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):
