Combine different segment scripts and set ml pipeline as a regression problem.
Primoz
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8defb271c9
commit
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# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.13.0
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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# %% jupyter={"source_hidden": true}
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# %matplotlib inline
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import datetime
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import importlib
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import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import yaml
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from pyprojroot import here
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from sklearn import linear_model, svm, kernel_ridge, gaussian_process
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import machine_learning.features_sensor
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import machine_learning.labels
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import machine_learning.model
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# %% [markdown]
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# # RAPIDS models
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# %% [markdown]
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# ## PANAS negative affect
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# %% jupyter={"source_hidden": true}
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# model_input = pd.read_csv("../data/input_PANAS_NA.csv") # Nestandardizirani podatki - pred temeljitim čiščenjem
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model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki - pred temeljitim čiščenjem
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# %% [markdown]
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# ### NaNs before dropping cols and rows
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# %% jupyter={"source_hidden": true}
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
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# %% jupyter={"source_hidden": true}
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nan_cols = list(model_input.loc[:, model_input.isna().all()].columns)
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nan_cols
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# %% jupyter={"source_hidden": true}
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model_input.dropna(axis=1, how="all", inplace=True)
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model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
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# %% [markdown]
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# ### NaNs after dropping NaN cols and rows where target is NaN
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# %% jupyter={"source_hidden": true}
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sns.set(rc={"figure.figsize":(16, 8)})
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sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
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# %% jupyter={"source_hidden": true}
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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model_input.set_index(index_columns, inplace=True)
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cv_method = '5kfold'
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if cv_method == 'half_logo':
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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else:
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model_input['pid_index'] = model_input.groupby('pid').cumcount()
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model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
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model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
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model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
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data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
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# %% jupyter={"source_hidden": true}
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categorical_feature_colnames = ["gender", "startlanguage"]
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# %% jupyter={"source_hidden": true}
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categorical_features = data_x[categorical_feature_colnames].copy()
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# %% jupyter={"source_hidden": true}
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mode_categorical_features = categorical_features.mode().iloc[0]
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# %% jupyter={"source_hidden": true}
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# fillna with mode
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categorical_features = categorical_features.fillna(mode_categorical_features)
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# %% jupyter={"source_hidden": true}
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# one-hot encoding
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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# %% jupyter={"source_hidden": true}
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numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
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# %% jupyter={"source_hidden": true}
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train_x = pd.concat([numerical_features, categorical_features], axis=1)
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# %% jupyter={"source_hidden": true}
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train_x.dtypes
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# %% jupyter={"source_hidden": true}
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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train_x,
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data_y,
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groups=data_groups,
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)
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# Defaults to 5 k folds in cross_validate method
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if cv_method != 'logo' and cv_method != 'half_logo':
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logo = None
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# %% jupyter={"source_hidden": true}
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sum(data_y.isna())
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# %% [markdown]
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# ### Linear Regression
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# %% jupyter={"source_hidden": true}
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lin_reg_rapids = linear_model.LinearRegression()
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# %% jupyter={"source_hidden": true}
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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# %% jupyter={"source_hidden": true}
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lin_reg_scores = cross_val_score(
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lin_reg_rapids,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring='r2'
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)
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lin_reg_scores
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np.median(lin_reg_scores)
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# %% [markdown]
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# ### Ridge regression
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# %% jupyter={"source_hidden": true}
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ridge_reg = linear_model.Ridge(alpha=.5)
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# %% tags=[] jupyter={"source_hidden": true}
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ridge_reg_scores = cross_val_score(
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ridge_reg,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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np.median(ridge_reg_scores)
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# %% [markdown]
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# ### Lasso
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# %% jupyter={"source_hidden": true}
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lasso_reg = linear_model.Lasso(alpha=0.1)
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# %% jupyter={"source_hidden": true}
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lasso_reg_score = cross_val_score(
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lasso_reg,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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np.median(lasso_reg_score)
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# %% [markdown]
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# ### Bayesian Ridge
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# %% jupyter={"source_hidden": true}
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bayesian_ridge_reg = linear_model.BayesianRidge()
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# %% jupyter={"source_hidden": true}
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bayesian_ridge_reg_score = cross_val_score(
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bayesian_ridge_reg,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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np.median(bayesian_ridge_reg_score)
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# %% [markdown]
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# ### RANSAC (outlier robust regression)
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# %% jupyter={"source_hidden": true}
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ransac_reg = linear_model.RANSACRegressor()
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# %% jupyter={"source_hidden": true}
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np.median(
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cross_val_score(
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ransac_reg,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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)
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# %% [markdown]
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# ### Support vector regression
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# %% jupyter={"source_hidden": true}
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svr = svm.SVR()
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# %% jupyter={"source_hidden": true}
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np.median(
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cross_val_score(
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svr,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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)
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# %% [markdown]
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# ### Kernel Ridge regression
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# %% jupyter={"source_hidden": true}
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kridge = kernel_ridge.KernelRidge()
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# %% jupyter={"source_hidden": true}
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np.median(
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cross_val_score(
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kridge,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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)
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# %% [markdown]
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# ### Gaussian Process Regression
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# %% jupyter={"source_hidden": true}
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gpr = gaussian_process.GaussianProcessRegressor()
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# %% jupyter={"source_hidden": true}
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np.median(
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cross_val_score(
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gpr,
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X=imputer.fit_transform(train_x),
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring="r2"
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)
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)
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# %%
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@ -1,332 +0,0 @@
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# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:percent
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# text_representation:
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# extension: .py
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# format_name: percent
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# format_version: '1.3'
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# jupytext_version: 1.13.0
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# kernelspec:
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# display_name: straw2analysis
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# language: python
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# name: straw2analysis
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# ---
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# %% jupyter={"source_hidden": true}
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# %matplotlib inline
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import datetime
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import importlib
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import os
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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import seaborn as sns
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import yaml
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from pyprojroot import here
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from sklearn import linear_model, svm, kernel_ridge, gaussian_process
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, cross_validate
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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from sklearn.dummy import DummyRegressor
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import xgboost as xg
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from IPython.core.interactiveshell import InteractiveShell
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InteractiveShell.ast_node_interactivity = "all"
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nb_dir = os.path.split(os.getcwd())[0]
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if nb_dir not in sys.path:
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sys.path.append(nb_dir)
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import machine_learning.features_sensor
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import machine_learning.labels
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import machine_learning.model
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# %% [markdown]
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# # RAPIDS models
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# %% [markdown]
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# ## PANAS negative affect
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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv("../data/daily_18_hours_all_targets/input_PANAS_negative_affect_mean.csv")
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# %% jupyter={"source_hidden": true}
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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#if "pid" in model_input.columns:
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# index_columns.append("pid")
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model_input.set_index(index_columns, inplace=True)
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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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# %% jupyter={"source_hidden": true}
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categorical_feature_colnames = ["gender", "startlanguage"]
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additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
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categorical_feature_colnames += additional_categorical_features
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# %% jupyter={"source_hidden": true}
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categorical_features = data_x[categorical_feature_colnames].copy()
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# %% jupyter={"source_hidden": true}
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mode_categorical_features = categorical_features.mode().iloc[0]
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# %% jupyter={"source_hidden": true}
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# fillna with mode
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categorical_features = categorical_features.fillna(mode_categorical_features)
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# %% jupyter={"source_hidden": true}
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# one-hot encoding
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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# %% jupyter={"source_hidden": true}
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numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
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# %% jupyter={"source_hidden": true}
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train_x = pd.concat([numerical_features, categorical_features], axis=1)
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# %% jupyter={"source_hidden": true}
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train_x.dtypes
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# %% jupyter={"source_hidden": true}
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logo = LeaveOneGroupOut()
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logo.get_n_splits(
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train_x,
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data_y,
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groups=data_groups,
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)
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# %% jupyter={"source_hidden": true}
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sum(data_y.isna())
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# %% [markdown]
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# ### Baseline: Dummy Regression (mean)
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dummy_regr = DummyRegressor(strategy="mean")
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# %% jupyter={"source_hidden": true}
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lin_reg_scores = cross_validate(
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dummy_regr,
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X=train_x,
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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)
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print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(lin_reg_scores['test_r2']))
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# %% [markdown]
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# ### Linear Regression
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# %% jupyter={"source_hidden": true}
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lin_reg_rapids = linear_model.LinearRegression()
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# %% jupyter={"source_hidden": true}
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lin_reg_scores = cross_validate(
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lin_reg_rapids,
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X=train_x,
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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)
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print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(lin_reg_scores['test_r2']))
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# %% [markdown]
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# ### XGBRegressor Linear Regression
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# %% jupyter={"source_hidden": true}
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xgb_r = xg.XGBRegressor(objective ='reg:squarederror', n_estimators = 10)
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# %% jupyter={"source_hidden": true}
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xgb_reg_scores = cross_validate(
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xgb_r,
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X=train_x,
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y=data_y,
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groups=data_groups,
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cv=logo,
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n_jobs=-1,
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scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
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)
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print("Negative Mean Squared Error", np.median(xgb_reg_scores['test_neg_mean_squared_error']))
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print("Negative Mean Absolute Error", np.median(xgb_reg_scores['test_neg_mean_absolute_error']))
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print("Negative Root Mean Squared Error", np.median(xgb_reg_scores['test_neg_root_mean_squared_error']))
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print("R2", np.median(xgb_reg_scores['test_r2']))
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# %% [markdown]
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# ### XGBRegressor Pseudo Huber Error Regression
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# %% jupyter={"source_hidden": true}
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xgb_psuedo_huber_r = xg.XGBRegressor(objective ='reg:pseudohubererror', n_estimators = 10)
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# %% jupyter={"source_hidden": true}
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xgb_psuedo_huber_reg_scores = cross_validate(
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xgb_psuedo_huber_r,
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X=train_x,
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||||
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=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=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=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=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=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=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=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']))
|
||||
|
||||
# %%
|
||||
|
|
@ -123,7 +123,7 @@ dummy_regr = DummyRegressor(strategy="mean")
|
|||
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lin_reg_scores = cross_validate(
|
||||
dummy_regressor = cross_validate(
|
||||
dummy_regr,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
|
|
@ -132,10 +132,10 @@ lin_reg_scores = cross_validate(
|
|||
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']))
|
||||
print("Negative Mean Squared Error", np.median(dummy_regressor['test_neg_mean_squared_error']))
|
||||
print("Negative Mean Absolute Error", np.median(dummy_regressor['test_neg_mean_absolute_error']))
|
||||
print("Negative Root Mean Squared Error", np.median(dummy_regressor['test_neg_root_mean_squared_error']))
|
||||
print("R2", np.median(dummy_regressor['test_r2']))
|
||||
|
||||
# %% [markdown]
|
||||
# ### Linear Regression
|
||||
Loading…
Reference in New Issue