Add xgboost to dependencies and reformat helper.py.
junos
parent
59552c18a9
commit
583ee82e80
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@ -23,3 +23,4 @@ dependencies:
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- sqlalchemy
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- statsmodels
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- tabulate
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- xgboost
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@ -1,15 +1,18 @@
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from pathlib import Path
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from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble, naive_bayes, neighbors, tree
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate, 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, DummyClassifier
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from xgboost import XGBRegressor, XGBClassifier
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import xgboost as xg
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import pandas as pd
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import numpy as np
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import pandas as pd
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from sklearn import (
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ensemble,
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gaussian_process,
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kernel_ridge,
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linear_model,
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naive_bayes,
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svm,
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)
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from sklearn.dummy import DummyClassifier, DummyRegressor
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from sklearn.model_selection import LeaveOneGroupOut, cross_validate
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from xgboost import XGBClassifier, XGBRegressor
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def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
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@ -65,20 +68,38 @@ def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> P
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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_regression_model_input(input_csv):
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def prepare_regression_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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index_columns = [
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"local_segment",
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"local_segment_label",
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"local_segment_start_datetime",
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"local_segment_end_datetime",
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]
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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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data_x, data_y, data_groups = (
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model_input.drop(["target", "pid"], axis=1),
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model_input["target"],
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model_input["pid"],
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)
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categorical_feature_colnames = ["gender", "startlanguage", "limesurvey_demand_control_ratio_quartile"]
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additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
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categorical_feature_colnames = [
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"gender",
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"startlanguage",
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"limesurvey_demand_control_ratio_quartile",
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]
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additional_categorical_features = [
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col
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for col in data_x.columns
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if "mostcommonactivity" in col or "homelabel" in col
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]
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categorical_feature_colnames += additional_categorical_features
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# TODO: check whether limesurvey_demand_control_ratio_quartile NaNs could be replaced meaningfully
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categorical_features = data_x[categorical_feature_colnames].copy()
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@ -86,7 +107,9 @@ def prepare_regression_model_input(input_csv):
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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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categorical_features = categorical_features.apply(
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lambda col: col.astype("category")
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)
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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@ -108,7 +131,7 @@ def run_all_regression_models(input_csv):
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data_y,
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groups=data_groups,
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)
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metrics = ['r2', 'neg_mean_absolute_error', 'neg_root_mean_squared_error']
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metrics = ["r2", "neg_mean_absolute_error", "neg_root_mean_squared_error"]
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test_metrics = ["test_" + metric for metric in metrics]
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scores = pd.DataFrame(columns=["method", "max", "nanmedian"])
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@ -121,13 +144,13 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("Dummy model:")
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print("R^2: ", np.nanmedian(dummy_regr_scores['test_r2']))
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print("R^2: ", np.nanmedian(dummy_regr_scores["test_r2"]))
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scores_df = pd.DataFrame(dummy_regr_scores)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "dummy"
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scores = pd.concat([scores, scores_df])
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@ -139,17 +162,17 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("Linear regression:")
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print("R^2: ", np.nanmedian(lin_reg_scores['test_r2']))
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print("R^2: ", np.nanmedian(lin_reg_scores["test_r2"]))
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scores_df = pd.DataFrame(lin_reg_scores)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "linear_reg"
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scores = pd.concat([scores, scores_df])
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ridge_reg = linear_model.Ridge(alpha=.5)
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ridge_reg = linear_model.Ridge(alpha=0.5)
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ridge_reg_scores = cross_validate(
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ridge_reg,
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X=data_x,
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@ -157,16 +180,15 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("Ridge regression")
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scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "ridge_reg"
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scores = pd.concat([scores, scores_df])
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lasso_reg = linear_model.Lasso(alpha=0.1)
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lasso_reg_score = cross_validate(
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lasso_reg,
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@ -175,12 +197,12 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("Lasso regression")
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scores_df = pd.DataFrame(lasso_reg_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "lasso_reg"
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scores = pd.concat([scores, scores_df])
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@ -192,12 +214,12 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("Bayesian Ridge")
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scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "bayesian_ridge"
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scores = pd.concat([scores, scores_df])
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@ -209,29 +231,23 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("RANSAC (outlier robust regression)")
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scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "RANSAC"
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scores = pd.concat([scores, scores_df])
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svr = svm.SVR()
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svr_score = cross_validate(
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svr,
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X=data_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=metrics
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svr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
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)
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print("Support vector regression")
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scores_df = pd.DataFrame(svr_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "SVR"
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scores = pd.concat([scores, scores_df])
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@ -243,80 +259,56 @@ def run_all_regression_models(input_csv):
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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=metrics
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scoring=metrics,
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)
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print("Kernel Ridge regression")
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scores_df = pd.DataFrame(kridge_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "kernel_ridge"
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scores = pd.concat([scores, scores_df])
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gpr = gaussian_process.GaussianProcessRegressor()
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gpr_score = cross_validate(
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gpr,
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X=data_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=metrics
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gpr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
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)
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print("Gaussian Process Regression")
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scores_df = pd.DataFrame(gpr_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "gaussian_proc"
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scores = pd.concat([scores, scores_df])
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rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
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rfr_score = cross_validate(
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rfr,
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X=data_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=metrics
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rfr, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
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)
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print("Random Forest Regression")
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scores_df = pd.DataFrame(rfr_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "random_forest"
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scores = pd.concat([scores, scores_df])
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xgb = XGBRegressor()
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xgb_score = cross_validate(
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xgb,
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X=data_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=metrics
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xgb, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
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)
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print("XGBoost Regressor")
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scores_df = pd.DataFrame(xgb_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "XGBoost"
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scores = pd.concat([scores, scores_df])
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ada = ensemble.AdaBoostRegressor()
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ada_score = cross_validate(
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ada,
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X=data_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=metrics
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ada, X=data_x, y=data_y, groups=data_groups, cv=logo, n_jobs=-1, scoring=metrics
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)
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print("ADA Boost Regressor")
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scores_df = pd.DataFrame(ada_score)[test_metrics]
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scores_df = scores_df.agg(['max', np.nanmedian]).transpose()
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scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
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scores_df["method"] = "ADA_boost"
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scores = pd.concat([scores, scores_df])
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@ -324,7 +316,7 @@ def run_all_regression_models(input_csv):
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def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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metrics = ['accuracy', 'average_precision', 'recall', 'f1']
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metrics = ["accuracy", "average_precision", "recall", "f1"]
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test_metrics = ["test_" + metric for metric in metrics]
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scores = pd.DataFrame(columns=["method", "max", "mean"])
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@ -338,13 +330,13 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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error_score='raise',
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scoring=metrics
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error_score="raise",
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scoring=metrics,
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)
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print("Dummy")
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scores_df = pd.DataFrame(dummy_score)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "Dummy"
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scores = pd.concat([scores, scores_df])
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@ -357,12 +349,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=metrics
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scoring=metrics,
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)
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print("Logistic regression")
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scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "logistic_reg"
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scores = pd.concat([scores, scores_df])
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@ -375,12 +367,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=metrics
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scoring=metrics,
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)
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print("Support Vector Machine")
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scores_df = pd.DataFrame(svc_scores)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "svc"
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scores = pd.concat([scores, scores_df])
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@ -393,12 +385,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=metrics
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scoring=metrics,
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)
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print("Gaussian Naive Bayes")
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scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "gaussian_naive_bayes"
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scores = pd.concat([scores, scores_df])
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@ -411,12 +403,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=metrics
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scoring=metrics,
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)
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print("Stochastic Gradient Descent")
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scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "stochastic_gradient_descent"
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scores = pd.concat([scores, scores_df])
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@ -429,12 +421,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=metrics
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scoring=metrics,
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)
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print("Random Forest")
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scores_df = pd.DataFrame(rfc_scores)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "random_forest"
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scores = pd.concat([scores, scores_df])
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@ -447,12 +439,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
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groups=data_groups,
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cv=cv_method,
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n_jobs=-1,
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scoring=metrics
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scoring=metrics,
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)
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print("XGBoost")
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scores_df = pd.DataFrame(xgb_scores)[test_metrics]
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scores_df = scores_df.agg(['max', 'mean']).transpose()
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scores_df = scores_df.agg(["max", "mean"]).transpose()
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scores_df["method"] = "xgboost"
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scores = pd.concat([scores, scores_df])
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