263 lines
6.1 KiB
Python
263 lines
6.1 KiB
Python
# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.12.0
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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# %%
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# %matplotlib inline
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import datetime
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import importlib
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import os
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import sys
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import yaml
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from sklearn import linear_model
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import machine_learning.features_sensor
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import machine_learning.labels
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import machine_learning.model
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# %%
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import participants.query_db
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from features import esm, helper, proximity
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# %% [markdown] tags=[]
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# # 1. Get the relevant data
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# %%
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participants_inactive_usernames = participants.query_db.get_usernames(
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collection_start=datetime.date.fromisoformat("2020-08-01")
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)
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# Consider only two participants to simplify.
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ptcp_2 = participants_inactive_usernames[0:2]
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# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[]
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# ## 1.1 Labels
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# %%
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df_esm = esm.get_esm_data(ptcp_2)
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df_esm_preprocessed = esm.preprocess_esm(df_esm)
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# %%
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df_esm_PANAS = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] == 8)
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| (df_esm_preprocessed["questionnaire_id"] == 9)
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]
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df_esm_PANAS_clean = esm.clean_up_esm(df_esm_PANAS)
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# %% [markdown]
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# ## 1.2 Sensor data
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# %%
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df_proximity = proximity.get_proximity_data(ptcp_2)
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df_proximity = helper.get_date_from_timestamp(df_proximity)
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df_proximity = proximity.recode_proximity(df_proximity)
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# %% [markdown]
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# ## 1.3 Standardization/personalization
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# %% [markdown]
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# # 2. Grouping/segmentation
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# %%
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df_esm_PANAS_daily_means = (
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df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
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.esm_user_answer_numeric.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
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)
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# %%
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df_esm_PANAS_daily_means = (
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df_esm_PANAS_daily_means.pivot(
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index=["participant_id", "date_lj"],
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columns="questionnaire_id",
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values="esm_numeric_mean",
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)
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.reset_index(col_level=1)
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.rename(columns={8.0: "PA", 9.0: "NA"})
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.set_index(["participant_id", "date_lj"])
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)
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# %%
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df_proximity_daily_counts = proximity.count_proximity(
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df_proximity, ["date_lj"]
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)
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# %%
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df_proximity_daily_counts
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# %% [markdown]
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# # 3. Join features (and export to csv?)
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# %%
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df_full_data_daily_means = df_esm_PANAS_daily_means.join(
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df_proximity_daily_counts
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).reset_index()
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# %% [markdown]
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# # 4. Machine learning model and parameters
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# %%
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lin_reg_proximity = linear_model.LinearRegression()
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# %% [markdown]
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# ## 4.1 Validation method
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# %%
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
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df_full_data_daily_means["PA"],
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groups=df_full_data_daily_means["participant_id"],
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)
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# %% [markdown]
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# ## 4.2 Fit results (export?)
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# %%
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cross_val_score(
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lin_reg_proximity,
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df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
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df_full_data_daily_means["PA"],
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groups=df_full_data_daily_means["participant_id"],
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cv=logo,
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n_jobs=-1,
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scoring="r2",
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)
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# %%
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lin_reg_proximity.fit(
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df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
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df_full_data_daily_means["PA"],
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)
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# %%
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lin_reg_proximity.score(
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df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
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df_full_data_daily_means["PA"],
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)
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# %% [markdown]
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# # Merging these into a pipeline
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# %%
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from machine_learning import features_sensor, labels, model, pipeline
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# %%
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importlib.reload(features_sensor)
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# %%
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with open("../machine_learning/config/minimal_features.yaml", "r") as file:
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sensor_features_params = yaml.safe_load(file)
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print(sensor_features_params)
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# %%
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sensor_features = machine_learning.features_sensor.SensorFeatures(
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**sensor_features_params
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)
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sensor_features.data_types
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# %%
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sensor_features.set_participants_label("nokia_0000003")
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# %%
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sensor_features.data_types = ["proximity", "communication"]
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sensor_features.participants_usernames = ptcp_2
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# %%
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sensor_features.get_sensor_data("proximity")
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# %%
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sensor_features.set_sensor_data()
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# %%
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sensor_features.get_sensor_data("proximity")
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# %%
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sensor_features.calculate_features(cached=False)
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features_all_calculated = sensor_features.get_features("all", "all")
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# %%
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sensor_features.calculate_features(cached=True)
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features_all_read = sensor_features.get_features("all", "all")
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# %%
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features_all_read = features_all_read.reset_index()
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features_all_read["date_lj"] = features_all_read["date_lj"].dt.date
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features_all_read.set_index(["participant_id", "date_lj"], inplace=True)
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# date_lj column is parsed as a date and represented as Timestamp, when read from csv.
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# When calculated, it is represented as date.
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# %%
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np.isclose(features_all_read, features_all_calculated).all()
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# %%
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with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
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labels_params = yaml.safe_load(file)
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# %%
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labels = machine_learning.labels.Labels(**labels_params)
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labels.participants_usernames = ptcp_2
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labels.set_participants_label("nokia_0000003")
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labels.questionnaires
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# %%
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labels.set_labels()
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# %%
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labels.get_labels("PANAS")
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# %%
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labels.aggregate_labels(cached=False)
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labels_calculated = labels.get_aggregated_labels()
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# %%
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labels.aggregate_labels(cached=True)
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labels_read = labels.get_aggregated_labels()
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labels_read = labels_read.reset_index()
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labels_read["date_lj"] = labels_read["date_lj"].dt.date
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labels_read.set_index(["participant_id", "date_lj"], inplace=True)
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# date_lj column is parsed as a date and represented as Timestamp, when read from csv.
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# When calculated, it is represented as date.
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# %%
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np.isclose(labels_read, labels_calculated).all()
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# %%
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model_validation = machine_learning.model.ModelValidation(
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sensor_features.get_features("all", "all"),
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labels.get_aggregated_labels(),
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group_variable="participant_id",
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cv_name="loso",
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)
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model_validation.model = linear_model.LinearRegression()
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model_validation.set_cv_method()
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# %%
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model_validation.cross_validate()
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# %%
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model_validation.groups
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# %%
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