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stress_at_work_analysis
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separated features

communication
Ivan Kobe 2021-08-10 12:34:21 +02:00
parent 06e1fe7410
commit 74b4f9ddbe
2 changed files with 58 additions and 68 deletions
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120
features/communication.py
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@ -114,8 +114,9 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
These are: These are:
* the number of calls by type (incoming, outgoing missed) and in total, * 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, * the ratio of incoming and outgoing calls to the total number of calls,
* the total and maximum duration of calls by type, and * the total and maximum duration of calls by type,
* the number of messages by type (received, sent). * the number of messages by type (received, sent), and
* the number of communication contacts by type.
""" """
if "call_type" in comm_df: if "call_type" in comm_df:
comm_counts = ( comm_counts = (
@ -148,8 +149,20 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
.add_prefix("duration_max_") .add_prefix("duration_max_")
) )
# Max call duration by type # Max call duration by type
comm_contacts_counts = (
enumerate_contacts(comm_df)
.groupby(["participant_id"])
.nunique()["contact_id"]
.reset_index()
.rename(columns={"contact_id": "no_contacts"})
)
# Number of communication contacts
comm_features = comm_counts.join(comm_duration_total) comm_features = comm_counts.join(comm_duration_total)
comm_features = comm_features.join(comm_duration_max) comm_features = comm_features.join(comm_duration_max)
comm_features = comm_features.merge(
comm_contacts_counts,
on="participant_id"
).set_index("participant_id")
try: try:
comm_features.drop(columns="duration_total_" + call_types[3], inplace=True) comm_features.drop(columns="duration_total_" + call_types[3], inplace=True)
comm_features.drop(columns="duration_max_" + call_types[3], inplace=True) comm_features.drop(columns="duration_max_" + call_types[3], inplace=True)
@ -172,68 +185,66 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
no_sent_ratio=lambda x: x.no_sent / x.no_all, no_sent_ratio=lambda x: x.no_sent / x.no_all,
) )
# Ratio of incoming and outgoing messages to all messages. # Ratio of incoming and outgoing messages to all messages.
comm_contacts_counts = (
enumerate_contacts(comm_df)
.groupby(["participant_id"])
.nunique()["contact_id"]
.reset_index()
.rename(columns={"contact_id": "no_contacts"})
)
# Number of communication contacts
comm_features = comm_features.merge(
comm_contacts_counts,
on="participant_id"
).set_index("participant_id")
else: else:
raise KeyError("The dataframe contains neither call_type or message_type") raise KeyError("The dataframe contains neither call_type or message_type")
return comm_features return comm_features
def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame: def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
""" """
Counts the number of people contacted (for each participant) and, if For each participant and for each of his contacts, this function
df_enumerated is a dataframe containing calls data, the total duration counts the number of communications (by type) between them. If the
of calls between a participant and each of her contacts. argument passed is a dataframe with calls data, it additionally counts
the total duration of calls between every pair (participant, contact).
Parameters Parameters
---------- ----------
df_enumerated: pd.DataFrame df_enumerated: pd.DataFrame
A dataframe of calls or SMSes; return of function enumerate_contacts. A dataframe of calls or SMSes.
Returns Returns
------- -------
comm_df: pd.DataFrame comm_df: pd.DataFrame
The altered dataframe with the column no_contacts and, if df_enumerated A new dataframe with a row for each pair (participant, contact).
contains calls data, an additional column total_call_duration.
""" """
df_enumerated = enumerate_contacts(comm_df)
contacts_count = (
df_enumerated
.groupby(["participant_id","contact_id"])
.size()
.reset_index()
)
# Check whether df contains calls or SMS data since some # Check whether df contains calls or SMS data since some
# features we want to calculate are type-specyfic # features we want to calculate are type-specyfic
if "call_duration" in df_enumerated: if "call_duration" in df_enumerated:
# Add a column with the total duration of calls between two people # Add a column with the total duration of calls between two people
duration_count = ( duration_count = (
df_enumerated.groupby(["participant_id", "contact_id"]) df_enumerated
.groupby(["participant_id", "contact_id"])
# For each participant and for each caller, sum durations of their calls # For each participant and for each caller, sum durations of their calls
["call_duration"] ["call_duration"]
.sum() .sum()
.reset_index() # Make index (which is actually the participant id) a normal column .reset_index() # Make index (which is actually the participant id) a normal column
.rename(columns={"call_duration": "total_call_duration"}) .rename(columns={"call_duration": "total_call_duration"})
) )
# The new dataframe now contains columns containing information about contacts_count = contacts_count.merge(duration_count, on=["participant_id", "contact_id"])
# participants, callers and the total duration of their calls. All that contacts_count.rename(columns={0:"no_calls"}, inplace=True)
# is now left to do is to merge the original df with the new one. else:
df_enumerated = df_enumerated.merge( contacts_count.rename(columns={0:"no_sms"}, inplace=True)
duration_count, on=["participant_id", "contact_id"]
)
contact_count = (
df_enumerated.groupby(["participant_id"])
.nunique()[
"contact_id"
] # For each participant, count the number of distinct contacts
.reset_index() # Make index (which is actually the participant id) a normal column
.rename(columns={"contact_id": "no_contacts"})
)
df_enumerated = (
# Merge df with the newely created df containing info about number of contacts
df_enumerated.merge(contact_count, on="participant_id")
# Sort first by participant_id and then by contact_id and
# thereby restore the inital ordering of input dataframes.
.sort_values(["participant_id", "contact_id"])
)
# TODO:Determine work vs non-work contacts by work hours heuristics # TODO:Determine work vs non-work contacts by work hours heuristics
return contacts_count
return df_enumerated
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) -> pd.DataFrame:
@ -245,7 +256,7 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
df_calls: pd.DataFrame df_calls: pd.DataFrame
A dataframe of calls (return of get_call_data). A dataframe of calls (return of get_call_data).
df_sms: pd.DataFrame df_sms: pd.DataFrame
A dataframe of calls (return of get_sms_data). A dataframe of SMSes (return of get_sms_data).
Returns Returns
------- -------
@ -263,10 +274,8 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
* proportion_calls_contacts: * proportion_calls_contacts:
proportion of calls contacts in total number of communication contacts proportion of calls contacts in total number of communication contacts
""" """
count_calls = count_comms(df_calls) count_calls = count_comms(df_calls)
count_sms = count_comms(df_sms) count_sms = count_comms(df_sms)
count_joined = ( count_joined = (
count_calls.merge( count_calls.merge(
count_sms, on="participant_id", suffixes=("_calls", "_sms") count_sms, on="participant_id", suffixes=("_calls", "_sms")
@ -284,6 +293,9 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
), ),
proportion_calls_outgoing=( proportion_calls_outgoing=(
lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent) 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 # Calculate new features and create additional columns
)[ )[
@ -292,33 +304,9 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
"proportion_calls", "proportion_calls",
"proportion_calls_incoming", "proportion_calls_incoming",
"proportion_calls_outgoing", "proportion_calls_outgoing",
"proportion_calls_contacts",
"proportion_calls_missed_sms_received", "proportion_calls_missed_sms_received",
] ]
] # Filter out only the relevant features ] # Filter out only the relevant features
) )
return count_joined
features_calls = contact_features(enumerate_contacts(df_calls))
features_sms = contact_features(enumerate_contacts(df_sms))
features_joined = (
features_calls.merge(
features_sms, on="participant_id", suffixes=("_calls", "_sms")
) # Merge calls and sms features
.reset_index() # Make participant_id a regular column
.assign(
proportion_calls_contacts=(
lambda x: x.no_contacts_calls
/ (x.no_contacts_calls + x.no_contacts_sms)
) # Calculate new features and create additional columns
)[
["participant_id", "proportion_calls_contacts"]
] # Filter out only the relevant features
# Since we are interested only in some features and ignored
# others, a lot of duplicate rows were created. Remove them.
.drop_duplicates()
)
# Join the newly created dataframes
df_calls_sms = count_joined.merge(features_joined, on="participant_id")
return df_calls_sms

6
features/screen.py
View File

@ -38,8 +38,10 @@ def identify_screen_sequence(df_screen: pd.DataFrame) -> pd.DataFrame:
# - OFF -> ON -> unlocked (a true phone unlock) # - OFF -> ON -> unlocked (a true phone unlock)
# - OFF -> ON -> OFF/locked (no unlocking, i.e. a screen status check) # - OFF -> ON -> OFF/locked (no unlocking, i.e. a screen status check)
# Consider that screen data is sometimes unreliable as shown in expl_screen.ipynb: # Consider that screen data is sometimes unreliable as shown in expl_screen.ipynb:
# "I have also seen off -> on -> unlocked (with 2 - locked missing) # "I have also seen
# and off -> locked -> on -> off -> locked (*again*)." # off -> on -> unlocked (with 2 - locked missing)
# and
# off -> locked -> on -> off -> locked (*again*)."
# Either clean the data beforehand or deal with these inconsistencies in this function. # Either clean the data beforehand or deal with these inconsistencies in this function.
pass pass