[Hackathon] feat: Data Profiling Panel — Instant Dataset Analysis Before You Run#5114
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EmilySun621 wants to merge 4 commits into
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[Hackathon] feat: Data Profiling Panel — Instant Dataset Analysis Before You Run#5114EmilySun621 wants to merge 4 commits into
EmilySun621 wants to merge 4 commits into
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This bundles the feature work that built up on this branch:
- Custom agents: dashboard CRUD page and editor dialog (48px icon tile,
chip-style guardrails, model selector). Each custom agent now carries a
LiteLLM model_name (Opus 4.7 / Haiku 4.5) that is passed through to the
agent-service so different agents can use different models.
- Conversation history is scoped per (workflowId, agentId): switching
agent or workflow yields a different conversation list. localStorage
key: texera.workflowConversations.v1.{workflowId}.{agentId}.
- Time machine: workflow snapshot list, revert, and agent-tagged
checkpoints. New workflow-history-tool in agent-service backs the
"undo my last change" flow; amber gains a WorkflowSnapshotResource;
sql/updates/23.sql adds the snapshot table.
- Operator-aware custom-agent prompts: the system prompt now injects the
full operator catalog with a "prefer built-in operators over Python
UDFs" rule, sourced from WorkflowSystemMetadata at request time.
- LiteLLM: added the claude-opus-4.7 entry alongside claude-haiku-4.5
and gpt-5-mini in bin/litellm-config.yaml.
- Agent panel rewritten around the (conversation list / chat) two-view
model with subscription-managed list reloads and per-step persistence.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…, role detection
Adds a Data Profiling Panel triggered from data-source operator properties
(CSV/JSON/Parquet/FileScan). The panel surfaces three derived views on top of
a single profile response — no new backend calls:
- Data Quality Score (0–100): completeness, duplicates, outliers, constant
columns, high-cardinality categoricals, and class-imbalance penalties,
with a colored progress bar and sub-score badges.
- Auto-Suggest Cleaning Actions: severity-sorted rules (drop sparse/ID/
constant cols, impute via median/mode, deduplicate, review outliers) with
an Add-to-Workflow button that copies an operator hint to the clipboard.
- Column Relationship Detector: heuristic ID/target/feature/datetime/
constant classification with badges per column and an auto-detected
summary section.
Wires a small "📊 Profile Data" button into the operator property editor that
opens the panel as a draggable modal seeded with the operator's file path.
Backend integration is intentionally a follow-up; the service ships a
deterministic mock so the UX is fully exercised.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
…ble rule Adds a console.debug so we can see what operatorType is on the selected operator (helps when the rule doesn't match an unexpected name). Also broadens the profileable regex to include Text/File so anything that looks remotely like a data source shows the button. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
DataProfilingService now fetches the actual dataset file via DatasetService.retrieveDatasetVersionSingleFile (presign-download endpoint), parses with papaparse (first 5000 rows for performance), and runs a new pure-TS profiler that computes: - dtype inference per column (numeric / datetime / boolean / categorical / text) - per-column: count, missing, missingPercent, unique, plus dtype-specific stats - numeric: mean, median, std, min, max, ±3σ outlier count, 10-bin histogram - categorical/boolean: top-5 value counts - dataset-level: row-key duplicate count - Pearson correlation matrix across (up to 8) numeric columns If the source isn't a dataset path or any step fails (fetch / parse / empty headers), we fall back to the deterministic mock so the panel always renders. The panel header now shows a short filename (full path on hover) and surfaces fetch/parse errors inline. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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🎯 Problem
Researchers drag a CSV onto the canvas and start building operators blind — they don't know how many missing values there are, which columns are IDs, or whether the target variable is imbalanced. They find out after the workflow fails.
💡 Solution
One-click data profiling that reads the real CSV and tells you everything before you write a single operator.
✨ Features
1. Data Quality Score (0-100)
Single number summarizing dataset health, with sub-score breakdown:
Color scale: 🟢 90-100 Excellent · 🟡 70-89 Good · 🟠 50-69 Needs attention · 🔴 0-49 Poor
2. Suggested Cleaning Actions
Rule-based, no LLM — zero hallucination risk:
Sorted by severity: critical → warning → info.
3. Column Role Detection (auto)
Heuristic classification of each column's ML role:
Summary: "1 possible target: Species · 1 ID: Id · 4 features: SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm"
4. Per-Column Statistics
Each column card displays:
5. Overview Tabs
6. Real Data, Not Mock
Reads the actual CSV through Texera's file-service API. Parses, computes statistics, and renders in real time. Mock data fallback only if API fails.
📸 Screenshots
🎬 Demo
📁 Files Changed
New files:
data-profiling-panel/data-profiling.types.ts— Profile, Column, Suggestion, Role typesdata-profiling-panel/data-profiling.utils.ts— Quality score, suggestions, role detection algorithmsdata-profiling-panel/data-profiling.service.ts— CSV fetch, parse, compute statsdata-profiling-panel/data-profiling-panel.component.*— Panel UI with score, suggestions, columns, correlationsdata-profiling-panel/data-profiling-modal.component.ts— Modal wrapperModified (additive only):
operator-property-edit-frame— Added "📊 Profile Data" button for CSV/scan operators✅ Testing