stress_at_work_analysis/exploration/ml_pipeline_daily.py

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# %% 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 - pred temeljitim čiščenjem
model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki - pred temeljitim čiščenjem
# %% [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"
    )
)
# %%