diff --git a/config/environment.yml b/config/environment.yml index 5d5ec3e..cf233ac 100644 --- a/config/environment.yml +++ b/config/environment.yml @@ -22,4 +22,5 @@ dependencies: - scikit-learn - sqlalchemy - statsmodels - - tabulate \ No newline at end of file + - tabulate + - xgboost \ No newline at end of file diff --git a/machine_learning/helper.py b/machine_learning/helper.py index aae5af9..69822d0 100644 --- a/machine_learning/helper.py +++ b/machine_learning/helper.py @@ -1,15 +1,18 @@ from pathlib import Path -from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble, naive_bayes, neighbors, tree -from sklearn.model_selection import LeaveOneGroupOut, cross_validate, cross_validate -from sklearn.metrics import mean_squared_error, r2_score -from sklearn.impute import SimpleImputer -from sklearn.dummy import DummyRegressor, DummyClassifier -from xgboost import XGBRegressor, XGBClassifier -import xgboost as xg - -import pandas as pd import numpy as np +import pandas as pd +from sklearn import ( + ensemble, + gaussian_process, + kernel_ridge, + linear_model, + naive_bayes, + svm, +) +from sklearn.dummy import DummyClassifier, DummyRegressor +from sklearn.model_selection import LeaveOneGroupOut, cross_validate +from xgboost import XGBClassifier, XGBRegressor def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame: @@ -65,28 +68,48 @@ def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> P full_path = folder / export_filename return full_path + def insert_row(df, row): return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True) -def prepare_regression_model_input(input_csv): +def prepare_regression_model_input(input_csv): model_input = pd.read_csv(input_csv) - index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] + index_columns = [ + "local_segment", + "local_segment_label", + "local_segment_start_datetime", + "local_segment_end_datetime", + ] model_input.set_index(index_columns, inplace=True) - data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"] + data_x, data_y, data_groups = ( + model_input.drop(["target", "pid"], axis=1), + model_input["target"], + model_input["pid"], + ) - categorical_feature_colnames = ["gender", "startlanguage", "limesurvey_demand_control_ratio_quartile"] - additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col] + categorical_feature_colnames = [ + "gender", + "startlanguage", + "limesurvey_demand_control_ratio_quartile", + ] + additional_categorical_features = [ + col + for col in data_x.columns + if "mostcommonactivity" in col or "homelabel" in col + ] categorical_feature_colnames += additional_categorical_features - #TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully + # TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully categorical_features = data_x[categorical_feature_colnames].copy() mode_categorical_features = categorical_features.mode().iloc[0] # fillna with mode categorical_features = categorical_features.fillna(mode_categorical_features) # one-hot encoding - categorical_features = categorical_features.apply(lambda col: col.astype("category")) + categorical_features = categorical_features.apply( + lambda col: col.astype("category") + ) if not categorical_features.empty: categorical_features = pd.get_dummies(categorical_features) @@ -108,7 +131,7 @@ def run_all_regression_models(input_csv): data_y, groups=data_groups, ) - metrics = ['r2', 'neg_mean_absolute_error', 'neg_root_mean_squared_error'] + metrics = ["r2", "neg_mean_absolute_error", "neg_root_mean_squared_error"] test_metrics = ["test_" + metric for metric in metrics] scores = pd.DataFrame(columns=["method", "max", "nanmedian"]) @@ -121,13 +144,13 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("Dummy model:") - print("R^2: ", np.nanmedian(dummy_regr_scores['test_r2'])) - + print("R^2: ", np.nanmedian(dummy_regr_scores["test_r2"])) + scores_df = pd.DataFrame(dummy_regr_scores)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "dummy" scores = pd.concat([scores, scores_df]) @@ -139,17 +162,17 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("Linear regression:") - print("R^2: ", np.nanmedian(lin_reg_scores['test_r2'])) + print("R^2: ", np.nanmedian(lin_reg_scores["test_r2"])) scores_df = pd.DataFrame(lin_reg_scores)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "linear_reg" scores = pd.concat([scores, scores_df]) - ridge_reg = linear_model.Ridge(alpha=.5) + ridge_reg = linear_model.Ridge(alpha=0.5) ridge_reg_scores = cross_validate( ridge_reg, X=data_x, @@ -157,16 +180,15 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("Ridge regression") scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "ridge_reg" scores = pd.concat([scores, scores_df]) - lasso_reg = linear_model.Lasso(alpha=0.1) lasso_reg_score = cross_validate( lasso_reg, @@ -175,12 +197,12 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("Lasso regression") scores_df = pd.DataFrame(lasso_reg_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "lasso_reg" scores = pd.concat([scores, scores_df]) @@ -192,12 +214,12 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("Bayesian Ridge") scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "bayesian_ridge" scores = pd.concat([scores, scores_df]) @@ -209,29 +231,23 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("RANSAC (outlier robust regression)") scores_df = pd.DataFrame(ransac_reg_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "RANSAC" scores = pd.concat([scores, scores_df]) svr = svm.SVR() svr_score = cross_validate( - svr, - X=data_x, - y=data_y, - groups=data_groups, - cv=logo, - n_jobs=-1, - scoring=metrics + svr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics ) print("Support vector regression") - + scores_df = pd.DataFrame(svr_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "SVR" scores = pd.concat([scores, scores_df]) @@ -243,80 +259,56 @@ def run_all_regression_models(input_csv): groups=data_groups, cv=logo, n_jobs=-1, - scoring=metrics + scoring=metrics, ) print("Kernel Ridge regression") - + scores_df = pd.DataFrame(kridge_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "kernel_ridge" scores = pd.concat([scores, scores_df]) gpr = gaussian_process.GaussianProcessRegressor() gpr_score = cross_validate( - gpr, - X=data_x, - y=data_y, - groups=data_groups, - cv=logo, - n_jobs=-1, - scoring=metrics + gpr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics ) print("Gaussian Process Regression") scores_df = pd.DataFrame(gpr_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "gaussian_proc" scores = pd.concat([scores, scores_df]) rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1) rfr_score = cross_validate( - rfr, - X=data_x, - y=data_y, - groups=data_groups, - cv=logo, - n_jobs=-1, - scoring=metrics + rfr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics ) print("Random Forest Regression") scores_df = pd.DataFrame(rfr_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "random_forest" scores = pd.concat([scores, scores_df]) xgb = XGBRegressor() xgb_score = cross_validate( - xgb, - X=data_x, - y=data_y, - groups=data_groups, - cv=logo, - n_jobs=-1, - scoring=metrics + xgb, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics ) print("XGBoost Regressor") scores_df = pd.DataFrame(xgb_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "XGBoost" scores = pd.concat([scores, scores_df]) ada = ensemble.AdaBoostRegressor() ada_score = cross_validate( - ada, - X=data_x, - y=data_y, - groups=data_groups, - cv=logo, - n_jobs=-1, - scoring=metrics + ada, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics ) print("ADA Boost Regressor") scores_df = pd.DataFrame(ada_score)[test_metrics] - scores_df = scores_df.agg(['max', np.nanmedian]).transpose() + scores_df = scores_df.agg(["max", np.nanmedian]).transpose() scores_df["method"] = "ADA_boost" scores = pd.concat([scores, scores_df]) @@ -324,7 +316,7 @@ def run_all_regression_models(input_csv): def run_all_classification_models(data_x, data_y, data_groups, cv_method): - metrics = ['accuracy', 'average_precision', 'recall', 'f1'] + metrics = ["accuracy", "average_precision", "recall", "f1"] test_metrics = ["test_" + metric for metric in metrics] scores = pd.DataFrame(columns=["method", "max", "mean"]) @@ -332,127 +324,127 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method): dummy_class = DummyClassifier(strategy="most_frequent") dummy_score = cross_validate( - dummy_class, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - error_score='raise', - scoring=metrics + dummy_class, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + error_score="raise", + scoring=metrics, ) print("Dummy") scores_df = pd.DataFrame(dummy_score)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "Dummy" scores = pd.concat([scores, scores_df]) logistic_regression = linear_model.LogisticRegression() log_reg_scores = cross_validate( - logistic_regression, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - scoring=metrics + logistic_regression, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + scoring=metrics, ) print("Logistic regression") scores_df = pd.DataFrame(log_reg_scores)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "logistic_reg" scores = pd.concat([scores, scores_df]) svc = svm.SVC() svc_scores = cross_validate( - svc, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - scoring=metrics + svc, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + scoring=metrics, ) print("Support Vector Machine") scores_df = pd.DataFrame(svc_scores)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "svc" scores = pd.concat([scores, scores_df]) gaussian_nb = naive_bayes.GaussianNB() - + gaussian_nb_scores = cross_validate( - gaussian_nb, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - scoring=metrics + gaussian_nb, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + scoring=metrics, ) print("Gaussian Naive Bayes") scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "gaussian_naive_bayes" scores = pd.concat([scores, scores_df]) sgdc = linear_model.SGDClassifier() sgdc_scores = cross_validate( - sgdc, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - scoring=metrics + sgdc, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + scoring=metrics, ) print("Stochastic Gradient Descent") scores_df = pd.DataFrame(sgdc_scores)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "stochastic_gradient_descent" scores = pd.concat([scores, scores_df]) rfc = ensemble.RandomForestClassifier() rfc_scores = cross_validate( - rfc, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - scoring=metrics + rfc, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + scoring=metrics, ) print("Random Forest") scores_df = pd.DataFrame(rfc_scores)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "random_forest" scores = pd.concat([scores, scores_df]) xgb_classifier = XGBClassifier() xgb_scores = cross_validate( - xgb_classifier, - X=data_x, - y=data_y, - groups=data_groups, - cv=cv_method, - n_jobs=-1, - scoring=metrics + xgb_classifier, + X=data_x, + y=data_y, + groups=data_groups, + cv=cv_method, + n_jobs=-1, + scoring=metrics, ) print("XGBoost") scores_df = pd.DataFrame(xgb_scores)[test_metrics] - scores_df = scores_df.agg(['max', 'mean']).transpose() + scores_df = scores_df.agg(["max", "mean"]).transpose() scores_df["method"] = "xgboost" scores = pd.concat([scores, scores_df])