Set di dati sul diabete

Articolo
2024-09-03

Il set di dati Diabetes include 442 esempi con 10 funzionalità ed è quindi ottimale per iniziare a usare gli algoritmi di Machine Learning. È uno dei set di dati più diffusi Scikit Learn Toy.

Descrizione del set di dati originale | File di dati originale

Nota

Microsoft fornisce i set di dati aperti di Azure così come sono e non fornisce né garanzie, esplicite o implicite, né specifica alcuna condizione in relazione all'uso dei set di dati. Nella misura consentita dalla legge locale, Microsoft declina tutte le responsabilità per eventuali danni o perdite, incluse dirette, consequenziali, speciali, indirette, accidentali o irreversibili, risultanti dall'uso dei set di dati.

Questo set di dati viene fornito in conformità con le condizioni originali in base alle quali Microsoft ha ricevuto i dati di origine. Il set di dati potrebbe includere dati provenienti da Microsoft.

Colonne

Nome	Tipo di dati	Unica	Valori (esempio)
AGE	bigint	58	53 60
BMI	double	163	24.1 23.5
BP	double	100	93.0 83.0
S1	bigint	141	162 184
S2	double	302	125.8 114.8
S3	double	63	46.0 38.0
S4	double	66	3.0 4.0
S5	double	184	4.4427 4.3041
S6	bigint	56	92 96
SEX	bigint	2	1 2
Y	bigint	214	72 200

Anteprima

AGE	SEX	BMI	BP	S1	S2	S3	S4	S5	S6	Y
59	2	32,1	101	157	93.2	38	4	4.8598	87	151
48	1	21.6	87	183	103.2	70	3	3.8918	69	75
72	2	30,5	93	156	93.6	41	4	4.6728	85	141
24	1	25.3	84	198	131.4	40	5	4.8903	89	206
50	1	23	101	192	125,4	52	4	4.2905	80	135
23	1	22.6	89	139	64.8	61	2	4.1897	68	97
36	2	22	90	160	99,6	50	3	3.9512	82	138
66	2	26.2	114	255	185	56	4.55	4.2485	92	63
60	2	32,1	83	179	119.4	42	4	4.4773	94	110
29	1	30	85	180	93.4	43	4	5.3845	88	310

Accesso ai dati

Usare gli esempi di codice seguenti per accedere a questo set di dati in Azure Notebooks, Azure Databricks o Azure Synapse.

Azure Notebooks

# This is a package in preview.
from azureml.opendatasets import Diabetes

diabetes = Diabetes.get_tabular_dataset()
diabetes_df = diabetes.to_pandas_dataframe()

diabetes_df.info()

# Pip install packages
import os, sys

!{sys.executable} -m pip install azure-storage-blob
!{sys.executable} -m pip install pyarrow
!{sys.executable} -m pip install pandas

# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "mlsamples"
folder_name = "diabetes"

from azure.storage.blob import BlockBlobServicefrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

if azure_storage_account_name is None or azure_storage_sas_token is None:
    raise Exception(
        "Provide your specific name and key for your Azure Storage account--see the Prerequisites section earlier.")

print('Looking for the first parquet under the folder ' +
      folder_name + ' in container "' + container_name + '"...')
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
    container_url, azure_storage_sas_token if azure_storage_sas_token else None)

container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)
sorted_blobs = sorted(list(blobs), key=lambda e: e.name, reverse=True)
targetBlobName = ''
for blob in sorted_blobs:
    if blob.name.startswith(folder_name) and blob.name.endswith('.parquet'):
        targetBlobName = blob.name
        break

print('Target blob to download: ' + targetBlobName)
_, filename = os.path.split(targetBlobName)
blob_client = container_client.get_blob_client(targetBlobName)
with open(filename, 'wb') as local_file:
    blob_client.download_blob().download_to_stream(local_file)

# Read the parquet file into Pandas data frame
import pandas as pd

print('Reading the parquet file into Pandas data frame')
df = pd.read_parquet(filename)

# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df

Azure Databricks

# This is a package in preview.
from azureml.opendatasets import Diabetes

diabetes = Diabetes.get_tabular_dataset()
diabetes_df = diabetes.to_spark_dataframe()

display(diabetes_df.limit(5))

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "mlsamples"
blob_relative_path = "diabetes"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

Campione non disponibile per questa combinazione di piattaforma/pacchetto.

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "mlsamples"
blob_relative_path = "diabetes"
blob_sas_token = r""

# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)

# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Passaggi successivi

Visualizzare il resto dei set di dati nel catalogo dei set di dati aperti.

Condividi tramite