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experiments.py
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experiments.py
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"""Exeperiments of XGBDistribution vs NGBRegressor and XGBRegressor
"""
import glob
import logging
import os
import shutil
import sys
import tempfile
import time
import zipfile
from argparse import ArgumentParser
from dataclasses import dataclass
from datetime import datetime
from functools import partial, wraps
from pathlib import Path
import numpy as np
import pandas as pd
import requests
import sqlalchemy as sa
from ngboost import NGBRegressor
from scipy.stats import norm
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold, train_test_split
from xgboost import XGBRegressor
from xgboost_distribution import XGBDistribution
_logger = logging.getLogger(__name__)
# -------------------------------------------------------------------------------------
# Datasets defintions
# -------------------------------------------------------------------------------------
DATASETS = {} # each instantiated dataset will be stored here, keyed by name
DATA_DIR = Path(__file__).parent.parent.absolute().joinpath("data")
@dataclass
class Dataset:
name: str
url: str
file_to_unpack: str = None # if url refers to zip, we mark the file to unpack
load_func: callable = pd.read_csv
processing_func: callable = None
def __post_init__(self):
DATASETS.update({self.name: self})
Dataset(
name="housing",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data", # noqa: E501
load_func=partial(pd.read_csv, header=None, delim_whitespace=True),
)
Dataset(
name="concrete",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/compressive/Concrete_Data.xls", # noqa: E501
load_func=pd.read_excel,
)
Dataset(
name="energy",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx", # noqa: E501
load_func=partial(pd.read_excel, usecols=[f"X{i}" for i in range(1, 9)] + ["Y1"]),
)
Dataset(
name="naval",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/00316/UCI%20CBM%20Dataset.zip", # noqa: E501
file_to_unpack="data.txt",
load_func=partial(pd.read_csv, header=None, delim_whitespace=True),
processing_func=lambda x: x.iloc[:, :-1],
)
Dataset(
name="power",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/00294/CCPP.zip",
file_to_unpack="Folds5x2_pp.xlsx",
load_func=pd.read_excel,
)
Dataset(
name="protein",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/00265/CASP.csv",
processing_func=lambda x: x.loc[
:, ["F1", "F2", "F3", "F4", "F5", "F6", "F7", "F8", "F9", "RMSD"]
],
)
Dataset(
name="wine",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv", # noqa: E501
load_func=partial(pd.read_csv, delimiter=";"),
)
Dataset(
name="yacht",
url="http://archive.ics.uci.edu/ml/machine-learning-databases/00243/yacht_hydrodynamics.data", # noqa: E501
load_func=partial(pd.read_csv, header=None, delim_whitespace=True),
)
Dataset(
name="msd",
url="https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip", # noqa: E501
file_to_unpack="YearPredictionMSD.txt",
load_func=pd.read_csv,
processing_func=lambda x: x.iloc[:, ::-1],
)
# -------------------------------------------------------------------------------------
# Datasets loading functions
# -------------------------------------------------------------------------------------
def load_dataset(name, data_dir=DATA_DIR):
dataset = DATASETS[name]
local_path = f"{data_dir}/{name}.data"
if os.path.exists(local_path):
_logger.info(f"Loading dataset from local {local_path}...")
return load_local_dataset(dataset, local_path)
else:
_logger.info("Dataset not locally cached, downloading from url...")
download_dataset(dataset, local_path)
return load_dataset(name)
def load_local_dataset(dataset, local_path):
df = dataset.load_func(local_path)
if dataset.processing_func:
df = dataset.processing_func(df)
return df.iloc[:, :-1].values, df.iloc[:, -1].values
def download_dataset(dataset, local_path):
r = requests.get(dataset.url)
if dataset.file_to_unpack is not None:
with tempfile.TemporaryFile() as fp:
fp.write(r.content)
unpack_file_from_zip(fp, dataset.file_to_unpack, local_path)
else:
with open(local_path, "wb") as f:
f.write(r.content)
_logger.info(f"Downloaded {dataset.name} to {local_path}")
def unpack_file_from_zip(zip_file, to_unpack, path):
with zipfile.ZipFile(zip_file, "r") as inzipfile:
with tempfile.TemporaryDirectory() as temp_dir:
inzipfile.extractall(path=temp_dir)
data_file = glob.glob(f"{temp_dir}/**/{to_unpack}", recursive=True)[0]
shutil.copyfile(data_file, path)
# -------------------------------------------------------------------------------------
# Metrics to evaluate
# -------------------------------------------------------------------------------------
def root_mean_squared_error(y_pred, y_test):
return np.sqrt(mean_squared_error(y_pred, y_test))
def normal_nll(loc, scale, y_test):
return -norm.logpdf(y_test.flatten(), loc=loc, scale=scale).mean()
@dataclass
class EvalResult:
total_time: float
rmse: float
nll: float
# -------------------------------------------------------------------------------------
# Evaluate decorator, which evaluates the pattern function(split_data) -> predictions
# -------------------------------------------------------------------------------------
@dataclass
class SplitData:
X_train: np.ndarray
y_train: np.ndarray
X_val: np.ndarray
y_val: np.ndarray
X_test: np.ndarray
y_test: np.ndarray
evaluations = []
def evaluate(evaluation_func):
@wraps(evaluation_func)
def measured(data):
t0 = time.perf_counter()
preds = evaluation_func(data)
elapsed = time.perf_counter() - t0
if isinstance(preds, np.ndarray):
rmse = root_mean_squared_error(preds, data.y_test)
nll = None
else:
rmse = root_mean_squared_error(preds.loc, data.y_test)
nll = normal_nll(preds.loc, preds.scale, data.y_test)
return EvalResult(rmse=rmse, nll=nll, total_time=elapsed)
evaluations.append(measured)
return measured
# -------------------------------------------------------------------------------------
# Model functions, everything decorated by @evaluate will be evaluated
# -------------------------------------------------------------------------------------
@evaluate
def ngb_regressor(data):
ngb = NGBRegressor(verbose=False)
ngb.fit(
data.X_train,
data.y_train,
X_val=data.X_val,
Y_val=data.y_val,
early_stopping_rounds=10,
)
return ngb.pred_dist(data.X_test, max_iter=ngb.best_val_loss_itr)
@evaluate
def xgb_distribution(data):
xgbd = XGBDistribution(max_depth=3, natural_gradient=True, n_estimators=500)
xgbd.fit(
data.X_train,
data.y_train,
eval_set=[(data.X_val, data.y_val)],
early_stopping_rounds=10,
verbose=False,
)
return xgbd.predict(data.X_test)
@evaluate
def xgb_regressor(data):
xgb = XGBRegressor(max_depth=3, n_estimators=500)
xgb.fit(
data.X_train,
data.y_train,
eval_set=[(data.X_val, data.y_val)],
early_stopping_rounds=10,
verbose=False,
)
return xgb.predict(data.X_test)
# -----------------------------------------------------------------------------
# Functions for running cross-validation becnhmark experiment
# -----------------------------------------------------------------------------
def parse_args():
argparser = ArgumentParser()
argparser.add_argument("--data-dir", type=str, default=DATA_DIR)
argparser.add_argument("--dataset", type=str, default="concrete")
argparser.add_argument("--random-seed", type=int, default=42)
argparser.add_argument("--n-folds", type=int, default=10)
argparser.add_argument("--db-name", type=str, default="results.db")
return argparser.parse_args()
def run_experiment():
args = parse_args()
setup_logging()
_logger.info(f"Storing datasets and results in {args.data_dir}")
os.makedirs(args.data_dir, exist_ok=True)
db = DataBase(data_dir=args.data_dir, db_name=args.db_name)
_logger.info("Connected to database")
X, y = load_dataset(args.dataset, data_dir=args.data_dir)
_logger.info(f"Loaded dataset: `{args.dataset}`, X.shape={X.shape}")
_logger.info(f"Setting random seed to {args.random_seed}")
np.random.seed(args.random_seed)
kf = KFold(n_splits=args.n_folds, shuffle=True, random_state=args.random_seed)
_logger.info(f"Cross-validation with {args.n_folds} folds...")
results = _cross_validate_evaluations(kf, X, y, args)
_logger.info("Inserting results into database...")
for eval_func, result in zip(evaluations, results):
df_metrics = aggregate_results(result)
df_metrics["dataset"] = args.dataset
df_metrics["model"] = eval_func.__name__
db.insert_metrics_pdf(df_metrics)
df_metrics = db.get_metrics_pdf()
df_summary = summarize_metrics(df_metrics)
_logger.info(f"\n{df_summary}")
def _cross_validate_evaluations(kfold, X, y, args):
results = [list() for eval in evaluations]
for ii, (train_index, test_index) in enumerate(kfold.split(X)):
_logger.info(f"Fold {ii+1} / {args.n_folds}...")
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
X_train, X_val, y_train, y_val = train_test_split(
X_train, y_train, test_size=0.1, random_state=args.random_seed
)
split_data = SplitData(
X_train=X_train,
y_train=y_train,
X_val=X_val,
y_val=y_val,
X_test=X_test,
y_test=y_test,
)
for eval_func, result in zip(evaluations, results):
result.append(eval_func(split_data))
return results
def aggregate_results(results):
return (
pd.DataFrame(results)
.agg(["mean", "std"], axis=0)
.melt(ignore_index=False, var_name="metric")
.rename_axis("agg_func")
.reset_index()
)
def summarize_metrics(df_metrics):
return df_metrics.pivot_table(
values=["value"],
index=["dataset", "agg_func"],
columns=["model", "metric"],
)
# -------------------------------------------------------------------------------------
# Database for storing results of each experiment run
# -------------------------------------------------------------------------------------
class DataBase:
def __init__(self, data_dir=DATA_DIR, db_name="results.db"):
_logger.info(f"Using SQLite database at {data_dir}/{db_name}")
self.metadata = sa.MetaData()
self.engine = sa.create_engine(f"sqlite:///{data_dir}/{db_name}")
self.connection = self.engine.connect()
self._create_tables()
def _create_tables(self):
self.metrics = sa.Table(
"metrics",
self.metadata,
sa.Column("dataset", sa.String(), nullable=False),
sa.Column("model", sa.String(), nullable=False),
sa.Column("metric", sa.String(), nullable=False),
sa.Column("agg_func", sa.String(), nullable=False),
sa.Column("value", sa.Float(), nullable=True),
sa.Column("created_on", sa.DateTime(), default=datetime.now),
)
self.metadata.create_all(self.engine)
def insert_records(self, records, table="metrics"):
ins = getattr(self, table).insert()
self.connection.execute(ins, records)
def insert_metrics_pdf(self, df):
self.insert_records(df.to_dict("records"), table="metrics")
def get_metrics_pdf(self):
return pd.read_sql(
sa.select(self.metrics).order_by(self.metrics.c.created_on.desc()),
self.connection,
)
def setup_logging(loglevel=logging.INFO):
logformat = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s"
logging.basicConfig(
level=loglevel, stream=sys.stdout, format=logformat, datefmt="%Y-%m-%d %H:%M:%S"
)
if __name__ == "__main__":
run_experiment()