# test integrity of the input data

"""

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​

THE SOFTWARE CODE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS

OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

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"""

import os

import numpy as np

import pandas as pd

# get absolute path of csv files from data folder

def get_absPath(filename):

"""Returns the path of the notebooks folder"""

path = os.path.abspath(

os.path.join(

os.path.dirname(__file__), os.path.pardir, os.path.pardir, "data", filename

)

)

return path

# number of features

expected_columns = 10

# distribution of features in the training set

historical_mean = np.array(

[

-3.63962254e-16,

1.26972339e-16,

-8.01646331e-16,

1.28856202e-16,

-8.99230414e-17,

1.29609747e-16,

-4.56397112e-16,

3.87573332e-16,

-3.84559152e-16,

-3.39848813e-16,

1.52133484e02,

]

)

historical_std = np.array(

[

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

4.75651494e-02,

7.70057459e01,

]

)

# maximal relative change in feature mean or standrd deviation

# that we can tolerate

shift_tolerance = 3

def test_check_schema():

datafile = get_absPath("diabetes.csv")

# check that file exists

assert os.path.exists(datafile)

dataset = pd.read_csv(datafile)

header = dataset[dataset.columns[:-1]]

actual_columns = header.shape[1]

# check header has expected number of columns

assert actual_columns == expected_columns

def test_check_bad_schema():

datafile = get_absPath("diabetes_bad_schema.csv")

# check that file exists

assert os.path.exists(datafile)

dataset = pd.read_csv(datafile)

header = dataset[dataset.columns[:-1]]

actual_columns = header.shape[1]

# check header has expected number of columns

assert actual_columns != expected_columns

def test_check_missing_values():

datafile = get_absPath("diabetes_missing_values.csv")

# check that file exists

assert os.path.exists(datafile)

dataset = pd.read_csv(datafile)

n_nan = np.sum(np.isnan(dataset.values))

assert n_nan > 0

def test_check_distribution():

datafile = get_absPath("diabetes_bad_dist.csv")

# check that file exists

assert os.path.exists(datafile)

dataset = pd.read_csv(datafile)

mean = np.mean(dataset.values, axis=0)

std = np.mean(dataset.values, axis=0)

assert (

np.sum(abs(mean - historical_mean) > shift_tolerance * abs(historical_mean))

or np.sum(abs(std - historical_std) > shift_tolerance * abs(historical_std)) > 0

)