Compare commits
No commits in common. "777e6f0a583df6503fbf62a03813f9241f4c567f" and "577a874288e84f5b57c4ae79b89712c67b1285ef" have entirely different histories.
777e6f0a58
...
577a874288
|
|
@ -1,63 +0,0 @@
|
|||
id,timestamp,device_id,_id,double_proximity,accuracy,label,dateTime
|
||||
39017,1565802024310,f67354f7-d675-4b76-80c8-123cc4744a5b,2962,0,3,,2019-08-14T17:00:24Z
|
||||
39018,1565802051075,f67354f7-d675-4b76-80c8-123cc4744a5b,2963,0,3,,2019-08-14T17:00:51Z
|
||||
39019,1565802051354,f67354f7-d675-4b76-80c8-123cc4744a5b,2964,8,3,,2019-08-14T17:00:51Z
|
||||
39089,1565010418305,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,51,5,3,,2019-08-05T13:06:58Z
|
||||
39090,1565010772188,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,52,5,3,,2019-08-05T13:12:52Z
|
||||
39091,1565012334450,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,53,5,3,,2019-08-05T13:38:54Z
|
||||
39092,1565013000660,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,54,5,3,,2019-08-05T13:50:00Z
|
||||
39093,1565022742894,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,55,0,3,,2019-08-05T16:32:22Z
|
||||
39094,1565089295906,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,56,5,3,,2019-08-06T11:01:35Z
|
||||
39095,1565096030817,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,57,0,3,,2019-08-06T12:53:50Z
|
||||
39096,1565096367694,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,58,5,3,,2019-08-06T12:59:27Z
|
||||
39097,1565096408570,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,59,5,3,,2019-08-06T13:00:08Z
|
||||
39098,1565116821528,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,60,5,3,,2019-08-06T18:40:21Z
|
||||
39099,1565131345333,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,61,0,3,,2019-08-06T22:42:25Z
|
||||
39100,1565131375072,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,62,5,3,,2019-08-06T22:42:55Z
|
||||
39101,1565131386353,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,63,0,3,,2019-08-06T22:43:06Z
|
||||
39102,1565131389213,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,64,5,3,,2019-08-06T22:43:09Z
|
||||
39103,1565131448891,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,65,0,3,,2019-08-06T22:44:08Z
|
||||
39104,1565131454131,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,66,5,3,,2019-08-06T22:44:14Z
|
||||
39105,1565176143083,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,67,0,3,,2019-08-07T11:09:03Z
|
||||
39106,1565179569310,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,68,5,3,,2019-08-07T12:06:09Z
|
||||
39107,1565180699173,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,69,5,3,,2019-08-07T12:24:59Z
|
||||
39108,1565182538578,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,70,5,3,,2019-08-07T12:55:38Z
|
||||
39109,1565192592776,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,71,0,3,,2019-08-07T15:43:12Z
|
||||
39110,1565216023797,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,72,5,3,,2019-08-07T22:13:43Z
|
||||
39111,1565248358647,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,73,0,3,,2019-08-08T07:12:38Z
|
||||
39112,1565275859157,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,74,5,3,,2019-08-08T14:50:59Z
|
||||
39113,1565304201431,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,75,0,3,,2019-08-08T22:43:21Z
|
||||
39114,1565304229591,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,76,5,3,,2019-08-08T22:43:49Z
|
||||
39115,1565304262050,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,77,0,3,,2019-08-08T22:44:22Z
|
||||
39116,1565613142970,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,78,5,3,,2019-08-12T12:32:22Z
|
||||
39117,1565618266531,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,79,5,3,,2019-08-12T13:57:46Z
|
||||
39118,1565618410488,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,80,5,3,,2019-08-12T14:00:10Z
|
||||
39119,1565618704942,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,81,5,3,,2019-08-12T14:05:04Z
|
||||
39120,1565619005315,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,82,5,3,,2019-08-12T14:10:05Z
|
||||
39121,1565619405904,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,83,5,3,,2019-08-12T14:16:45Z
|
||||
39122,1565619678037,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,84,5,3,,2019-08-12T14:21:18Z
|
||||
39123,1565621206713,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,85,5,3,,2019-08-12T14:46:46Z
|
||||
39124,1565626622125,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,86,5,3,,2019-08-12T16:17:02Z
|
||||
39125,1565684876738,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,87,5,3,,2019-08-13T08:27:56Z
|
||||
39126,1565684956618,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,88,5,3,,2019-08-13T08:29:16Z
|
||||
39127,1565684965647,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,89,5,3,,2019-08-13T08:29:25Z
|
||||
39128,1565685092246,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,90,5,3,,2019-08-13T08:31:32Z
|
||||
39129,1565685136337,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,91,5,3,,2019-08-13T08:32:16Z
|
||||
39130,1565685147453,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,92,5,3,,2019-08-13T08:32:27Z
|
||||
39131,1565685212523,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,93,5,3,,2019-08-13T08:33:32Z
|
||||
39132,1565703397796,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,94,0,3,,2019-08-13T13:36:37Z
|
||||
39133,1565776203019,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,95,5,3,,2019-08-14T09:50:03Z
|
||||
39134,1565776434168,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,96,5,3,,2019-08-14T09:53:54Z
|
||||
39135,1565776435231,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,97,0,3,,2019-08-14T09:53:55Z
|
||||
39136,1565776443368,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,98,5,3,,2019-08-14T09:54:03Z
|
||||
39137,1565779277109,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,99,0,3,,2019-08-14T10:41:17Z
|
||||
39138,1565780016327,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,100,5,3,,2019-08-14T10:53:36Z
|
||||
39139,1565780027437,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,101,5,3,,2019-08-14T10:53:47Z
|
||||
39140,1565783470934,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,102,5,3,,2019-08-14T11:51:10Z
|
||||
39141,1565783801540,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,103,0,3,,2019-08-14T11:56:41Z
|
||||
39142,1565783802120,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,104,5,3,,2019-08-14T11:56:42Z
|
||||
39143,1565783861495,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,105,5,3,,2019-08-14T11:57:41Z
|
||||
39144,1565785318762,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,106,0,3,,2019-08-14T12:21:58Z
|
||||
39145,1565785319346,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,107,5,3,,2019-08-14T12:21:59Z
|
||||
39146,1565960121019,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,108,5,3,,2019-08-16T12:55:21Z
|
||||
39147,1565960226792,fdb06d4a-ee6e-4336-9a96-fc8d2715f243,109,5,3,,2019-08-16T12:57:06Z
|
||||
|
|
|
@ -148,28 +148,3 @@ lin_reg_proximity.score(
|
|||
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
||||
df_full_data_daily_means["PA"],
|
||||
)
|
||||
|
||||
# %% [markdown]
|
||||
# # Merging these into a pipeline
|
||||
|
||||
# %%
|
||||
from machine_learning import pipeline
|
||||
|
||||
# %%
|
||||
ml_pipeline = pipeline.MachineLearningPipeline(
|
||||
labels_questionnaire="PANAS", data_types="proximity"
|
||||
)
|
||||
|
||||
# %%
|
||||
ml_pipeline.get_labels()
|
||||
|
||||
# %% tags=[]
|
||||
ml_pipeline.get_sensor_data()
|
||||
|
||||
# %%
|
||||
ml_pipeline.aggregate_daily()
|
||||
|
||||
# %%
|
||||
ml_pipeline.df_full_data_daily_means
|
||||
|
||||
# %%
|
||||
|
|
|
|||
|
|
@ -8,43 +8,6 @@ from setup import db_engine, session
|
|||
call_types = {1: "incoming", 2: "outgoing", 3: "missed"}
|
||||
sms_types = {1: "received", 2: "sent"}
|
||||
|
||||
FEATURES_CALLS = (
|
||||
["no_calls_all"]
|
||||
+ ["no_" + call_type for call_type in call_types.values()]
|
||||
+ ["duration_total_" + call_types.get(1), "duration_total_" + call_types.get(2)]
|
||||
+ ["duration_max_" + call_types.get(1), "duration_max_" + call_types.get(2)]
|
||||
+ ["no_" + call_types.get(1) + "_ratio", "no_" + call_types.get(2) + "_ratio"]
|
||||
+ ["no_contacts_calls"]
|
||||
)
|
||||
|
||||
# FEATURES_CALLS =
|
||||
# ["no_calls_all",
|
||||
# "no_incoming", "no_outgoing", "no_missed",
|
||||
# "duration_total_incoming", "duration_total_outgoing",
|
||||
# "duration_max_incoming", "duration_max_outgoing",
|
||||
# "no_incoming_ratio", "no_outgoing_ratio",
|
||||
# "no_contacts"]
|
||||
|
||||
FEATURES_SMS = (
|
||||
["no_sms_all"]
|
||||
+ ["no_" + sms_type for sms_type in sms_types.values()]
|
||||
+ ["no_" + sms_types.get(1) + "_ratio", "no_" + sms_types.get(2) + "_ratio"]
|
||||
+ ["no_contacts_sms"]
|
||||
)
|
||||
# FEATURES_SMS =
|
||||
# ["no_sms_all",
|
||||
# "no_received", "no_sent",
|
||||
# "no_received_ratio", "no_sent_ratio",
|
||||
# "no_contacts"]
|
||||
|
||||
FEATURES_CONTACT = [
|
||||
"proportion_calls_all",
|
||||
"proportion_calls_incoming",
|
||||
"proportion_calls_outgoing",
|
||||
"proportion_calls_contacts",
|
||||
"proportion_calls_missed_sms_received",
|
||||
]
|
||||
|
||||
|
||||
def get_call_data(usernames: Collection) -> pd.DataFrame:
|
||||
"""
|
||||
|
|
@ -151,12 +114,10 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
|
|||
These are:
|
||||
* 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 total and maximum duration of calls by type,
|
||||
* the number of messages by type (received, sent), and
|
||||
* the number of communication contacts by type.
|
||||
* the total and maximum duration of calls by type, and
|
||||
* the number of messages by type (received, sent).
|
||||
"""
|
||||
if "call_type" in comm_df:
|
||||
data_type = "calls"
|
||||
comm_counts = (
|
||||
comm_df.value_counts(subset=["participant_id", "call_type"])
|
||||
.unstack()
|
||||
|
|
@ -164,11 +125,11 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
|
|||
.add_prefix("no_")
|
||||
)
|
||||
# Count calls by type.
|
||||
comm_counts["no_calls_all"] = comm_counts.sum(axis=1)
|
||||
comm_counts["no_all"] = comm_counts.sum(axis=1)
|
||||
# Add a total count of calls.
|
||||
comm_counts = comm_counts.assign(
|
||||
no_incoming_ratio=lambda x: x.no_incoming / x.no_calls_all,
|
||||
no_outgoing_ratio=lambda x: x.no_outgoing / x.no_calls_all,
|
||||
no_incoming_ratio=lambda x: x.no_incoming / x.no_all,
|
||||
no_outgoing_ratio=lambda x: x.no_outgoing / x.no_all,
|
||||
)
|
||||
# Ratio of incoming and outgoing calls to all calls.
|
||||
comm_duration_total = (
|
||||
|
|
@ -198,56 +159,44 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
|
|||
# If there were no missed calls, this exception is raised.
|
||||
# But we are dropping the column anyway, so no need to deal with the exception.
|
||||
elif "message_type" in comm_df:
|
||||
data_type = "sms"
|
||||
comm_counts = (
|
||||
comm_df.value_counts(subset=["participant_id", "message_type"])
|
||||
.unstack()
|
||||
.rename(columns=sms_types)
|
||||
.add_prefix("no_")
|
||||
)
|
||||
comm_counts["no_sms_all"] = comm_counts.sum(axis=1)
|
||||
comm_counts["no_all"] = comm_counts.sum(axis=1)
|
||||
# Add a total count of messages.
|
||||
comm_features = comm_counts.assign(
|
||||
no_received_ratio=lambda x: x.no_received / x.no_sms_all,
|
||||
no_sent_ratio=lambda x: x.no_sent / x.no_sms_all,
|
||||
no_received_ratio=lambda x: x.no_received / x.no_all,
|
||||
no_sent_ratio=lambda x: x.no_sent / x.no_all,
|
||||
)
|
||||
# Ratio of incoming and outgoing messages to all messages.
|
||||
else:
|
||||
raise KeyError("The dataframe contains neither call_type or message_type")
|
||||
comm_contacts_counts = (
|
||||
enumerate_contacts(comm_df)
|
||||
.groupby(["participant_id"])
|
||||
.nunique()["contact_id"]
|
||||
.rename("no_contacts_" + data_type)
|
||||
)
|
||||
# Number of communication contacts
|
||||
comm_features = comm_features.join(comm_contacts_counts)
|
||||
return comm_features
|
||||
|
||||
|
||||
def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
|
||||
def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
For each participant and for each of his contacts, this function
|
||||
counts the number of communications (by type) between them. If the
|
||||
argument passed is a dataframe with calls data, it additionally counts
|
||||
the total duration of calls between every pair (participant, contact).
|
||||
Counts the number of people contacted (for each participant) and, if
|
||||
df_enumerated is a dataframe containing calls data, the total duration
|
||||
of calls between a participant and each of her contacts.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
comm_df: pd.DataFrame
|
||||
A dataframe of calls or SMSes.
|
||||
df_enumerated: pd.DataFrame
|
||||
A dataframe of calls or SMSes; return of function enumerate_contacts.
|
||||
|
||||
Returns
|
||||
-------
|
||||
comm_df: pd.DataFrame
|
||||
A new dataframe with a row for each pair (participant, contact).
|
||||
The altered dataframe with the column no_contacts and, if df_enumerated
|
||||
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
|
||||
# features we want to calculate are type-specific
|
||||
# features we want to calculate are type-specyfic
|
||||
if "call_duration" in df_enumerated:
|
||||
# Add a column with the total duration of calls between two people
|
||||
duration_count = (
|
||||
|
|
@ -258,14 +207,33 @@ def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
|
|||
.reset_index() # Make index (which is actually the participant id) a normal column
|
||||
.rename(columns={"call_duration": "total_call_duration"})
|
||||
)
|
||||
contacts_count = contacts_count.merge(
|
||||
# The new dataframe now contains columns containing information about
|
||||
# participants, callers and the total duration of their calls. All that
|
||||
# is now left to do is to merge the original df with the new one.
|
||||
df_enumerated = df_enumerated.merge(
|
||||
duration_count, on=["participant_id", "contact_id"]
|
||||
)
|
||||
contacts_count.rename(columns={0: "no_calls"}, inplace=True)
|
||||
else:
|
||||
contacts_count.rename(columns={0: "no_sms"}, inplace=True)
|
||||
|
||||
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
|
||||
return contacts_count
|
||||
|
||||
return df_enumerated
|
||||
|
||||
|
||||
def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataFrame:
|
||||
|
|
@ -277,14 +245,14 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
|
|||
df_calls: pd.DataFrame
|
||||
A dataframe of calls (return of get_call_data).
|
||||
df_sms: pd.DataFrame
|
||||
A dataframe of SMSes (return of get_sms_data).
|
||||
A dataframe of calls (return of get_sms_data).
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_calls_sms: pd.DataFrame
|
||||
The list of features relating calls and sms data for every participant.
|
||||
These are:
|
||||
* proportion_calls_all:
|
||||
* proportion_calls:
|
||||
proportion of calls in total number of communications
|
||||
* proportion_calls_incoming:
|
||||
proportion of incoming calls in total number of incoming/received communications
|
||||
|
|
@ -295,11 +263,18 @@ 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(
|
||||
proportion_calls_all=(
|
||||
lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
|
||||
|
||||
count_joined = (
|
||||
count_calls.merge(
|
||||
count_sms, on="participant_id", suffixes=("_calls", "_sms")
|
||||
) # Merge calls and sms features
|
||||
.reset_index() # Make participant_id a regular column
|
||||
.assign(
|
||||
proportion_calls=(
|
||||
lambda x: x.no_all_calls / (x.no_all_calls + x.no_all_sms)
|
||||
),
|
||||
proportion_calls_incoming=(
|
||||
lambda x: x.no_incoming / (x.no_incoming + x.no_received)
|
||||
|
|
@ -309,10 +284,41 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
|
|||
),
|
||||
proportion_calls_outgoing=(
|
||||
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
|
||||
)[
|
||||
[
|
||||
"participant_id",
|
||||
"proportion_calls",
|
||||
"proportion_calls_incoming",
|
||||
"proportion_calls_outgoing",
|
||||
"proportion_calls_missed_sms_received",
|
||||
]
|
||||
] # 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
|
||||
|
|
|
|||
|
|
@ -5,8 +5,6 @@ import pandas as pd
|
|||
from config.models import Participant, Proximity
|
||||
from setup import db_engine, session
|
||||
|
||||
FEATURES_PROXIMITY = ["freq_prox_near", "prop_prox_near"]
|
||||
|
||||
|
||||
def get_proximity_data(usernames: Collection) -> pd.DataFrame:
|
||||
"""
|
||||
|
|
@ -58,7 +56,7 @@ def recode_proximity(df_proximity: pd.DataFrame) -> pd.DataFrame:
|
|||
|
||||
|
||||
def count_proximity(
|
||||
df_proximity: pd.DataFrame, group_by: Collection = None
|
||||
df_proximity: pd.DataFrame, group_by: Collection = ["participant_id"]
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
The function counts how many times a "near" value occurs in proximity
|
||||
|
|
@ -77,8 +75,6 @@ def count_proximity(
|
|||
df_proximity_features: pd.DataFrame
|
||||
A dataframe with the count of "near" proximity values and their relative count.
|
||||
"""
|
||||
if group_by is None:
|
||||
group_by = ["participant_id"]
|
||||
if "bool_prox_near" not in df_proximity:
|
||||
df_proximity = recode_proximity(df_proximity)
|
||||
df_proximity["bool_prox_far"] = ~df_proximity["bool_prox_near"]
|
||||
|
|
|
|||
|
|
@ -38,10 +38,8 @@ def identify_screen_sequence(df_screen: pd.DataFrame) -> pd.DataFrame:
|
|||
# - OFF -> ON -> unlocked (a true phone unlock)
|
||||
# - 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:
|
||||
# "I have also seen
|
||||
# off -> on -> unlocked (with 2 - locked missing)
|
||||
# and
|
||||
# off -> locked -> on -> off -> locked (*again*)."
|
||||
# "I have also seen 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.
|
||||
pass
|
||||
|
||||
|
|
|
|||
|
|
@ -1,7 +0,0 @@
|
|||
QUESTIONNAIRE_IDS = {"PANAS": {"PA": 8.0, "NA": 9.0}}
|
||||
|
||||
QUESTIONNAIRE_IDS_RENAME = {}
|
||||
|
||||
for questionnaire in QUESTIONNAIRE_IDS.items():
|
||||
for k, v in questionnaire[1].items():
|
||||
QUESTIONNAIRE_IDS_RENAME[v] = k
|
||||
|
|
@ -1,125 +0,0 @@
|
|||
import datetime
|
||||
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import cross_val_score
|
||||
|
||||
import participants.query_db
|
||||
from features import esm, helper, proximity
|
||||
from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
|
||||
|
||||
|
||||
class MachineLearningPipeline:
|
||||
def __init__(
|
||||
self,
|
||||
labels_questionnaire,
|
||||
labels_scale,
|
||||
data_types,
|
||||
participants_usernames=None,
|
||||
feature_names=None,
|
||||
grouping_variable=None,
|
||||
):
|
||||
if participants_usernames is None:
|
||||
participants_usernames = participants.query_db.get_usernames(
|
||||
collection_start=datetime.date.fromisoformat("2020-08-01")
|
||||
)
|
||||
self.participants_usernames = participants_usernames
|
||||
self.labels_questionnaire = labels_questionnaire
|
||||
self.data_types = data_types
|
||||
|
||||
if feature_names is None:
|
||||
self.feature_names = []
|
||||
self.df_features = pd.DataFrame()
|
||||
self.labels_scale = labels_scale
|
||||
self.df_labels = pd.DataFrame()
|
||||
self.grouping_variable = grouping_variable
|
||||
self.df_groups = pd.DataFrame()
|
||||
|
||||
self.model = None
|
||||
self.validation_method = None
|
||||
|
||||
self.df_esm = pd.DataFrame()
|
||||
self.df_esm_preprocessed = pd.DataFrame()
|
||||
self.df_esm_interest = pd.DataFrame()
|
||||
self.df_esm_clean = pd.DataFrame()
|
||||
|
||||
self.df_proximity = pd.DataFrame()
|
||||
|
||||
self.df_full_data_daily_means = pd.DataFrame()
|
||||
self.df_esm_daily_means = pd.DataFrame()
|
||||
self.df_proximity_daily_counts = pd.DataFrame()
|
||||
|
||||
def get_labels(self):
|
||||
self.df_esm = esm.get_esm_data(self.participants_usernames)
|
||||
self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
|
||||
if self.labels_questionnaire == "PANAS":
|
||||
self.df_esm_interest = self.df_esm_preprocessed[
|
||||
(
|
||||
self.df_esm_preprocessed["questionnaire_id"]
|
||||
== QUESTIONNAIRE_IDS.get("PANAS").get("PA")
|
||||
)
|
||||
| (
|
||||
self.df_esm_preprocessed["questionnaire_id"]
|
||||
== QUESTIONNAIRE_IDS.get("PANAS").get("NA")
|
||||
)
|
||||
]
|
||||
self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
|
||||
|
||||
def get_sensor_data(self):
|
||||
if "proximity" in self.data_types:
|
||||
self.df_proximity = proximity.get_proximity_data(
|
||||
self.participants_usernames
|
||||
)
|
||||
self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
|
||||
self.df_proximity = proximity.recode_proximity(self.df_proximity)
|
||||
|
||||
def aggregate_daily(self):
|
||||
self.df_esm_daily_means = (
|
||||
self.df_esm_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
|
||||
.esm_user_answer_numeric.agg("mean")
|
||||
.reset_index()
|
||||
.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
|
||||
)
|
||||
self.df_esm_daily_means = (
|
||||
self.df_esm_daily_means.pivot(
|
||||
index=["participant_id", "date_lj"],
|
||||
columns="questionnaire_id",
|
||||
values="esm_numeric_mean",
|
||||
)
|
||||
.reset_index(col_level=1)
|
||||
.rename(columns=QUESTIONNAIRE_IDS_RENAME)
|
||||
.set_index(["participant_id", "date_lj"])
|
||||
)
|
||||
self.df_full_data_daily_means = self.df_esm_daily_means.copy()
|
||||
if "proximity" in self.data_types:
|
||||
self.df_proximity_daily_counts = proximity.count_proximity(
|
||||
self.df_proximity, ["participant_id", "date_lj"]
|
||||
)
|
||||
self.df_full_data_daily_means = self.df_full_data_daily_means.join(
|
||||
self.df_proximity_daily_counts
|
||||
)
|
||||
|
||||
def assign_columns(self):
|
||||
self.df_features = self.df_full_data_daily_means[self.feature_names]
|
||||
self.df_labels = self.df_full_data_daily_means[self.labels_scale]
|
||||
if self.grouping_variable:
|
||||
self.df_groups = self.df_full_data_daily_means[self.grouping_variable]
|
||||
else:
|
||||
self.df_groups = None
|
||||
|
||||
def validate_model(self):
|
||||
if self.model is None:
|
||||
raise AttributeError(
|
||||
"Please, specify a machine learning model first, by setting the .model attribute."
|
||||
)
|
||||
if self.validation_method is None:
|
||||
raise AttributeError(
|
||||
"Please, specify a cross validation method first, by setting the .validation_method attribute."
|
||||
)
|
||||
cross_val_score(
|
||||
estimator=self.model,
|
||||
X=self.df_features,
|
||||
y=self.df_labels,
|
||||
groups=self.df_groups,
|
||||
cv=self.validation_method,
|
||||
n_jobs=-1,
|
||||
)
|
||||
|
|
@ -5,7 +5,7 @@ import pandas as pd
|
|||
from numpy.random import default_rng
|
||||
from pandas.testing import assert_series_equal
|
||||
|
||||
from features.communication import *
|
||||
from features.communication import count_comms, enumerate_contacts, get_call_data
|
||||
|
||||
rng = default_rng()
|
||||
|
||||
|
|
@ -76,18 +76,10 @@ class CallsFeatures(unittest.TestCase):
|
|||
|
||||
def test_count_comms_calls(self):
|
||||
self.features = count_comms(self.calls)
|
||||
print(self.features)
|
||||
self.assertIsInstance(self.features, pd.DataFrame)
|
||||
self.assertCountEqual(self.features.columns.to_list(), FEATURES_CALLS)
|
||||
|
||||
def test_count_comms_sms(self):
|
||||
self.features = count_comms(self.sms)
|
||||
print(self.features)
|
||||
self.assertIsInstance(self.features, pd.DataFrame)
|
||||
self.assertCountEqual(self.features.columns.to_list(), FEATURES_SMS)
|
||||
|
||||
def test_calls_sms_features(self):
|
||||
self.features_call_sms = calls_sms_features(self.calls, self.sms)
|
||||
self.assertIsInstance(self.features_call_sms, pd.DataFrame)
|
||||
self.assertCountEqual(
|
||||
self.features_call_sms.columns.to_list(),
|
||||
FEATURES_CALLS + FEATURES_SMS + FEATURES_CONTACT,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,31 +0,0 @@
|
|||
import unittest
|
||||
|
||||
from features.proximity import *
|
||||
|
||||
|
||||
class ProximityFeatures(unittest.TestCase):
|
||||
df_proximity = pd.DataFrame()
|
||||
df_proximity_recoded = pd.DataFrame()
|
||||
df_proximity_features = pd.DataFrame()
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls) -> None:
|
||||
cls.df_proximity = pd.read_csv("../data/example_proximity.csv")
|
||||
cls.df_proximity["participant_id"] = 99
|
||||
|
||||
def test_recode_proximity(self):
|
||||
self.df_proximity_recoded = recode_proximity(self.df_proximity)
|
||||
self.assertIn("bool_prox_near", self.df_proximity_recoded)
|
||||
# Is the recoded column present?
|
||||
self.assertIn(True, self.df_proximity_recoded.bool_prox_near)
|
||||
# Are there "near" values in the data?
|
||||
self.assertIn(False, self.df_proximity_recoded.bool_prox_near)
|
||||
# Are there "far" values in the data?
|
||||
|
||||
def test_count_proximity(self):
|
||||
self.df_proximity_recoded = recode_proximity(self.df_proximity)
|
||||
self.df_proximity_features = count_proximity(self.df_proximity_recoded)
|
||||
print(self.df_proximity_features.columns)
|
||||
self.assertCountEqual(
|
||||
self.df_proximity_features.columns.to_list(), FEATURES_PROXIMITY
|
||||
)
|
||||
Loading…
Reference in New Issue