Data Analysis
Liest ein CSV/JSON-Dataset und teilt es stratifiziert nach der Zielspalte in train/validation/test (standardmäßig 70/15/15, reproduzierbarer Seed) oder stratifiziertes k-fold; Bericht zur Klassenverteilung pro Split mit Abweichungsbalken, Leck-Check über doppelte Zeilen, SMOTE-Vorschau (Nächste-Nachbarn-Interpolation auf dem Train-Split) und CSV-Export als ZIP.
Rufen Sie dieses Werkzeug aus Ihrem Code in drei Sprachen auf.
# 1) Request a presigned URL → returns { uploadUrl, storageKey }
curl -X POST 'http://127.0.0.1:3003/api/upload/presign/train-test-split-with-stratification' \
-H 'Content-Type: application/json' \
-d '{"filename":"dataFile.ext","contentType":"application/octet-stream","size":12345}'
# 2) PUT the file bytes directly to the presigned uploadUrl
curl -X PUT '<presigned uploadUrl>' \
--data-binary @/path/to/file.ext
# 3) Call the tool, passing the returned storageKey for each file field
curl -X POST 'http://127.0.0.1:3003/de/api/tools/train-test-split-with-stratification' \
-F 'dataFile=uploads/2026/01/01/your-tool-1700000000000-abc123.ext' \
-F 'datasetText=text,length,label
The movie was wonderful and I loved every minute,42,positive
Absolutely terrible waste of time,31,negative
Pretty good overall would recommend,31,positive
Bad acting and a worse plot,25,negative
I enjoyed it a lot,16,positive
Not great not terrible just fine,30,neutral
Loved the soundtrack and visuals,28,positive
The pacing was boring and slow,28,negative
It was okay nothing special,24,neutral
Fantastic acting by the whole cast,30,positive
Weak ending ruined it for me,24,negative
Fine for a rainy afternoon,24,neutral
Brilliant and moving film,23,positive
Disappointing sequel,19,negative
Mediocre but watchable,21,neutral
Gorgeous cinematography throughout,29,positive
Painfully dull script,19,negative
A masterpiece of the genre,24,positive
Cliched and forgettable,20,negative
Charming and genuinely funny,25,positive
Skip it you will thank me,21,negative
Delightful from start to finish,27,positive
Two hours I will never get back,29,negative
Watchable if nothing else is on,26,neutral' \
-F 'targetColumn=label' \
-F 'splitMode=standard' \
-F 'trainPercent=70' \
-F 'valPercent=15' \
-F 'kFolds=5' \
-F 'randomSeed=42' \
-F 'stratify=true' \
-F 'shuffle=true' \
-F 'smotePreview=false' \
-F 'exportZip=true'Senden Sie eine POST-Anfrage mit Ihren Eingaben als JSON. Dateiparameter erfordern einen vorherigen Upload.
POST http://127.0.0.1:3003/de/api/tools/train-test-split-with-stratification| Name | Typ | Erforderlich | Beschreibung |
|---|---|---|---|
| dataFile | fileUpload nötig | Nein | — |
| datasetText | textarea | Nein | — |
| targetColumn | text | Ja | — |
| splitMode | select | Nein | — |
| trainPercent | number | Nein | — |
| valPercent | number | Nein | — |
| kFolds | number | Nein | — |
| randomSeed | number | Nein | — |
| stratify | checkbox | Nein | — |
| shuffle | checkbox | Nein | — |
| smotePreview | checkbox | Nein | — |
| exportZip | checkbox | Nein | — |
HTML-Ergebnis
{
"result": "<div>Processed HTML content</div>",
"error": "Error message (optional)",
"message": "Notification message (optional)",
"metadata": {
"key": "value"
}
}Fügen Sie dieses Werkzeug Ihrem Model-Context-Protocol-Server hinzu, damit KI-Agenten es auflisten und aufrufen können.
Fügen Sie diesen Block Ihrer MCP-Client-Konfiguration hinzu:
{
"mcpServers": {
"elysiatools-train-test-split-with-stratification": {
"name": "train-test-split-with-stratification",
"description": "Liest ein CSV/JSON-Dataset und teilt es stratifiziert nach der Zielspalte in train/validation/test (standardmäßig 70/15/15, reproduzierbarer Seed) oder stratifiziertes k-fold; Bericht zur Klassenverteilung pro Split mit Abweichungsbalken, Leck-Check über doppelte Zeilen, SMOTE-Vorschau (Nächste-Nachbarn-Interpolation auf dem Train-Split) und CSV-Export als ZIP.",
"baseUrl": "http://127.0.0.1:3003/mcp/sse?toolId=train-test-split-with-stratification",
"command": "",
"args": [],
"env": {},
"isActive": true,
"type": "sse"
}
}
}Nach dem Verbinden mit dem SSE-Endpunkt listen Sie die bereitgestellten Werkzeuge auf:
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/list"
}Rufen Sie das Werkzeug über seine ID auf; Argumente werden aus seiner Parameterliste gebildet:
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "train-test-split-with-stratification",
"arguments": {
"dataFile": "https://example.com/file.ext",
"datasetText": "text,length,label\nThe movie was wonderful and I loved every minute,42,positive\nAbsolutely terrible waste of time,31,negative\nPretty good overall would recommend,31,positive\nBad acting and a worse plot,25,negative\nI enjoyed it a lot,16,positive\nNot great not terrible just fine,30,neutral\nLoved the soundtrack and visuals,28,positive\nThe pacing was boring and slow,28,negative\nIt was okay nothing special,24,neutral\nFantastic acting by the whole cast,30,positive\nWeak ending ruined it for me,24,negative\nFine for a rainy afternoon,24,neutral\nBrilliant and moving film,23,positive\nDisappointing sequel,19,negative\nMediocre but watchable,21,neutral\nGorgeous cinematography throughout,29,positive\nPainfully dull script,19,negative\nA masterpiece of the genre,24,positive\nCliched and forgettable,20,negative\nCharming and genuinely funny,25,positive\nSkip it you will thank me,21,negative\nDelightful from start to finish,27,positive\nTwo hours I will never get back,29,negative\nWatchable if nothing else is on,26,neutral",
"targetColumn": "label",
"splitMode": "standard",
"trainPercent": 70,
"valPercent": 15,
"kFolds": 5,
"randomSeed": 42,
"stratify": true,
"shuffle": true,
"smotePreview": false,
"exportZip": true
}
}
}Fragen oder Probleme? Kontakt: [email protected]