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

exploration/ml_pipeline_daily.py

@@ -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/input_PANAS_NA.csv") # Nestandardizirani podatki - pred temeljitim čiščenjem
-model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki
+model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki - pred temeljitim čiščenjem
-
 # %% [markdown]
 # ### NaNs before dropping cols and rows
 

exploration/ml_pipeline_daily_cleaned.py

@@ -0,0 +1,338 @@
+# ---
+# 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, 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']))
+
+# %%