"""
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Read the data from the sms table and return it in a dataframe.
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Parameters
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----------
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usernames: Collection
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A list of usernames to put into the WHERE condition.
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Returns
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-------
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df_sms: pd.DataFrame
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A dataframe of call data.
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"""
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query_sms = (
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session.query(SMS, Participant.username)
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.filter(Participant.id == SMS.participant_id)
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.filter(Participant.username.in_(usernames))
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)
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with db_engine.connect() as connection:
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df_sms = pd.read_sql(query_sms.statement, connection)
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return df_sms
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def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame:
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"""
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Count contacts (callers, senders) and enumerate them by their frequency.
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Parameters
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----------
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comm_df: pd.DataFrame
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A dataframe of calls or SMSes.
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Returns
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-------
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comm_df: pd.DataFrame
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The altered dataframe with the column contact_id, arranged by frequency.
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"""
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contact_counts = (
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comm_df.groupby(
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["participant_id", "trace"]
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) # We want to count rows by participant_id and trace
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.size() # Count rows
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.reset_index() # Make participant_id a regular column.
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.rename(columns={0: "freq"})
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.sort_values(["participant_id", "freq"], ascending=False)
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# First sort by participant_id and then by call frequency.
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)
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# We now have a frequency table of different traces (contacts) *within* each participant_id.
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# Next, enumerate these contacts.
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# In other words, recode the contacts into integers from 0 to n_contacts,
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# so that the first one is contacted the most often.
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contact_ids = (
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# Group again for enumeration.
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contact_counts.groupby("participant_id")
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.cumcount() # Enumerate (count) rows *within* participants.
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.to_frame("contact_id")
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)
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contact_counts = contact_counts.join(contact_ids)
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# Add these contact_ids to the temporary (grouped) data frame.
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comm_df = comm_df.merge(contact_counts, on=["participant_id", "trace"])
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# Add these contact_ids to the original data frame.
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return comm_df
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def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
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"""
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Calculate frequencies (and duration) of messages (or calls), grouped by their types.
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Parameters
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----------
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comm_df: pd.DataFrame
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A dataframe of calls or SMSes.
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Returns
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-------
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comm_features: pd.DataFrame
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A list of communication features for every participant.
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These are:
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* the number of calls by type (incoming, outgoing missed) and in total,
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* 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,
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* the number of messages by type (received, sent), and
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* the number of communication contacts by type.
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"""
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if "call_type" in comm_df:
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comm_counts = (
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comm_df.value_counts(subset=["participant_id", "call_type"])
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.unstack()
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.rename(columns=call_types)
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.add_prefix("no_")
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)
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# Count calls by type.
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comm_counts["no_all"] = comm_counts.sum(axis=1)
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# Add a total count of calls.
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comm_counts = comm_counts.assign(
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no_incoming_ratio=lambda x: x.no_incoming / x.no_all,
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no_outgoing_ratio=lambda x: x.no_outgoing / x.no_all,
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)
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# Ratio of incoming and outgoing calls to all calls.
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comm_duration_total = (
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comm_df.groupby(["participant_id", "call_type"])
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.sum()["call_duration"]
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.unstack()
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.rename(columns=call_types)
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.add_prefix("duration_total_")
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)
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# Total call duration by type.
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comm_duration_max = (
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comm_df.groupby(["participant_id", "call_type"])
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.max()["call_duration"]
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.unstack()
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.rename(columns=call_types)
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.add_prefix("duration_max_")
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)
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# Max call duration by type
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comm_contacts_counts = (
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enumerate_contacts(comm_df)
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.groupby(["participant_id"])
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.nunique()["contact_id"]
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.reset_index()
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.rename(columns={"contact_id": "no_contacts"})
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)
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# Number of communication contacts
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comm_features = comm_counts.join(comm_duration_total)
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comm_features = comm_features.join(comm_duration_max)
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comm_features = comm_features.merge(
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comm_contacts_counts,
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on="participant_id"
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).set_index("participant_id")
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try:
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comm_features.drop(columns="duration_total_" + call_types[3], inplace=True)
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comm_features.drop(columns="duration_max_" + call_types[3], inplace=True)
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# The missed calls are always of 0 duration.
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except KeyError:
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pass
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# If there were no missed calls, this exception is raised.
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# But we are dropping the column anyway, so no need to deal with the exception.
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elif "message_type" in comm_df:
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comm_counts = (
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comm_df.value_counts(subset=["participant_id", "message_type"])
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.unstack()
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.rename(columns=sms_types)
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.add_prefix("no_")
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)
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comm_counts["no_all"] = comm_counts.sum(axis=1)
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# Add a total count of messages.
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comm_features = comm_counts.assign(
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no_received_ratio=lambda x: x.no_received / x.no_all,
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no_sent_ratio=lambda x: x.no_sent / x.no_all,
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)
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# Ratio of incoming and outgoing messages to all messages.
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comm_contacts_counts = (
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enumerate_contacts(comm_df)
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.groupby(["participant_id"])
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.nunique()["contact_id"]
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.reset_index()
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.rename(columns={"contact_id": "no_contacts"})
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)
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# Number of communication contacts
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comm_features = comm_features.merge(
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comm_contacts_counts,
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on="participant_id"
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).set_index("participant_id")
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else:
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raise KeyError("The dataframe contains neither call_type or message_type")
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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
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counts the number of communications (by type) between them. If the
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argument passed is a dataframe with calls data, it additionally counts
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the total duration of calls between every pair (participant, contact).
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Parameters
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----------
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df_enumerated: pd.DataFrame
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A dataframe of calls or SMSes.
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Returns
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-------
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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)
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contacts_count = (
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df_enumerated
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.groupby(["participant_id","contact_id"])
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.size()
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.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-specyfic
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if "call_duration" in df_enumerated:
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# Add a column with the total duration of calls between two people
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duration_count = (
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df_enumerated
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.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"]
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.sum()
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.reset_index() # Make index (which is actually the participant id) a normal column
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.rename(columns={"call_duration": "total_call_duration"})
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)
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contacts_count = contacts_count.merge(duration_count, on=["participant_id", "contact_id"])
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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) -> pd.DataFrame:
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"""
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Calculates additional features relating calls and sms data.
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Parameters
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----------
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df_calls: pd.DataFrame
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A dataframe of calls (return of get_call_data).
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df_sms: pd.DataFrame
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A dataframe of SMSes (return of get_sms_data).
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Returns
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-------
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df_calls_sms: pd.DataFrame
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The list of features relating calls and sms data for every participant.
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These are:
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* proportion_calls:
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proportion of calls in total number of communications
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* proportion_calls_incoming:
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proportion of incoming calls in total number of incoming/received communications
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* proportion_calls_outgoing:
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proportion of outgoing calls in total number of outgoing/sent communications
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* proportion_calls_missed_sms_received:
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proportion of missed calls to the number of received messages
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* proportion_calls_contacts:
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proportion of calls contacts in total number of communication contacts
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"""
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count_calls = count_comms(df_calls)
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count_sms = count_comms(df_sms)
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count_joined = (
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count_calls.merge(
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count_sms, on="participant_id", suffixes=("_calls", "_sms")
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) # Merge calls and sms features
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.reset_index() # Make participant_id a regular column
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.assign(
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proportion_calls=(
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lambda x: x.no_all_calls / (x.no_all_calls + x.no_all_sms)
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),
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proportion_calls_incoming=(
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lambda x: x.no_incoming / (x.no_incoming + x.no_received)
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),
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proportion_calls_missed_sms_received=(
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lambda x: x.no_missed / (x.no_missed + x.no_received)
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),
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proportion_calls_outgoing=(
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lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
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),
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proportion_calls_contacts=(
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lambda x: x.no_contacts_calls / (x.no_contacts_calls + x.no_contacts_sms)
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)
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# Calculate new features and create additional columns
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)[
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[
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"participant_id",
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"proportion_calls",
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"proportion_calls_incoming",
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"proportion_calls_outgoing",
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"proportion_calls_contacts",
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"proportion_calls_missed_sms_received",
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]
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] # Filter out only the relevant features
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)
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return count_joined
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