Ap bundle performance #162
Conversation
…d mapping files for performance levels.
…riptors from template and destination in earthmover.
… render only exams taken.
… needed and fixed earthmover file.
* APBundle: Fixed Academic subject seed
* APBundle: Fixed if statement on score result to show irregularity code if available.
|
@jalvord1 here is the modified AP package with the pre-execute step. @jayckaiser tagging you as this has a pre-execute step. |
| - codeValue | ||
| - operation: add_columns | ||
| columns: | ||
| assessmentIdentifier: "{%raw%}AP - {{AP_Exam_Code}}{%endraw%}" |
There was a problem hiding this comment.
ideally all of this should live in an assessment.csv so it's easier to find and is consistent with other bundles
There was a problem hiding this comment.
Please change to this logic (we've tested this resolves issues we are running into): "{%raw-%} {%- if AwardType != 'nan' and AwardType != '' and AwardType is not none -%} {{'%02d' % AwardType | int}} {%-endif-%} {%-endraw%}"
| 'IrregularityCode1': f'Irregularity Code #1 {exam_id}', | ||
| 'IrregularityCode2': f'Irregularity Code #2 {exam_id}' | ||
| } | ||
| available_cols = [col for col in columns.values() if col in df_copy.columns] |
There was a problem hiding this comment.
I am a major proponent of the Return-Early design pattern in programming. I'd recommend changing the code below to as follows:
# [Comment about why 3 has been chosen as the threshold here]
if len(available_cols) < 3:
continue
subset = df_copy[id_columns + available_cols].copy()
rename_dict = {v: k for k, v in columns.items() if v in subset.columns}
subset.rename(columns=rename_dict, inplace=True)
subset.rename(columns={
'Student Identifier': 'studentId',
'Grade Level': 'GradeLevel',
'AI Code': 'SchoolCode'
}, inplace=True)
subset = subset[subset['studentId'].astype(str) != '']
subset['studentId'] = subset['studentId'].astype(str)
subset['ExamCode'] = subset['ExamCode'].astype(str)
unpivoted_exams.append(subset)
| 'AwardYear': f'Award Year {award_id}' | ||
| } | ||
| available_cols = [col for col in cols.values() if col in df_copy.columns] | ||
| if len(available_cols) == 2: |
There was a problem hiding this comment.
Same here regarding early-return.
| whenAssessedGradeLevelDescriptor: "{%raw-%} {{Grade_Namespace}}#{{Grade_CodeValue}}{%-endraw%}" | ||
| administrationDate: "{%raw-%} {{'05/01/'~ School_Year }}{%-endraw%}" | ||
| descriptorNamespace: ${DESCRIPTOR_NAMESPACE} | ||
| performanceLevel: "{%raw-%} {%- if AwardType != 'nan'-%} {{AwardType}} {%-endif-%} {%-endraw%}" |
There was a problem hiding this comment.
@Bruk can we edit this to pad with a 0 if the digit length is = 1? We are seeing a few failures because of this
4 participants
This is the APBundle with performance optimization. Otherwise, the output is identical.
@jayckaiser tagging you as there is a pre-execute step