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"""Anomaly score for each row based on reconstruction error of an H2O-3 deep learning autoencoder"""
from h2oaicore.transformer_utils import CustomTransformer
import datatable as dt
import numpy as np
import os
import h2o
import uuid
from h2oaicore.systemutils import user_dir, config, remove
from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
class MyH2OAutoEncoderAnomalyTransformer(CustomTransformer):
_testing_can_skip_failure = False # ensure tested as if shouldn't fail
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.id = None
self.raw_model_bytes = None
@staticmethod
def get_default_properties():
return dict(col_type="numcat", min_cols=2, max_cols=10, relative_importance=1)
def fit_transform(self, X: dt.Frame, y: np.array = None):
h2o.init(port=config.h2o_recipes_port)
model = H2OAutoEncoderEstimator(activation='tanh', epochs=1, hidden=[50, 50], reproducible=True, seed=1234)
frame = h2o.H2OFrame(X.to_pandas())
model_path = None
try:
model.train(x=list(range(X.ncols)), training_frame=frame)
self.id = model.model_id
model_path = os.path.join(user_dir(), "h2o_model." + str(uuid.uuid4()))
model_path = h2o.save_model(model=model, path=model_path)
with open(model_path, "rb") as f:
self.raw_model_bytes = f.read()
return model.anomaly(frame).as_data_frame(header=False)
finally:
if model_path is not None:
remove(model_path)
h2o.remove(model)
def transform(self, X: dt.Frame):
h2o.init(port=config.h2o_recipes_port)
model_path = os.path.join(user_dir(), self.id)
model_file = os.path.join(model_path, "h2o_model." + str(uuid.uuid4()) + ".bin")
os.makedirs(model_path, exist_ok=True)
with open(model_file, "wb") as f:
f.write(self.raw_model_bytes)
model = h2o.load_model(os.path.abspath(model_file))
frame = h2o.H2OFrame(X.to_pandas())
anomaly_frame = None
try:
anomaly_frame = model.anomaly(frame)
anomaly_frame_df = anomaly_frame.as_data_frame(header=False)
return anomaly_frame_df
finally:
remove(model_file)
h2o.remove(self.id)
h2o.remove(anomaly_frame)