1308 lines
33 KiB
Python
1308 lines
33 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: 1.13.0
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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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# %% jupyter={"source_hidden": true}
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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 matplotlib.pyplot as plt
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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 pyprojroot import here
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from sklearn import linear_model, svm, kernel_ridge, gaussian_process
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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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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# %% jupyter={"source_hidden": true}
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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(df_proximity, ["date_lj"])
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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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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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from machine_learning import features_sensor, labels, model, pipeline
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# %% jupyter={"source_hidden": true}
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importlib.reload(features_sensor)
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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sensor_features.set_participants_label("nokia_0000003")
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# %% jupyter={"source_hidden": true}
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sensor_features.data_types = ["proximity", "communication"]
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sensor_features.participants_usernames = ptcp_2
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# %% jupyter={"source_hidden": true}
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sensor_features.get_sensor_data("proximity")
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# %% jupyter={"source_hidden": true}
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sensor_features.set_sensor_data()
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# %% jupyter={"source_hidden": true}
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sensor_features.get_sensor_data("proximity")
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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np.isclose(features_all_read, features_all_calculated).all()
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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labels.set_labels()
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# %% jupyter={"source_hidden": true}
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labels.get_labels("PANAS")
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# %% jupyter={"source_hidden": true}
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labels.aggregate_labels(cached=False)
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labels_calculated = labels.get_aggregated_labels()
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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np.isclose(labels_read, labels_calculated).all()
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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model_validation.cross_validate()
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# %% jupyter={"source_hidden": true}
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model_validation.groups
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# %% [markdown]
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# # Use RAPIDS
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# %% jupyter={"source_hidden": true}
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with open(here("rapids/config.yaml"), "r") as file:
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rapids_config = yaml.safe_load(file)
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# %% jupyter={"source_hidden": true}
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for key in rapids_config.keys():
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if isinstance(rapids_config[key], dict): # Remove top-level configs
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if ("PROVIDERS" in rapids_config[key]): # Retain features (that have providers)
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if rapids_config[key]["PROVIDERS"]: # Remove non-implemented features
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for provider in rapids_config[key]["PROVIDERS"]:
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if rapids_config[key]["PROVIDERS"][provider]["COMPUTE"]: # Check that the features were actually calculated
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if "FEATURES" in rapids_config[key]["PROVIDERS"][provider]:
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print(key)
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print(provider)
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print(rapids_config[key]["PROVIDERS"][provider]["FEATURES"])
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# %% jupyter={"source_hidden": true}
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features_rapids = pd.read_csv(here("rapids/data/processed/features/all_participants/all_sensor_features.csv"), parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"])
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# %% jupyter={"source_hidden": true}
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features_rapids.columns
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# %% jupyter={"source_hidden": true}
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features_rapids = features_rapids.assign(date_lj=lambda x: x.local_segment_start_datetime.dt.date)
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# %% jupyter={"source_hidden": true}
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features_rapids["participant_id"] = features_rapids["pid"].str.extract("(\d+)")
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features_rapids["participant_id"] = pd.to_numeric(features_rapids["participant_id"])
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features_rapids.set_index(["participant_id", "date_lj"], inplace=True)
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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labels = machine_learning.labels.Labels(**labels_params)
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labels.set_participants_label("all")
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# %% jupyter={"source_hidden": true}
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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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# %% jupyter={"source_hidden": true}
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features_rapids.shape
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# %% jupyter={"source_hidden": true}
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labels_read.shape
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# %% jupyter={"source_hidden": true}
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features_labels = features_rapids.join(labels_read, how="inner").reset_index()
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# %% jupyter={"source_hidden": true}
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features_labels.shape
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# %% jupyter={"source_hidden": true}
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features_labels.columns
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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# %% jupyter={"source_hidden": true}
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feature_columns = features_labels.columns[6:-3]
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label_column = "NA"
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group_column = "pid"
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# %% jupyter={"source_hidden": true}
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lin_reg_rapids = linear_model.LinearRegression()
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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features_labels[feature_columns],
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features_labels[label_column],
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groups=features_labels[group_column],
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)
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# %% jupyter={"source_hidden": true}
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cross_val_score(
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lin_reg_rapids,
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X=imputer.fit_transform(features_labels[feature_columns]),
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y=features_labels[label_column],
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groups=features_labels[group_column],
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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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# %% tags=[]
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(features_labels[feature_columns].isna(), cbar=False)
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# %% [markdown] tags=[]
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# ```yaml
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# ALL_CLEANING_INDIVIDUAL:
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# PROVIDERS:
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# RAPIDS:
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# COMPUTE: True
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# IMPUTE_SELECTED_EVENT_FEATURES: # Fill NAs with 0 only for event-based features, see table below
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# COMPUTE: True
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# MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 # Any feature value in a time segment instance with phone data yield > [MIN_DATA_YIELDED_MINUTES_TO_IMPUTE] will be replaced with a zero.
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# COLS_NAN_THRESHOLD: 0.3 # Discard columns with missing value ratios higher than [COLS_NAN_THRESHOLD]. Set to 1 to disable
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# COLS_VAR_THRESHOLD: True # Set to True to discard columns with zero variance
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# ROWS_NAN_THRESHOLD: 1 # Discard rows with missing value ratios higher than [ROWS_NAN_THRESHOLD]. Set to 1 to disable
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# DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
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# DATA_YIELD_RATIO_THRESHOLD: 0.3 # Discard rows with ratiovalidyieldedhours or ratiovalidyieldedminutes feature less than [DATA_YIELD_RATIO_THRESHOLD]. The feature name is determined by [DATA_YIELD_FEATURE] parameter. Set to 0 to disable
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# DROP_HIGHLY_CORRELATED_FEATURES:
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# COMPUTE: False
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# MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
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# CORR_THRESHOLD: 0.95
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# SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
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# ```
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# %% jupyter={"source_hidden": true}
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features_rapids_cleaned = pd.read_csv(here("rapids/data/processed/features/all_participants/all_sensor_features_cleaned_rapids.csv"), parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"])
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features_rapids_cleaned = features_rapids_cleaned.assign(date_lj=lambda x: x.local_segment_start_datetime.dt.date)
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features_rapids_cleaned["participant_id"] = features_rapids_cleaned["pid"].str.extract("(\d+)")
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features_rapids_cleaned["participant_id"] = pd.to_numeric(features_rapids_cleaned["participant_id"])
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features_rapids_cleaned.set_index(["participant_id", "date_lj"], inplace=True)
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# %% jupyter={"source_hidden": true}
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features_cleaned_labels = features_rapids_cleaned.join(labels_read, how="inner").reset_index()
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feature_clean_columns = features_cleaned_labels.columns[6:-3]
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# %% jupyter={"source_hidden": true}
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print(feature_columns.shape)
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print(feature_clean_columns.shape)
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# %% tags=[]
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(features_cleaned_labels[feature_clean_columns].isna(), cbar=False)
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# %% jupyter={"source_hidden": true}
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lin_reg_rapids_clean = linear_model.LinearRegression()
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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features_cleaned_labels[feature_clean_columns],
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features_cleaned_labels[label_column],
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groups=features_cleaned_labels[group_column],
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)
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# %% jupyter={"source_hidden": true}
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features_clean_imputed = imputer.fit_transform(features_cleaned_labels[feature_clean_columns])
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# %% jupyter={"source_hidden": true}
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cross_val_score(
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lin_reg_rapids_clean,
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X=features_clean_imputed,
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y=features_cleaned_labels[label_column],
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groups=features_cleaned_labels[group_column],
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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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# %% jupyter={"source_hidden": true}
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lin_reg_full = linear_model.LinearRegression()
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lin_reg_full.fit(features_clean_imputed,features_cleaned_labels[label_column])
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# %% jupyter={"source_hidden": true}
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NA_pred = lin_reg_full.predict(features_clean_imputed)
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# %% jupyter={"source_hidden": true}
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# The coefficients
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print("Coefficients: \n", lin_reg_full.coef_)
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# The mean squared error
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print("Mean squared error: %.2f" % mean_squared_error(features_cleaned_labels[label_column], NA_pred))
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# The coefficient of determination: 1 is perfect prediction
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print("Coefficient of determination: %.2f" % r2_score(features_cleaned_labels[label_column], NA_pred))
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# %% jupyter={"source_hidden": true}
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feature_clean_columns[np.argmax(lin_reg_full.coef_)]
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# %% [markdown]
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# Ratio between stationary time and total location sensed time. A lat/long coordinate pair is labeled as stationary if its speed (distance/time) to the next coordinate pair is less than 1km/hr. A higher value represents a more stationary routine.
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# %% jupyter={"source_hidden": true}
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plt.scatter(features_clean_imputed[:,np.argmax(lin_reg_full.coef_)], features_cleaned_labels[label_column], color="black")
|
|
plt.scatter(features_clean_imputed[:,np.argmax(lin_reg_full.coef_)], NA_pred, color="red", linewidth=3)
|
|
|
|
plt.xticks()
|
|
plt.yticks()
|
|
|
|
fig = plt.gcf()
|
|
fig.set_size_inches(18.5, 10.5)
|
|
|
|
plt.show()
|
|
|
|
# %% [markdown]
|
|
# # RAPIDS models
|
|
|
|
# %% [markdown]
|
|
# ## PANAS negative affect
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input = pd.read_csv("../rapids/data/processed/models/population_model/input_PANAS_NA.csv")
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
|
#if "pid" in model_input.columns:
|
|
# index_columns.append("pid")
|
|
model_input.set_index(index_columns, inplace=True)
|
|
|
|
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_feature_colnames = ["gender", "startlanguage"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_features = data_x[categorical_feature_colnames].copy()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
mode_categorical_features = categorical_features.mode().iloc[0]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# fillna with mode
|
|
categorical_features = categorical_features.fillna(mode_categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# one-hot encoding
|
|
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
|
if not categorical_features.empty:
|
|
categorical_features = pd.get_dummies(categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x = pd.concat([numerical_features, categorical_features], axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x.dtypes
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
logo = LeaveOneGroupOut()
|
|
logo.get_n_splits(
|
|
train_x,
|
|
data_y,
|
|
groups=data_groups,
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
sum(data_y.isna())
|
|
|
|
# %% [markdown]
|
|
# ### Linear Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_rapids = linear_model.LinearRegression()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_scores = cross_val_score(
|
|
lin_reg_rapids,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
np.median(lin_reg_scores)
|
|
|
|
# %% [markdown]
|
|
# ### Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ridge_reg = linear_model.Ridge(alpha=.5)
|
|
|
|
# %% tags=[] jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Lasso
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lasso_reg = linear_model.Lasso(alpha=0.1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
lasso_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Bayesian Ridge
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
bayesian_ridge_reg = linear_model.BayesianRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
bayesian_ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### RANSAC (outlier robust regression)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ransac_reg = linear_model.RANSACRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
ransac_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Support vector regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
svr = svm.SVR()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Kernel Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
kridge = kernel_ridge.KernelRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
kridge,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Gaussian Process Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
gpr = gaussian_process.GaussianProcessRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
gpr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ## PANAS positive affect
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input = pd.read_csv("../rapids/data/processed/models/population_model/input_PANAS_PA.csv")
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
|
#if "pid" in model_input.columns:
|
|
# index_columns.append("pid")
|
|
model_input.set_index(index_columns, inplace=True)
|
|
|
|
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_feature_colnames = ["gender", "startlanguage"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_features = data_x[categorical_feature_colnames].copy()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
mode_categorical_features = categorical_features.mode().iloc[0]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# fillna with mode
|
|
categorical_features = categorical_features.fillna(mode_categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# one-hot encoding
|
|
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
|
if not categorical_features.empty:
|
|
categorical_features = pd.get_dummies(categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x = pd.concat([numerical_features, categorical_features], axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x.dtypes
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
logo = LeaveOneGroupOut()
|
|
logo.get_n_splits(
|
|
train_x,
|
|
data_y,
|
|
groups=data_groups,
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
sum(data_y.isna())
|
|
|
|
# %% [markdown]
|
|
# ### Linear Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_rapids = linear_model.LinearRegression()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_scores = cross_val_score(
|
|
lin_reg_rapids,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
np.median(lin_reg_scores)
|
|
|
|
# %% [markdown]
|
|
# ### Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ridge_reg = linear_model.Ridge(alpha=.5)
|
|
|
|
# %% tags=[] jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Lasso
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lasso_reg = linear_model.Lasso(alpha=0.1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
lasso_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Bayesian Ridge
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
bayesian_ridge_reg = linear_model.BayesianRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
bayesian_ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### RANSAC (outlier robust regression)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ransac_reg = linear_model.RANSACRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
ransac_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Support vector regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
svr = svm.SVR()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Kernel Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
kridge = kernel_ridge.KernelRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
kridge,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Gaussian Process Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
gpr = gaussian_process.GaussianProcessRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
gpr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ## JCQ demand
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input = pd.read_csv("../rapids/data/processed/models/population_model/input_JCQ_demand.csv")
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
|
#if "pid" in model_input.columns:
|
|
# index_columns.append("pid")
|
|
model_input.set_index(index_columns, inplace=True)
|
|
|
|
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_feature_colnames = ["gender", "startlanguage"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_features = data_x[categorical_feature_colnames].copy()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
mode_categorical_features = categorical_features.mode().iloc[0]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# fillna with mode
|
|
categorical_features = categorical_features.fillna(mode_categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# one-hot encoding
|
|
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
|
if not categorical_features.empty:
|
|
categorical_features = pd.get_dummies(categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x = pd.concat([numerical_features, categorical_features], axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x.dtypes
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
logo = LeaveOneGroupOut()
|
|
logo.get_n_splits(
|
|
train_x,
|
|
data_y,
|
|
groups=data_groups,
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
sum(data_y.isna())
|
|
|
|
# %% [markdown]
|
|
# ### Linear Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_rapids = linear_model.LinearRegression()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_scores = cross_val_score(
|
|
lin_reg_rapids,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
np.median(lin_reg_scores)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_scores
|
|
|
|
# %% [markdown]
|
|
# ### Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ridge_reg = linear_model.Ridge(alpha=.5)
|
|
|
|
# %% tags=[] jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Lasso
|
|
|
|
# %% jupyter={"source_hidden": true}
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|
lasso_reg = linear_model.Lasso(alpha=0.1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
lasso_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Bayesian Ridge
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
bayesian_ridge_reg = linear_model.BayesianRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
bayesian_ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### RANSAC (outlier robust regression)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ransac_reg = linear_model.RANSACRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
ransac_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Support vector regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
svr = svm.SVR()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Kernel Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
kridge = kernel_ridge.KernelRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
kridge,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Gaussian Process Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
gpr = gaussian_process.GaussianProcessRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
gpr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ## JCQ control
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input = pd.read_csv("../rapids/data/processed/models/population_model/input_JCQ_control.csv")
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
|
#if "pid" in model_input.columns:
|
|
# index_columns.append("pid")
|
|
model_input.set_index(index_columns, inplace=True)
|
|
|
|
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_feature_colnames = ["gender", "startlanguage"]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
categorical_features = data_x[categorical_feature_colnames].copy()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
mode_categorical_features = categorical_features.mode().iloc[0]
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# fillna with mode
|
|
categorical_features = categorical_features.fillna(mode_categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
# one-hot encoding
|
|
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
|
if not categorical_features.empty:
|
|
categorical_features = pd.get_dummies(categorical_features)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x = pd.concat([numerical_features, categorical_features], axis=1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
train_x.dtypes
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
logo = LeaveOneGroupOut()
|
|
logo.get_n_splits(
|
|
train_x,
|
|
data_y,
|
|
groups=data_groups,
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
sum(data_y.isna())
|
|
|
|
# %% [markdown]
|
|
# ### Linear Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_rapids = linear_model.LinearRegression()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lin_reg_scores = cross_val_score(
|
|
lin_reg_rapids,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
np.median(lin_reg_scores)
|
|
|
|
# %% [markdown]
|
|
# ### Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ridge_reg = linear_model.Ridge(alpha=.5)
|
|
|
|
# %% tags=[] jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Lasso
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
lasso_reg = linear_model.Lasso(alpha=0.1)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
lasso_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Bayesian Ridge
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
bayesian_ridge_reg = linear_model.BayesianRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
bayesian_ridge_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### RANSAC (outlier robust regression)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
ransac_reg = linear_model.RANSACRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
ransac_reg,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Support vector regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
svr = svm.SVR()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
np.median(
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
svr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown] tags=[]
|
|
# ### Kernel Ridge regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
kridge = kernel_ridge.KernelRidge()
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
cross_val_score(
|
|
kridge,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ### Gaussian Process Regression
|
|
|
|
# %% jupyter={"source_hidden": true}
|
|
gpr = gaussian_process.GaussianProcessRegressor()
|
|
|
|
# %% jupyter={"source_hidden": true} tags=[]
|
|
cross_val_score(
|
|
gpr,
|
|
X=imputer.fit_transform(train_x),
|
|
y=data_y,
|
|
groups=data_groups,
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2"
|
|
)
|
|
|
|
# %% jupyter={"source_hidden": true}
|