Communication
Ivan Kobe
Junos Lukan
parent
40293c4752
commit
62af04fe09
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@ -6,7 +6,7 @@
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.11.2
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# jupytext_version: 1.11.4
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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@ -14,6 +14,7 @@
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# ---
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# %%
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# %matplotlib inline
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import os
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import sys
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@ -53,6 +54,15 @@ import participants.query_db
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participants_inactive_usernames = participants.query_db.get_usernames()
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df_calls_inactive = get_call_data(participants_inactive_usernames)
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# %%
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participants_inactive_usernames
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# %%
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df_calls_inactive.head()
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# %%
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enumerate_contacts(df_calls_inactive).head()
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# %%
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df_calls_features = count_comms(df_calls_inactive)
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df_calls_features.head()
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@ -70,6 +80,9 @@ calls_number = pd.wide_to_long(
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suffix="\D+",
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)
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# %%
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calls_number
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# %%
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sns.displot(calls_number, x="no", hue="call_type", binwidth=5, element="step", height=8)
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@ -126,3 +139,30 @@ sms_number = pd.wide_to_long(
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sns.displot(
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sms_number, x="no", hue="message_type", binwidth=5, element="step", height=8
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)
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# %% [markdown]
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# # Communication features
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# %%
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df_calls_enumerated = enumerate_contacts(df_calls)
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display(df_calls_enumerated)
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# %%
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df_calls_contact_features = contact_features(df_calls_enumerated)
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display(df_calls_contact_features)
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# %%
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df_sms_enumerated = enumerate_contacts(df_sms)
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df_sms_contact_features = contact_features(df_sms_enumerated)
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display(df_sms_contact_features)
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# %%
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display(count_comms(df_calls))
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# %%
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display(count_comms(df_sms))
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# %%
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display(calls_sms_features(df_calls, df_sms))
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# %%
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@ -86,7 +86,8 @@ def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame:
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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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contact_counts.groupby("participant_id") # Group again for enumeration.
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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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@ -150,8 +151,10 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
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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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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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comm_features.drop(columns="duration_total_" +
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call_types[3], inplace=True)
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comm_features.drop(columns="duration_max_" +
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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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@ -172,19 +175,145 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
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)
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# Ratio of incoming and outgoing messages to all messages.
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else:
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raise KeyError("The dataframe contains neither call_type or message_type")
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raise KeyError(
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"The dataframe contains neither call_type or message_type")
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return comm_features
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def contact_features():
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# TODO Implement a method that takes a DF with enumerated contacts as argument and calculates:
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# * Duration of calls per caller (for most common callers)
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# * Determine work vs non-work contacts by work hours heuristics
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# * Number of people contacted
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# And similarly for SMS.
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pass
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def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame:
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"""
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Counts the number of people contacted (for each participant) and, if
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df_enumerated is a dataframe containing calls data, the total duration
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of calls between a participant and each of her contacts.
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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; return of function enumerate_contacts.
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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 no_contacts and, if df_enumerated
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contains calls data, an additional column total_call_duration.
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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.groupby(
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["participant_id", "contact_id"]
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)
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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
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.rename(columns={"call_duration": "total_call_duration"})
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)
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# The new dataframe now contains columns containing information about
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# participants, callers and the total duration of their calls. All that
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# is now left to do is to merge the original df with the new one.
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df_enumerated = df_enumerated.merge(
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duration_count,
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on=["participant_id", "contact_id"]
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)
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contact_count = (
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df_enumerated.groupby(["participant_id"])
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.nunique()["contact_id"] # For each participant, count the number of distinct contacts
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.reset_index() # Make index (which is actually the participant id) a normal column
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.rename(columns={"contact_id": "no_contacts"})
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)
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df_enumerated = (
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# Merge df with the newely created df containing info about number of contacts
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df_enumerated.merge(contact_count, on="participant_id")
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# Sort first by participant_id and then by contact_id and
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# thereby restore the inital ordering of input dataframes.
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.sort_values(["participant_id", "contact_id"])
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)
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# TODO:Determine work vs non-work contacts by work hours heuristics
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return df_enumerated
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def calls_sms_features():
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# TODO Relate the calls and sms data, such as comparing the number of (missed) calls and messages.
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pass
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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 calls (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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* no_calls_no_sms_ratio:
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proportion of calls in total number of communications
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* no_incoming_calls_no_recieved_sms_ratio:
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proportion of incoming calls in total number of incoming/recieved communications
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* no_outgoing_calls_no_sent_sms_ratio:
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proportion of outgoing calls in total number of outgoing/sent communications
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* no_calls_contacts_no_sms_contacts_ratio:
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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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no_calls_no_sms_ratio=(
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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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no_incoming_calls_no_recieved_sms_ratio=(
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lambda x: x.no_received / (x.no_incoming + x.no_received)
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),
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no_outgoing_calls_no_sent_sms_ratio=(
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lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
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) # Calculate new features and create additional columns
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)[
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["participant_id",
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"no_calls_no_sms_ratio",
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"no_incoming_calls_no_recieved_sms_ratio",
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"no_outgoing_calls_no_sent_sms_ratio"]
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] # Filter out only the relevant feautres
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)
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features_calls = contact_features(enumerate_contacts(df_calls))
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features_sms = contact_features(enumerate_contacts(df_sms))
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features_joined = (
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features_calls.merge(
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features_sms, on="participant_id", suffixes=("_calls", "_sms")
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) # Merge calls and sms features
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.reset_index() # Make participand_id a regular column
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.assign(
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no_calls_contacts_no_sms_contacts_ratio=(
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lambda x: x.no_contacts_calls /
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(x.no_contacts_calls + x.no_contacts_sms)
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) # Calculate new features and create additional columns
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)[
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["participant_id",
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"no_calls_contacts_no_sms_contacts_ratio"]
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] # Filter out only the relevant feautres
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# Since we are interested only in some features and ignored
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# others, a lot of duplicate rows were created. Remove them.
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.drop_duplicates()
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)
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# Join the newely created dataframes
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df_calls_sms = count_joined.merge(features_joined, on="participant_id")
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return df_calls_sms
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