Merge branch 'master' into ml_pipeline
junos
commit
de92e1309d
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@ -8,6 +8,43 @@ 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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FEATURES_CALLS = (
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["no_calls_all"]
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+ ["no_" + call_type for call_type in call_types.values()]
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+ ["duration_total_" + call_types.get(1), "duration_total_" + call_types.get(2)]
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+ ["duration_max_" + call_types.get(1), "duration_max_" + call_types.get(2)]
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+ ["no_" + call_types.get(1) + "_ratio", "no_" + call_types.get(2) + "_ratio"]
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+ ["no_contacts_calls"]
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)
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# FEATURES_CALLS =
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# ["no_calls_all",
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# "no_incoming", "no_outgoing", "no_missed",
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# "duration_total_incoming", "duration_total_outgoing",
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# "duration_max_incoming", "duration_max_outgoing",
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# "no_incoming_ratio", "no_outgoing_ratio",
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# "no_contacts"]
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FEATURES_SMS = (
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["no_sms_all"]
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+ ["no_" + sms_type for sms_type in sms_types.values()]
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+ ["no_" + sms_types.get(1) + "_ratio", "no_" + sms_types.get(2) + "_ratio"]
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+ ["no_contacts_sms"]
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)
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# FEATURES_SMS =
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# ["no_sms_all",
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# "no_received", "no_sent",
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# "no_received_ratio", "no_sent_ratio",
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# "no_contacts"]
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FEATURES_CONTACT = [
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"proportion_calls_all",
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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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def get_call_data(usernames: Collection) -> pd.DataFrame:
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"""
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@ -114,10 +151,12 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
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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, and
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* the number of messages by type (received, sent).
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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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data_type = "calls"
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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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@ -125,11 +164,11 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
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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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comm_counts["no_calls_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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no_incoming_ratio=lambda x: x.no_incoming / x.no_calls_all,
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no_outgoing_ratio=lambda x: x.no_outgoing / x.no_calls_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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@ -159,44 +198,56 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
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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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data_type = "sms"
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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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comm_counts["no_sms_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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no_received_ratio=lambda x: x.no_received / x.no_sms_all,
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no_sent_ratio=lambda x: x.no_sent / x.no_sms_all,
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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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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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.rename("no_contacts_" + data_type)
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)
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# Number of communication contacts
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comm_features = comm_features.join(comm_contacts_counts)
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return comm_features
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def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame:
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def contact_features(comm_df: 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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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; return of function enumerate_contacts.
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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 no_contacts and, if df_enumerated
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contains calls data, an additional column total_call_duration.
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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.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-specyfic
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# features we want to calculate are type-specific
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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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@ -207,33 +258,14 @@ def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame:
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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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contacts_count = contacts_count.merge(
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duration_count, 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()[
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"contact_id"
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] # 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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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 df_enumerated
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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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@ -245,14 +277,14 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
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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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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_calls_all:
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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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@ -263,62 +295,24 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
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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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# 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_missed_sms_received",
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]
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] # Filter out only the relevant features
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count_joined = count_calls.join(count_sms).assign(
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proportion_calls_all=(
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lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
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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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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 participant_id a regular column
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.assign(
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proportion_calls_contacts=(
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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", "proportion_calls_contacts"]
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] # Filter out only the relevant features
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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 newly 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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return count_joined
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@ -38,8 +38,10 @@ def identify_screen_sequence(df_screen: pd.DataFrame) -> pd.DataFrame:
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# - OFF -> ON -> unlocked (a true phone unlock)
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# - OFF -> ON -> OFF/locked (no unlocking, i.e. a screen status check)
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# Consider that screen data is sometimes unreliable as shown in expl_screen.ipynb:
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# "I have also seen off -> on -> unlocked (with 2 - locked missing)
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# and off -> locked -> on -> off -> locked (*again*)."
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# "I have also seen
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# off -> on -> unlocked (with 2 - locked missing)
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# and
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# off -> locked -> on -> off -> locked (*again*)."
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# Either clean the data beforehand or deal with these inconsistencies in this function.
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pass
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@ -5,7 +5,7 @@ import pandas as pd
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from numpy.random import default_rng
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from pandas.testing import assert_series_equal
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from features.communication import count_comms, enumerate_contacts, get_call_data
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from features.communication import *
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rng = default_rng()
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@ -76,10 +76,18 @@ class CallsFeatures(unittest.TestCase):
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def test_count_comms_calls(self):
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self.features = count_comms(self.calls)
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print(self.features)
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self.assertIsInstance(self.features, pd.DataFrame)
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self.assertCountEqual(self.features.columns.to_list(), FEATURES_CALLS)
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def test_count_comms_sms(self):
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self.features = count_comms(self.sms)
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print(self.features)
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self.assertIsInstance(self.features, pd.DataFrame)
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self.assertCountEqual(self.features.columns.to_list(), FEATURES_SMS)
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def test_calls_sms_features(self):
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self.features_call_sms = calls_sms_features(self.calls, self.sms)
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self.assertIsInstance(self.features_call_sms, pd.DataFrame)
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self.assertCountEqual(
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self.features_call_sms.columns.to_list(),
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FEATURES_CALLS + FEATURES_SMS + FEATURES_CONTACT,
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)
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Loading…
Reference in New Issue