473 lines
13 KiB
Python
473 lines
13 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 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, ensemble
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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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from xgboost import XGBRegressor
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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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#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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# %% 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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# ### 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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def insert_row(df, row):
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return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
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# %%
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def run_all_models(input_csv):
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# Prepare data
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model_input = pd.read_csv(input_csv)
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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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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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data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
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categorical_feature_colnames = ["gender", "startlanguage"]
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categorical_features = data_x[categorical_feature_colnames].copy()
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mode_categorical_features = categorical_features.mode().iloc[0]
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# fillna with mode
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categorical_features = categorical_features.fillna(mode_categorical_features)
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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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numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
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train_x = pd.concat([numerical_features, categorical_features], axis=1)
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imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
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train_x_imputed = imputer.fit_transform(train_x)
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# Prepare cross validation
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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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scores = pd.DataFrame(columns=["method", "median", "max"])
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# Validate models
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lin_reg_rapids = linear_model.LinearRegression()
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lin_reg_scores = cross_val_score(
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lin_reg_rapids,
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X=train_x_imputed,
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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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print("Linear regression:")
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print(np.median(lin_reg_scores))
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scores = insert_row(scores, ["Linear regression",np.median(lin_reg_scores),np.max(lin_reg_scores)])
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ridge_reg = linear_model.Ridge(alpha=.5)
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ridge_reg_scores = cross_val_score(
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ridge_reg,
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X=train_x_imputed,
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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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print("Ridge regression")
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print(np.median(ridge_reg_scores))
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scores = insert_row(scores, ["Ridge regression",np.median(ridge_reg_scores),np.max(ridge_reg_scores)])
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lasso_reg = linear_model.Lasso(alpha=0.1)
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lasso_reg_score = cross_val_score(
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lasso_reg,
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X=train_x_imputed,
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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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print("Lasso regression")
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print(np.median(lasso_reg_score))
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scores = insert_row(scores, ["Lasso regression",np.median(lasso_reg_score),np.max(lasso_reg_score)])
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bayesian_ridge_reg = linear_model.BayesianRidge()
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bayesian_ridge_reg_score = cross_val_score(
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bayesian_ridge_reg,
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X=train_x_imputed,
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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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print("Bayesian Ridge")
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print(np.median(bayesian_ridge_reg_score))
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scores = insert_row(scores, ["Bayesian Ridge",np.median(bayesian_ridge_reg_score),np.max(bayesian_ridge_reg_score)])
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ransac_reg = linear_model.RANSACRegressor()
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ransac_reg_score = cross_val_score(
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ransac_reg,
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X=train_x_imputed,
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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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print("RANSAC (outlier robust regression)")
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print(np.median(ransac_reg_score))
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scores = insert_row(scores, ["RANSAC",np.median(ransac_reg_score),np.max(ransac_reg_score)])
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svr = svm.SVR()
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svr_score = cross_val_score(
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svr,
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X=train_x_imputed,
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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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print("Support vector regression")
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print(np.median(svr_score))
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scores = insert_row(scores, ["Support vector regression",np.median(svr_score),np.max(svr_score)])
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kridge = kernel_ridge.KernelRidge()
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kridge_score = cross_val_score(
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kridge,
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X=train_x_imputed,
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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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print("Kernel Ridge regression")
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print(np.median(kridge_score))
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scores = insert_row(scores, ["Kernel Ridge regression",np.median(kridge_score),np.max(kridge_score)])
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gpr = gaussian_process.GaussianProcessRegressor()
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gpr_score = cross_val_score(
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gpr,
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X=train_x_imputed,
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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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print("Gaussian Process Regression")
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print(np.median(gpr_score))
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scores = insert_row(scores, ["Gaussian Process Regression",np.median(gpr_score),np.max(gpr_score)])
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rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
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rfr_score = cross_val_score(
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rfr,
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X=train_x_imputed,
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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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print("Random Forest Regression")
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print(np.median(rfr_score))
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scores = insert_row(scores, ["Random Forest Regression",np.median(rfr_score),np.max(rfr_score)])
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xgb = XGBRegressor()
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xgb_score = cross_val_score(
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xgb,
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X=train_x_imputed,
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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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print("XGBoost Regressor")
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print(np.median(xgb_score))
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scores = insert_row(scores, ["XGBoost Regressor",np.median(xgb_score),np.max(xgb_score)])
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ada = ensemble.AdaBoostRegressor()
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ada_score = cross_val_score(
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ada,
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X=train_x_imputed,
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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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print("ADA Boost Regressor")
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print(np.median(ada_score))
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scores = insert_row(scores, ["ADA Boost Regressor",np.median(ada_score),np.max(ada_score)])
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return scores
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