Add xgboost to dependencies and reformat helper.py.
parent
59552c18a9
commit
583ee82e80
|
@ -23,3 +23,4 @@ dependencies:
|
|||
- sqlalchemy
|
||||
- statsmodels
|
||||
- tabulate
|
||||
- xgboost
|
|
@ -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:
|
||||
|
@ -65,20 +68,38 @@ 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
|
||||
categorical_features = data_x[categorical_feature_colnames].copy()
|
||||
|
@ -86,7 +107,9 @@ def prepare_regression_model_input(input_csv):
|
|||
# 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"])
|
||||
|
@ -338,13 +330,13 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
error_score='raise',
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
@ -357,12 +349,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
@ -375,12 +367,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
@ -393,12 +385,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
@ -411,12 +403,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
@ -429,12 +421,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
@ -447,12 +439,12 @@ def run_all_classification_models(data_x, data_y, data_groups, cv_method):
|
|||
groups=data_groups,
|
||||
cv=cv_method,
|
||||
n_jobs=-1,
|
||||
scoring=metrics
|
||||
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])
|
||||
|
||||
|
|
Loading…
Reference in New Issue