demo12.py

#!/usr/bin/env python
# coding: utf-8

import numpy as np
import pandas as pd

from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split

from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task
from lightautoml.validation.np_iterators import TimeSeriesIterator


################################
# Features:
# - working with np.arrays
# - working with file
# - custom time series split
# - parallel/batch inference
################################


np.random.seed(42)

data = pd.read_csv("./data/sampled_app_train.csv")

data["BIRTH_DATE"] = (np.datetime64("2018-01-01") + data["DAYS_BIRTH"].astype(np.dtype("timedelta64[D]"))).astype(str)
data["EMP_DATE"] = (
    np.datetime64("2018-01-01") + np.clip(data["DAYS_EMPLOYED"], None, 0).astype(np.dtype("timedelta64[D]"))
).astype(str)

data["report_dt"] = np.datetime64("2018-01-01")

data["constant"] = 1
data["allnan"] = np.nan

data.drop(["DAYS_BIRTH", "DAYS_EMPLOYED"], axis=1, inplace=True)

train, test = train_test_split(data, test_size=2000, random_state=42)
# create time series iterator that is passed as cv_func
cv_iter = TimeSeriesIterator(train["EMP_DATE"].astype(np.datetime64), n_splits=5, sorted_kfold=False)

# train dataset may be passed as dict of np.ndarray
train = {
    "data": train[["AMT_CREDIT", "AMT_ANNUITY"]].values,
    "target": train["TARGET"].values,
}

task = Task(
    "binary",
)

automl = TabularAutoML(
    task=task,
    timeout=200,
)
oof_pred = automl.fit_predict(train, train_features=["AMT_CREDIT", "AMT_ANNUITY"], cv_iter=cv_iter)
# prediction can be made on file by
test.to_csv("temp_test_data.csv", index=False)
test_pred = automl.predict("temp_test_data.csv", batch_size=100, n_jobs=4)

print("Check scores...")
oof_prediction = oof_pred.data[:, 0]
not_empty = np.logical_not(np.isnan(oof_prediction))

print(f'OOF score: {roc_auc_score(train["target"][not_empty], oof_prediction[not_empty])}')
print(f'TEST score: {roc_auc_score(test["TARGET"].values, test_pred.data[:, 0])}')