# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.14.4
#   kernelspec:
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# default_exp loss
# %load_ext lab_black
# nb_black if running in jupyter
# %load_ext autoreload
# automatically reload python modules if there are changes in the
# %autoreload 2

# hide
from nbdev.showdoc import *

# # Loss
#
# > Train and evaluate your algorithms on real data. You can also save the model for later use, or deploy it to production!
#

# ***input:*** clean and tidy dataset from data notebook + ML model class from hypotheses space notebook
#
# ***output:*** evaluated, trained and (optionally) deployed model
#
# ***description:***
#
# In this notebook you train and evaluate ML methods implemented with the whole dataset.
# You can also save the model for later use, or deploy it to production environment, or use this notebook as the final output.
#
# Edit this and the other text cells to describe your project.

# ## Import relevant modules

# +
# export
import numpy as np

# your code here
# -

# ## Define notebook parameters

# + tags=["parameters"]
# This cell is tagged with 'parameters'
seed = 0
# -

# make direct derivations from the parameters:

np.random.seed(seed)
# your code here

# ## Load clean and tidy dataset

# +
# your code here
# -

# > Note that depending on the file format and your variables, you might have to refefine datatypes in your dataframe!

# ## Split the data into training, testing and validation data

# +
# your code here
# -

# ## Train and evaluate the models

# +
# your code here
# -

# ## Visualize the results

# +
# your code here
# -

# You can also include statistical tests!

# +
# your code here
# -

# ## Validate model (if hyperparameters are optimized)

# +
# your code here
# -

# ## Visualize validation

# +
# your code here
# -

# ## Acceptance testing & deployment
#
# Is the model 'good enough' for production?
#
# Define criterion and tests for 'good enough' and automatic model save / deployment process.

# +
# your code here

# EXAMPLE: model.deploy() if val_loss > 0.85 else raise ModelValidationError
# -

# ## Conclusions
#
# How the results look like?

# ## Output of this notebook
#
# Saved or deployed trained model

# ## You can now move on to the workflow notebook!

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