twiecki

eggie5

import pandas as pd
import tensorflow as tf
import dask.dataframe as dd
from dask.distributed import Client, LocalCluster
from tensorflow.python.saved_model import loader


def encode_factory(sess, export_path:str):
    """Loads TF SavedModel and returns a callable"""
    

orhermansaffar

# How to use TimeBasedCV
data_for_modeling=pd.read_csv('data.csv', parse_dates=['record_date'])
tscv = TimeBasedCV(train_period=30,
                   test_period=7,
                   freq='days')
for train_index, test_index in tscv.split(data_for_modeling,
                   validation_split_date=datetime.date(2019,2,1), date_column='record_date'):
    print(train_index, test_index)

# get number of splits

orhermansaffar

import pandas as pd
import datetime
from datetime import datetime as dt
from dateutil.relativedelta import *

class TimeBasedCV(object):
    '''
    Parameters 
    ----------
    train_period: int

c-bata

import numpy as np
import pandas as pd

#import lightgbm as lgb
from optuna.integration import lightgbm as lgb

from sklearn.model_selection import train_test_split
from sklearn.utils import check_X_y, safe_sqr
from sklearn.feature_selection.base import SelectorMixin
from lightgbm import Booster

eggie5

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.eager import context

def cyclic_learning_rate(global_step,
                         learning_rate=0.01,
                         max_lr=0.1,
                         step_size=20.,
                         gamma=0.99994,

piyueh

#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2019 Pi-Yueh Chuang <pychuang@gwu.edu>
#
# Distributed under terms of the MIT license.

"""An example of using tfp.optimizer.lbfgs_minimize to optimize a TensorFlow model.

baogorek

import pandas as pd
import numpy as np
import patsy

import tensorflow as tf

class SleepReg(tf.Module):
    def __init__(self, sleepdata_path):
        """Initializing tensorflow variables and other necessary matrices"""
        # These two TensorFlow variables show up in the trainable_variables

wjohnson

{
  "type" : "record",
  "name" : "TrainingExample",
  "namespace" : "com.linkedin.metronome.avro.generated",
  "fields" : [ {
    "name" : "uid",
    "type" : [ "null", "string", "long", "int" ],
    "doc" : "a unique id for the training event",
    "default" : null
  }, {

tgsmith61591

# -*- coding: utf-8 -*-
#
# Author: Taylor G Smith
#
# Recommender system ranking metrics derived from Spark source for use with
# Python-based recommender libraries (i.e., implicit, 
# http://github.com/benfred/implicit/). These metrics are derived from the
# original Spark Scala source code for recommender metrics.
# https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala