import os from datetime import datetime, timedelta from deepsense import neptune from torch.optim.lr_scheduler import ExponentialLR from steps.utils import get_logger from .utils import Averager, save_model from .validation import score_model logger = get_logger() class Callback: def __init__(self): self.epoch_id = None self.batch_id = None self.model = None self.optimizer = None self.loss_function = None self.output_names = None self.validation_datagen = None self.lr_scheduler = None def set_params(self, transformer, validation_datagen): self.model = transformer.model self.optimizer = transformer.optimizer self.loss_function = transformer.loss_function self.output_names = transformer.output_names self.validation_datagen = validation_datagen
import lightgbm as lgb import numpy as np import sklearn.linear_model as lr from attrdict import AttrDict from catboost import CatBoostClassifier from sklearn import ensemble from sklearn import svm from sklearn.externals import joblib from xgboost import XGBClassifier from steps.base import BaseTransformer from steps.utils import get_logger logger = get_logger() class SklearnClassifier(BaseTransformer): def __init__(self, estimator): self.estimator = estimator def fit(self, X, y, **kwargs): self.estimator.fit(X, y) return self def transform(self, X, y=None, **kwargs): prediction = self.estimator.predict_proba(X) return {'prediction': prediction} class SklearnRegressor(BaseTransformer): def __init__(self, estimator):