Understand experiments · Lesson 06
Inspect the fraud notebook before training
The recording reviews the committed synthetic fraud notebook: a random forest, a held-out split, three metrics and a saved prediction model. It asks what can be established before training, then tests two different kinds of file change.
By the end of this lesson
- Explain why a recognized AUC call has no observed score during inspection.
- Distinguish a CSV path from its content identity.
- Separate raw notebook bytes from the identity of scientific signals.
Watch the lesson
English · 5:54 · Recorded with Apizr 0.4.6 development candidate.
Chapters in this recording
| Time | Chapter |
|---|---|
| 00:00 | A notebook arrives for review |
| 00:30 | From source to a review decision |
| 00:56 | Read the experiment, not a shell script |
| 01:47 | Inspect once; read the eight evidence states |
| 02:37 | Read each state in its evidence context |
| 03:07 | Same filename; different data |
| 03:58 | Variant: notebook bytes versus scientific signals |
| 04:53 | Your turn: can we report an AUC? |
| 05:13 | Answer: evidence determines the next action |
| 05:43 | Continue with the written guide |
Read the recording transcript and commands
This is the retained English narration, in the original chapter order. Commands keep the recorded version and prerequisites; setup additions in the lesson are labelled separately.
00:00 — A notebook arrives for review
A colleague sends you a fraud detection notebook and asks whether its result is ready to share. Before spending time on another training run, what can you establish from the notebook itself? We will inspect the real example in this repository: synthetic transactions, a random forest, a held-out test split, three metrics and a saved prediction model. By the end, you will distinguish a source finding from an observed result and explain one missing piece of evidence.
00:30 — From source to a review decision
Imagine that the notebook names a ROC AUC calculation and fixes random state to forty two. Does reading that source establish an AUC value? Does the seed establish that the experiment has been reproduced? Keep your answers in mind. Our question is practical: which facts can we use for review now, and which facts require a separately chosen execution?
00:56 — Read the experiment, not a shell script
Start with the scientific workflow rather than the command list. The active CSV is the training input. The split reserves a quarter of the rows for evaluation. Twenty four trees and maximum depth five are the visible hyperparameters. Both the split and the forest name a seed. The main guard separates training from prediction. Notice that these are statements in source; we have not loaded the CSV into pandas or fitted a model.
python lab.py source settings
test ! -e artifacts/model.joblib && echo "No trained model in this fresh copy"
01:47 — Inspect once; read the eight evidence states
We now ask Apizr to inspect the saved notebook and retain its JSON report. The second command is a teaching projection of that actual file; the complete report remains on disk. Read the execution line first: not executed. Parameters can be captured as static candidates while metrics and outputs remain partial. Captured does not mean scientifically verified. The data section can also remain partial because the model loader contains a dynamic path. A known CSV and an unresolved reference can coexist.
apizr experiment inspect fraud_detection.ipynb --format json > inspection-a.json
python lab.py inspection inspection-a.json
02:37 — Read each state in its evidence context
Use three different questions. For data, ask which exact bytes were identified. For parameters, ask whether the value came from source or from execution. For a metric, ask whether we found a calculation or actually observed its value. This prevents a common mistake: treating a visually complete report as an experiment that has already run. The source location is your way back to the notebook when a finding needs explanation.
03:07 — Same filename; different data
We replace only the active CSV with the second committed synthetic cohort and inspect again. Before reading the comparison, predict which facts should change. The notebook and its hyperparameters have not been edited. The path is still data slash transactions dot CSV, but its bytes differ because the synthetic label rule differs. The new input digest should expose that change. Neither inspection creates a model or an observed AUC. A familiar filename is not a data version.
cp data/transactions-b.csv data/transactions.csv
apizr experiment inspect fraud_detection.ipynb --format json > inspection-b.json
python lab.py inspect-change
03:58 — Variant: notebook bytes versus scientific signals
For an advanced review, distinguish raw notebook identity from the identity of its scientific signals. We deliberately change stored execution counts and presentation metadata without changing any executable cell text. These are fixture edits, not kernel execution. Inspect again and compare both digests. Raw bytes should differ, while the signal digest should remain stable. This lets you explain why a notebook file change need not be a changed scientific signal. Neither identity establishes that the cell actually executed.
python lab.py presentation
apizr experiment inspect fraud_detection.ipynb --format json > presentation.json
python lab.py signal-check
04:53 — Your turn: can we report an AUC?
Pause the lesson. A colleague sees captured parameters, a seed and a recognized AUC call, then writes: the model achieved a reproducible score. Identify two unsupported parts of that sentence. Then name the next operation you would choose, and the environment you would need.
05:13 — Answer: evidence determines the next action
No AUC value has been observed, because inspection never executed the notebook. A declared seed also does not establish a reproduced outcome across environments. If we trust the notebook, the next step is an explicit experiment Run in the Python environment containing its scientific dependencies. Keep the inspection as a review starting point. We will use that distinction in the next lesson when a Plan becomes a retained Run.
05:43 — Continue with the written guide
The current written guide remains authoritative. Continue there for the detailed steps. Thank you for following Apizr Academy.
1. Prepare the same case
Download the recorded VI–XII source materials. For each fraud lesson, start with a fresh copy of its fraud-detection/ directory and restore data/transactions-a.csv as data/transactions.csv. The archive includes the actual notebook, both synthetic cohorts and lab.py from the video.
Use the 0.4.6 development environment. Inspection reads notebook source; it does not need to import the scientific workload. Later execution lessons require that workload's pinned requirements.txt too.
cp data/transactions-a.csv data/transactions.csv
python lab.py source settings
apizr experiment inspect fraud_detection.ipynb --format json > inspection-a.json
python lab.py inspection inspection-a.json
Read the visible settings: 24 trees, maximum depth 5, a quarter reserved for evaluation and seed 42. These are source statements, not proof that training ran. In the recording, execution is not_executed, parameters are captured as candidates, metrics remain partial, and no trained model exists. The dynamic joblib.load reference leaves data evidence partial even alongside an identified CSV.
2. Same filename, different cohort
cp data/transactions-b.csv data/transactions.csv
apizr experiment inspect fraud_detection.ipynb --format json > inspection-b.json
python lab.py inspect-change
The CSV's bytes change while its path, notebook and declared hyperparameters remain the same. Neither inspection produces an AUC value or a model.
3. Replay the recorded presentation variant
python lab.py presentation
apizr experiment inspect fraud_detection.ipynb --format json > presentation.json
python lab.py signal-check
The helper changes stored execution counts and presentation metadata without executing a cell or editing its code. The retained result is different raw notebook bytes, equal scientific-signal digest, and execution still not_executed. These identities answer different questions.
Practice
Use the video's question: a colleague sees a seed, captured parameters and an AUC calculation, then claims a reproducible model score. Identify two unsupported claims and choose the next operation and environment.
Check your reasoning
No AUC value was observed because the notebook was not executed. A declared seed does not establish a reproduced outcome across environments. If you trust the notebook, explicitly run it in the environment containing its scientific dependencies. Preserve the inspection's evidence and unknowns instead of treating its populated sections as a completed experiment.