Datasets (phone&E4 features) and Jupyter script of regression models.

exploration/ex_all_feat_ml_pipeline.py

@@ -0,0 +1,272 @@
+# ---
+# 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"
+    )
+)
+# %%