451 lines
11 KiB
Python
451 lines
11 KiB
Python
# ---
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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 os
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import sys
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import numpy as np
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import pandas as pd
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import xgboost as xg
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from machine_learning.helper import prepare_regression_model_input
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from sklearn import gaussian_process, kernel_ridge, linear_model, svm
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from sklearn.dummy import DummyRegressor
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from sklearn.impute import SimpleImputer
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate
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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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# %% jupyter={"source_hidden": true}
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model_input = pd.read_csv(
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"../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv"
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)
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# %% jupyter={"source_hidden": true}
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cv_method = "half_logo" # logo, half_logo, 5kfold
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train_x, data_y, data_groups = prepare_regression_model_input(model_input, cv_method)
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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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# ### 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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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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# %% jupyter={"source_hidden": true}
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dummy_regressor = cross_validate(
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dummy_regr,
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(dummy_regressor["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(dummy_regressor["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(dummy_regressor["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(dummy_regressor["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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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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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=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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(lin_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(lin_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(lin_reg_scores["test_neg_root_mean_squared_error"]),
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)
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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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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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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=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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(xgb_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(xgb_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(xgb_reg_scores["test_neg_root_mean_squared_error"]),
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)
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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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imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
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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=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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(xgb_psuedo_huber_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(xgb_psuedo_huber_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(xgb_psuedo_huber_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(xgb_psuedo_huber_reg_scores["test_r2"]))
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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=0.5)
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# %% tags=[] jupyter={"source_hidden": true}
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ridge_reg_scores = cross_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(ridge_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(ridge_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(ridge_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(ridge_reg_scores["test_r2"]))
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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_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(lasso_reg_score["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(lasso_reg_score["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(lasso_reg_score["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(lasso_reg_score["test_r2"]))
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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_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(bayesian_ridge_reg_score["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(bayesian_ridge_reg_score["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(bayesian_ridge_reg_score["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(bayesian_ridge_reg_score["test_r2"]))
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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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ransac_reg_scores = cross_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(ransac_reg_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(ransac_reg_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(ransac_reg_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(ransac_reg_scores["test_r2"]))
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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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svr_scores = cross_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error", np.median(svr_scores["test_neg_mean_squared_error"])
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)
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print(
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"Negative Mean Absolute Error",
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np.median(svr_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(svr_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(svr_scores["test_r2"]))
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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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kridge_scores = cross_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error",
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np.median(kridge_scores["test_neg_mean_squared_error"]),
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)
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print(
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"Negative Mean Absolute Error",
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np.median(kridge_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(kridge_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(kridge_scores["test_r2"]))
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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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gpr_scores = cross_validate(
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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=(
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"r2",
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"neg_mean_squared_error",
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"neg_mean_absolute_error",
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"neg_root_mean_squared_error",
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),
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)
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print(
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"Negative Mean Squared Error", np.median(gpr_scores["test_neg_mean_squared_error"])
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)
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print(
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"Negative Mean Absolute Error",
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np.median(gpr_scores["test_neg_mean_absolute_error"]),
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)
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print(
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"Negative Root Mean Squared Error",
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np.median(gpr_scores["test_neg_root_mean_squared_error"]),
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)
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print("R2", np.median(gpr_scores["test_r2"]))
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# %%
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