"""Recipe for Kaggle Competition: Amazon.com - Employee Access Challenge"""
import datatable as dt
import numpy as np
from sklearn.preprocessing import LabelEncoder
from h2oaicore.models import CustomModel, BaseCustomModel, LightGBMModel
from h2oaicore.transformer_utils import CustomTransformer
from h2oaicore.systemutils import config, physical_cores_count, user_dir
# https://www.kaggle.com/c/amazon-employee-access-challenge
# Data: https://www.kaggle.com/c/amazon-employee-access-challenge/data
# # Run DAI with 5/2/1 settings, AUC scorer
# Recommended: Include only LIGHTGBMDEEP and CATBOOST (in expert settings -> custom recipes -> include models).
class MyLightGBMDeep(BaseCustomModel, LightGBMModel):
_binary = True
_display_name = "MYLGBMDEEP"
_description = "LightGBM with more depth"
_included_transformers = ['NumToCatWoETransformer', 'CVTargetEncodeTransformer']
def set_default_params(self,
accuracy=None, time_tolerance=None, interpretability=None,
**kwargs):
# First call the parent set_default_params
LightGBMModel.set_default_params(
self,
accuracy=accuracy,
time_tolerance=time_tolerance,
interpretability=interpretability,
**kwargs
)
# Then modify the parameters
self.params["grow_policy"] = "lossguide"
self.params["max_leaves"] = 8192
self.params["max_depth"] = -1
class CatBoostModel(CustomModel):
_can_handle_non_numeric = True
_binary = True
_display_name = "CatBoost"
_description = "Yandex CatBoost GBM"
_modules_needed_by_name = ['catboost']
_included_transformers = ['MyToStringTransformer', 'CVTargetEncodeTransformer']
def fit(self, X, y, sample_weight=None, eval_set=None, sample_weight_eval_set=None, **kwargs):
lb = LabelEncoder()
lb.fit(self.labels)
y = lb.transform(y)
orig_cols = list(X.names)
XX = X.to_pandas()
params = {
'train_dir': user_dir(),
'allow_writing_files': False,
'thread_count': 10,
# 'loss_function': 'Logloss'
}
from catboost import CatBoostClassifier
model = CatBoostClassifier(**params)
model.fit(XX, y=y, sample_weight=sample_weight, verbose=False,
cat_features=list(X[:, [str, int]].names)) # Amazon specific, also no early stopping
# must always set best_iterations
self.set_model_properties(model=model,
features=orig_cols,
importances=model.feature_importances_,
iterations=0)
def predict(self, X, **kwargs):
model, features, importances, iterations = self.get_model_properties()
X = X.to_pandas()
kwargs['ntree_limit'] = iterations - 1
preds = model.predict_proba(X, thread_count=10)
if preds.shape[1] == 2:
return preds[:, 1]
else:
return preds
# Not necessary, but nice to demonstrate creation of string input for CatBoost
class MyToStringTransformer(CustomTransformer):
_numeric_output = False
_included_model_classes = ['CatBoostModel']
@property
def display_name(self):
return "Str"
@staticmethod
def get_default_properties():
return dict(col_type="numeric", min_cols=1, max_cols=1, relative_importance=1)
def fit_transform(self, X: dt.Frame, y: np.array = None):
return self.transform(X)
def transform(self, X: dt.Frame):
return X[:, dt.stype.str32(dt.f[0])]
