#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 30 09:38:17 2019
Validates elements in a pandas DataFrame against its input data model. Output
is a boolean DataFrame
Validated elements are those with the following column_types:
- any in properties.numeric_types: range validation
- 'key': code table validation
- 'datetime': because of the way they are converted, read into datetime,
they should already be NaT if they not validate as a valid datetime. The
correspoding mask is just created for them
DEV notes:
need to add tolerance to the numeric range validation
@author: iregon
"""
import os
import pandas as pd
import numpy as np
import logging
from .. import properties
from ..data_models import code_tables
from ..data_models import schemas
def validate_numeric(elements,data,schema):
# Find thresholds in schema. Flag if not available -> warn
mask = pd.DataFrame(index = data.index, data = False, columns = elements)
lower = { x:schema.get(x).get('valid_min', -np.inf) for x in elements }
upper = { x:schema.get(x).get('valid_max', np.inf) for x in elements }
set_elements = [ x for x in lower.keys() if lower.get(x) != -np.inf and upper.get(x) != np.inf ]
if len([ x for x in elements if x not in set_elements ]) > 0:
logging.warning('Data numeric elements with missing upper or lower threshold: {}'.format(",".join([ str(x) for x in elements if x not in set_elements ])))
logging.warning('Corresponding upper and/or lower bounds set to +/-inf for validation')
mask[elements] = ((data[elements] >= [ lower.get(x) for x in elements ] ) & (data[elements] <= [ upper.get(x) for x in elements ])) | data[elements].isna()
return mask
def validate_codes(elements, data, code_tables_path, schema, supp = False):
mask = pd.DataFrame(index = data.index, data = False, columns = elements)
if os.path.isdir(code_tables_path):
for element in elements:
code_table = schema.get(element).get('codetable')
if not code_table:
logging.error('Code table not defined for element {}'.format(element))
logging.warning('Element mask set to False')
else:
code_table_path = os.path.join(code_tables_path, code_table + '.json')
# Eval elements: if ._yyyy, ._xxx in name: pd.DateTimeIndex().xxxx is the element to pass
# Additionally, on doing this, should make sure that element is a datetime type:
if os.path.isfile(code_table_path):
try:
table = code_tables.read_table(code_table_path)
if supp:
key_elements = [ element[1] ] if not table.get('_keys') else list(table['_keys'].get(element[1]))
else:
key_elements = [ element ] if not table.get('_keys') else list(table['_keys'].get(element))
if supp:
key_elements = [ (element[0],x) for x in key_elements ]
else:
key_elements = [ (properties.dummy_level,x) if not isinstance(x,tuple) else x for x in key_elements ]
dtypes = { x:properties.pandas_dtypes.get(schema.get(x).get('column_type')) for x in key_elements }
table_keys = code_tables.table_keys(table)
table_keys_str = [ "∿".join(x) if isinstance(x,list) else x for x in table_keys ]
validation_df = data[key_elements]
imask = pd.Series(index = data.index, data =True)
imask.iloc[np.where(validation_df.notna().all(axis = 1))[0]] = validation_df.iloc[np.where(validation_df.notna().all(axis = 1))[0],:].astype(dtypes).astype('str').apply("∿".join, axis=1).isin(table_keys_str)
mask[element] = imask
except Exception as e:
logging.error('Error validating coded element {}:'.format(element))
logging.error('Error is {}:'.format(e))
logging.warning('Element mask set to False')
else:
logging.error('Error validating coded element {}:'.format(element))
logging.error('Code table file {} not found'.format(code_table_path))
logging.warning('Element mask set to False')
continue
else:
logging.error('Code tables path {} not found'.format(code_tables_path))
logging.warning('All coded elements set to False')
return mask
def validate(data, mask0, schema, code_tables_path):
logging.basicConfig(format='%(levelname)s\t[%(asctime)s](%(filename)s)\t%(message)s',
level=logging.INFO,datefmt='%Y%m%d %H:%M:%S',filename=None)
# Check input
if not isinstance(data,pd.DataFrame) or not isinstance(mask0,pd.DataFrame):
logging.error('Input data and mask must be a pandas data frame object')
return
# Get the data elements from the input data: might be just a subset of
# data model and flatten the schema to get a simple and sequential list
# of elements included in the input data
elements = [ x for x in data ]
element_atts = schemas.df_schema(elements, schema)
# See what elements we need to validate
numeric_elements = [ x for x in elements if element_atts.get(x).get('column_type') in properties.numeric_types ]
datetime_elements = [ x for x in elements if element_atts.get(x).get('column_type') == 'datetime' ]
coded_elements = [ x for x in elements if element_atts.get(x).get('column_type') == 'key' ]
if any([isinstance(x,tuple) for x in numeric_elements + datetime_elements + coded_elements ]):
validated_columns = pd.MultiIndex.from_tuples(list(set(numeric_elements + coded_elements + datetime_elements)))
else:
validated_columns = list(set(numeric_elements + coded_elements + datetime_elements))
mask = pd.DataFrame(index = data.index, columns = data.columns)
# Validate elements by dtype:
# 1. Numeric elements
mask[numeric_elements] = validate_numeric(numeric_elements, data, element_atts)
# 2. Table coded elements
# See following: in multiple keys code tables, the non parameter element,
# won't have a code_table attribute in the element_atts:
# So we need to check the code_table.keys files in addition to the element_atts
# Additionally, a YEAR key can fail in one table, but be compliant with anbother, then, how would we mask this?
# also, a YEAR defined as an integer, will undergo its own check.....
# So I think we need to check nested keys as a whole, and mask only the actual parameterized element:
# Get the full list of keys combinations (tuples, triplets...) and check the column combination against that: if it fails, mark the element!
# Need to see how to grab the YEAR part of a datetime when YEAR comes from a datetime element
# pd.DatetimeIndex(df['_datetime']).year
if len(coded_elements)> 0:
mask[coded_elements] = validate_codes(coded_elements, data, code_tables_path, element_atts)
# 3. Datetime elements
# Those declared as such in element_atts
# Because of the way they are converted, read into datetime,
# they should already be NaT if they not validate as a valid datetime;
# let's check: hurray! they are!
mask[datetime_elements] = data[datetime_elements].notna()
mask[validated_columns] = mask[validated_columns].mask(mask0[validated_columns] == False, False)
return mask