Compare commits
No commits in common. "8b8d626cf0091d42856604ec90a37a24638f585e" and "6295cc8e9140ffdf4c7bc80669cd12350df75b48" have entirely different histories.
8b8d626cf0
...
6295cc8e91
|
|
@ -7,5 +7,3 @@ __pycache__/
|
|||
/statistical_analysis/*.ipynb
|
||||
/machine_learning/intermediate_results/
|
||||
/data/features/
|
||||
/data/baseline/
|
||||
/data/*input*.csv
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -1,272 +0,0 @@
|
|||
# ---
|
||||
# jupyter:
|
||||
# jupytext:
|
||||
# formats: ipynb,py:percent
|
||||
# text_representation:
|
||||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.13.0
|
||||
# kernelspec:
|
||||
# display_name: straw2analysis
|
||||
# language: python
|
||||
# name: straw2analysis
|
||||
# ---
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %matplotlib inline
|
||||
import datetime
|
||||
import importlib
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import yaml
|
||||
from pyprojroot import here
|
||||
from sklearn import linear_model, svm, kernel_ridge, gaussian_process
|
||||
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
from sklearn.impute import SimpleImputer
|
||||
|
||||
nb_dir = os.path.split(os.getcwd())[0]
|
||||
if nb_dir not in sys.path:
|
||||
sys.path.append(nb_dir)
|
||||
|
||||
import machine_learning.features_sensor
|
||||
import machine_learning.labels
|
||||
import machine_learning.model
|
||||
|
||||
# %% [markdown]
|
||||
# # RAPIDS models
|
||||
|
||||
# %% [markdown]
|
||||
# ## PANAS negative affect
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# model_input = pd.read_csv("../data/input_PANAS_NA.csv") # Nestandardizirani podatki
|
||||
model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki
|
||||
|
||||
# %% [markdown]
|
||||
# ### NaNs before dropping cols and rows
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
nan_cols = list(model_input.loc[:, model_input.isna().all()].columns)
|
||||
nan_cols
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
model_input.dropna(axis=1, how="all", inplace=True)
|
||||
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
||||
|
||||
# %% [markdown]
|
||||
# ### NaNs after dropping NaN cols and rows where target is NaN
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
|
||||
|
||||
# %% 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'
|
||||
)
|
||||
lin_reg_scores
|
||||
np.median(lin_reg_scores)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Ridge regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
ridge_reg = linear_model.Ridge(alpha=.5)
|
||||
|
||||
# %% tags=[] jupyter={"source_hidden": true}
|
||||
ridge_reg_scores = cross_val_score(
|
||||
ridge_reg,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
np.median(ridge_reg_scores)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Lasso
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lasso_reg = linear_model.Lasso(alpha=0.1)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lasso_reg_score = cross_val_score(
|
||||
lasso_reg,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
np.median(lasso_reg_score)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Bayesian Ridge
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
bayesian_ridge_reg = linear_model.BayesianRidge()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
bayesian_ridge_reg_score = 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"
|
||||
)
|
||||
np.median(bayesian_ridge_reg_score)
|
||||
|
||||
# %% [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"
|
||||
)
|
||||
)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Kernel Ridge regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
kridge = kernel_ridge.KernelRidge()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
np.median(
|
||||
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}
|
||||
|
||||
np.median(
|
||||
cross_val_score(
|
||||
gpr,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
)
|
||||
# %%
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -7,7 +7,7 @@
|
|||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.13.0
|
||||
# jupytext_version: 1.11.2
|
||||
# kernelspec:
|
||||
# display_name: straw2analysis
|
||||
# language: python
|
||||
|
|
@ -17,7 +17,6 @@
|
|||
# %%
|
||||
import os
|
||||
import sys
|
||||
import datetime
|
||||
|
||||
import seaborn as sns
|
||||
|
||||
|
|
@ -27,7 +26,6 @@ if nb_dir not in sys.path:
|
|||
import participants.query_db
|
||||
from features.esm import *
|
||||
from features.esm_JCQ import *
|
||||
from features.esm_SAM import *
|
||||
|
||||
# %%
|
||||
participants_inactive_usernames = participants.query_db.get_usernames(
|
||||
|
|
@ -101,12 +99,6 @@ df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
|
|||
# %% [markdown]
|
||||
# # Stress appraisal measure
|
||||
|
||||
# %%
|
||||
df_SAM_all = extract_stressful_events(df_esm_inactive)
|
||||
|
||||
# %%
|
||||
df_SAM_all.head()
|
||||
|
||||
# %%
|
||||
df_esm_SAM = df_esm_preprocessed[
|
||||
(df_esm_preprocessed["questionnaire_id"] >= 87)
|
||||
|
|
|
|||
2
rapids
2
rapids
|
|
@ -1 +1 @@
|
|||
Subproject commit f78aa3e7b3567423b44045766b230cd60d557cb0
|
||||
Subproject commit bf9c764c97f076f4af288f7afa1a32931996b2db
|
||||
|
|
@ -6,7 +6,7 @@
|
|||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.13.0
|
||||
# jupytext_version: 1.12.0
|
||||
# kernelspec:
|
||||
# display_name: straw2analysis
|
||||
# language: python
|
||||
|
|
@ -14,7 +14,25 @@
|
|||
# ---
|
||||
|
||||
# %%
|
||||
SAVE_FIGS = False
|
||||
# %matplotlib inline
|
||||
import datetime
|
||||
import os
|
||||
import sys
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import statsmodels.api as sm
|
||||
import statsmodels.formula.api as smf
|
||||
|
||||
nb_dir = os.path.split(os.getcwd())[0]
|
||||
if nb_dir not in sys.path:
|
||||
sys.path.append(nb_dir)
|
||||
import participants.query_db
|
||||
from features.esm import *
|
||||
|
||||
# %%
|
||||
SAVE_FIGS = True
|
||||
FIG_HEIGHT = 5
|
||||
FIG_ASPECT = 1.7
|
||||
FIG_COLOUR = "#28827C"
|
||||
|
|
|
|||
Loading…
Reference in New Issue