Processing of a newly cleaned script. Addition of two ML models. And modifications with one hot encoding.

ml_pipeline
Primoz 2022-10-10 16:47:00 +02:00
parent 27b2282ee0
commit 466cd3dc23
3 changed files with 1065 additions and 3 deletions

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@ -46,9 +46,8 @@ import machine_learning.model
# ## 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
# 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

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@ -0,0 +1,338 @@
# ---
# jupyter:
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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, cross_validate
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor
import xgboost as xg
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
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_negative_affect_mean.csv")
# %% 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"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
# %% 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]
# ### Baseline: Dummy Regression (mean)
dummy_regr = DummyRegressor(strategy="mean")
# %% jupyter={"source_hidden": true}
lin_reg_scores = cross_validate(
dummy_regr,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(lin_reg_scores['test_r2']))
# %% [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_validate(
lin_reg_rapids,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(lin_reg_scores['test_r2']))
# %% [markdown]
# ### XGBRegressor Linear Regression
# %% jupyter={"source_hidden": true}
xgb_r = xg.XGBRegressor(objective ='reg:squarederror', n_estimators = 10)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %% jupyter={"source_hidden": true}
xgb_reg_scores = cross_validate(
xgb_r,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(xgb_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(xgb_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(xgb_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(xgb_reg_scores['test_r2']))
# %% [markdown]
# ### XGBRegressor Pseudo Huber Error Regression
# %% jupyter={"source_hidden": true}
xgb_psuedo_huber_r = xg.XGBRegressor(objective ='reg:pseudohubererror', n_estimators = 10)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %% jupyter={"source_hidden": true}
xgb_psuedo_huber_reg_scores = cross_validate(
xgb_psuedo_huber_r,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(xgb_psuedo_huber_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(xgb_psuedo_huber_reg_scores['test_r2']))
# %% [markdown]
# ### Ridge regression
# %% jupyter={"source_hidden": true}
ridge_reg = linear_model.Ridge(alpha=.5)
# %% tags=[] jupyter={"source_hidden": true}
ridge_reg_scores = cross_validate(
ridge_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(ridge_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(ridge_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(ridge_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(ridge_reg_scores['test_r2']))
# %% [markdown]
# ### Lasso
# %% jupyter={"source_hidden": true}
lasso_reg = linear_model.Lasso(alpha=0.1)
# %% jupyter={"source_hidden": true}
lasso_reg_score = cross_validate(
lasso_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(lasso_reg_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(lasso_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(lasso_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.median(lasso_reg_score['test_r2']))
# %% [markdown]
# ### Bayesian Ridge
# %% jupyter={"source_hidden": true}
bayesian_ridge_reg = linear_model.BayesianRidge()
# %% jupyter={"source_hidden": true}
bayesian_ridge_reg_score = cross_validate(
bayesian_ridge_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(bayesian_ridge_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.median(bayesian_ridge_reg_score['test_r2']))
# %% [markdown]
# ### RANSAC (outlier robust regression)
# %% jupyter={"source_hidden": true}
ransac_reg = linear_model.RANSACRegressor()
# %% jupyter={"source_hidden": true}
ransac_reg_scores = cross_validate(
ransac_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(ransac_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(ransac_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(ransac_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(ransac_reg_scores['test_r2']))
# %% [markdown]
# ### Support vector regression
# %% jupyter={"source_hidden": true}
svr = svm.SVR()
# %% jupyter={"source_hidden": true}
svr_scores = cross_validate(
svr,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(svr_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(svr_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(svr_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(svr_scores['test_r2']))
# %% [markdown]
# ### Kernel Ridge regression
# %% jupyter={"source_hidden": true}
kridge = kernel_ridge.KernelRidge()
# %% jupyter={"source_hidden": true}
kridge_scores = cross_validate(
kridge,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(kridge_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(kridge_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(kridge_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(kridge_scores['test_r2']))
# %% [markdown]
# ### Gaussian Process Regression
# %% jupyter={"source_hidden": true}
gpr = gaussian_process.GaussianProcessRegressor()
# %% jupyter={"source_hidden": true}
gpr_scores = cross_validate(
gpr,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(gpr_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(gpr_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(gpr_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(gpr_scores['test_r2']))
# %%