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Add an option to group on other than just participant_id.

communication
junos 10 months ago
parent
commit
9a319ac6e5
  1. 23
      exploration/expl_communication.py
  2. 34
      features/communication.py

23
exploration/expl_communication.py

@ -13,14 +13,15 @@
# name: straw2analysis
# ---
# %%
import importlib
# %%
# %matplotlib inline
import os
import sys
import matplotlib.pyplot as plt
# %%
import seaborn as sns
nb_dir = os.path.split(os.getcwd())[0]
@ -28,21 +29,29 @@ if nb_dir not in sys.path:
sys.path.append(nb_dir)
# %%
from features.communication import *
from features import communication, helper
# %%
importlib.reload(communication)
# %% [markdown]
# # Example of communication data and feature calculation
# %%
df_calls = get_call_data(["nokia_0000003"])
df_calls = communication.get_call_data(["nokia_0000003"])
print(df_calls)
# %%
count_comms(df_calls)
df_calls = helper.get_date_from_timestamp(df_calls)
communication.count_comms(df_calls, ["date_lj"])
# %%
df_sms = communication.get_sms_data(["nokia_0000003"])
df_sms = helper.get_date_from_timestamp(df_sms)
communication.count_comms(df_sms, ["date_lj"])
# %%
df_sms = get_sms_data(["nokia_0000003"])
count_comms(df_sms)
communication.calls_sms_features(df_calls, df_sms, ["date_lj"])
# %% [markdown]
# # Call data

34
features/communication.py

@ -135,7 +135,7 @@ def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame:
return comm_df
def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
def count_comms(comm_df: pd.DataFrame, group_by=None) -> pd.DataFrame:
"""
Calculate frequencies (and duration) of messages (or calls), grouped by their types.
@ -143,6 +143,9 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
----------
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
-------
@ -155,10 +158,12 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
* 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=["participant_id", "call_type"])
comm_df.value_counts(subset=group_by + ["participant_id", "call_type"])
.unstack()
.rename(columns=call_types)
.add_prefix("no_")
@ -172,7 +177,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
)
# Ratio of incoming and outgoing calls to all calls.
comm_duration_total = (
comm_df.groupby(["participant_id", "call_type"])
comm_df.groupby(group_by + ["participant_id", "call_type"])
.sum()["call_duration"]
.unstack()
.rename(columns=call_types)
@ -180,7 +185,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
)
# Total call duration by type.
comm_duration_max = (
comm_df.groupby(["participant_id", "call_type"])
comm_df.groupby(group_by + ["participant_id", "call_type"])
.max()["call_duration"]
.unstack()
.rename(columns=call_types)
@ -200,7 +205,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
elif "message_type" in comm_df:
data_type = "sms"
comm_counts = (
comm_df.value_counts(subset=["participant_id", "message_type"])
comm_df.value_counts(subset=group_by + ["participant_id", "message_type"])
.unstack()
.rename(columns=sms_types)
.add_prefix("no_")
@ -216,7 +221,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
raise KeyError("The dataframe contains neither call_type or message_type")
comm_contacts_counts = (
enumerate_contacts(comm_df)
.groupby(["participant_id"])
.groupby(group_by + ["participant_id"])
.nunique()["contact_id"]
.rename("no_contacts_" + data_type)
)
@ -268,7 +273,9 @@ def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
return contacts_count
def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataFrame:
def calls_sms_features(
df_calls: pd.DataFrame, df_sms: pd.DataFrame, group_by=None
) -> pd.DataFrame:
"""
Calculates additional features relating calls and sms data.
@ -278,6 +285,9 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
A dataframe of calls (return of get_call_data).
df_sms: pd.DataFrame
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.
Returns
-------
@ -295,9 +305,13 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
* proportion_calls_contacts:
proportion of calls contacts in total number of communication contacts
"""
count_calls = count_comms(df_calls)
count_sms = count_comms(df_sms)
count_joined = count_calls.join(count_sms).assign(
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"
).assign(
proportion_calls_all=(
lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
),

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