268 lines
8.1 KiB
Python
268 lines
8.1 KiB
Python
from pathlib import Path
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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, 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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from xgboost import XGBRegressor
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import pandas as pd
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import numpy as np
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def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
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if left.empty:
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return right
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elif right.empty:
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return left
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else:
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return pd.merge(
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left,
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right,
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how="outer",
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left_index=True,
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right_index=True,
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validate="one_to_one",
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)
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def to_csv_with_settings(
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df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
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) -> None:
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full_path = construct_full_path(folder, filename_prefix, data_type)
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df.to_csv(
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path_or_buf=full_path,
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sep=",",
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na_rep="NA",
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header=True,
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index=True,
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encoding="utf-8",
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)
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print("Exported the dataframe to " + str(full_path))
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def read_csv_with_settings(
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folder: Path, filename_prefix: str, data_type: str, grouping_variable: list
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) -> pd.DataFrame:
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full_path = construct_full_path(folder, filename_prefix, data_type)
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return pd.read_csv(
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filepath_or_buffer=full_path,
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sep=",",
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header=0,
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na_values="NA",
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encoding="utf-8",
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index_col=(["participant_id"] + grouping_variable),
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parse_dates=True,
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infer_datetime_format=True,
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cache_dates=True,
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)
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def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> Path:
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export_filename = filename_prefix + "_" + data_type + ".csv"
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full_path = folder / export_filename
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return full_path
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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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def prepare_model_input(input_csv):
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model_input = pd.read_csv(input_csv)
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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", "limesurvey_demand_control_ratio_quartile"]
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#TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
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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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#TODO: check if mostcommonactivity is indeed a categorical features after aggregating
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#categorical_feature_colnames += additional_categorical_features
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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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return train_x, data_y, data_groups
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def run_all_models(input_csv):
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# Prepare data
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train_x, data_y, data_groups = prepare_model_input(input_csv)
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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,
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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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