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[WIP] Methods to get the labels and data plus aggregate them.

communication
junos 1 year ago
parent
commit
b06ec6e1ae
  1. 15
      exploration/ex_ml_pipeline.py
  2. 7
      machine_learning/__init__.py
  3. 69
      machine_learning/pipeline.py

15
exploration/ex_ml_pipeline.py

@ -156,9 +156,20 @@ lin_reg_proximity.score(
from machine_learning import pipeline
# %%
ml_pipeline = pipeline.MachineLearningPipeline()
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.participants_usernames
ml_pipeline.df_full_data_daily_means
# %%

7
machine_learning/__init__.py

@ -0,0 +1,7 @@
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

69
machine_learning/pipeline.py

@ -1,12 +1,79 @@
import datetime
import pandas as pd
import participants.query_db
from features import esm, helper, proximity
from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
class MachineLearningPipeline:
def __init__(self, participants_usernames=None):
def __init__(self, labels_questionnaire, data_types, participants_usernames=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
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
)
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