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

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

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# 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

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

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

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"""Checks if a set of TFRecords appear to be valid.

Specifically, this checks whether the provided record sizes are consistent and
that the file does not end in the middle of a record. It does not verify the
CRCs.
"""
import struct
import tensorflow as tf

from tensorflow import app

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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,

mindis

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"""
    

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"""
(C) August 2013, Mathieu Blondel
# License: BSD 3 clause

This is a Numba-based reimplementation of the block coordinate descent solver
(without line search) described in the paper:

    Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass
    Classification.  Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara.
    Machine Learning, May 2013.

mindis

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

mindis

# -*- coding: utf-8 -*-
"""
Created on Sun Oct 22 10:31:23 2017

@author: SF
"""

import numpy as np
from keras.layers import Input
from keras.models import Model

mindis

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential