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این نوت بوک نحوه تولید یک کارت مدل را با استفاده از جعبه ابزار مدل کارت با یک مدل scikit-learn در محیط Jupyter/Colab نشان می دهد. شما می توانید اطلاعات بیشتر در مورد کارت های مدل در یاد https://modelcards.withgoogle.com/about .
ابتدا باید بسته های لازم را نصب و وارد کنیم.
pip install -q --upgrade pip==20.2
pip install -q -U seaborn scikit-learn model-card-toolkit
اگر از Google Colab استفاده می کنید، اولین باری که سلول بالا را اجرا می کنید، باید زمان اجرا را مجدداً راه اندازی کنید (Runtime > Restart runtime ...).
ما بسته های لازم را وارد می کنیم، از جمله scikit-learn.
from datetime import date
from io import BytesIO
from IPython import display
from model_card_toolkit import ModelCardToolkit
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_roc_curve, plot_confusion_matrix
import base64
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import uuid
این مثال با استفاده از مجموعه داده پستان سرطان ویسکانسین تشخیصی که scikit یادگیری می توانید با استفاده از بار load_breast_cancer () تابع.
cancer = load_breast_cancer()
X = pd.DataFrame(cancer.data, columns=cancer.feature_names)
y = pd.Series(cancer.target)
X_train, X_test, y_train, y_test = train_test_split(X, y)
X_train.head()
y_train.head()
28 0 157 1 381 1 436 1 71 1 dtype: int64
از داده هایی که در کارت مدل قرار می دهیم چندین نمودار ایجاد می کنیم.
# Utility function that will export a plot to a base-64 encoded string that the model card will accept.
def plot_to_str():
img = BytesIO()
plt.savefig(img, format='png')
return base64.encodebytes(img.getvalue()).decode('utf-8')
# Plot the mean radius feature for both the train and test sets
sns.displot(x=X_train['mean radius'], hue=y_train)
mean_radius_train = plot_to_str()
sns.displot(x=X_test['mean radius'], hue=y_test)
mean_radius_test = plot_to_str()
# Plot the mean texture feature for both the train and test sets
sns.displot(x=X_train['mean texture'], hue=y_train)
mean_texture_train = plot_to_str()
sns.displot(x=X_test['mean texture'], hue=y_test)
mean_texture_test = plot_to_str()
# Create a classifier and fit the training data
clf = GradientBoostingClassifier().fit(X_train, y_train)
# Plot a ROC curve
plot_roc_curve(clf, X_test, y_test)
roc_curve = plot_to_str()
# Plot a confusion matrix
plot_confusion_matrix(clf, X_test, y_test)
confusion_matrix = plot_to_str()
mct = ModelCardToolkit()
model_card = mct.scaffold_assets()
model_card.model_details.name = 'Breast Cancer Wisconsin (Diagnostic) Dataset'
model_card.model_details.overview = (
'This model predicts whether breast cancer is benign or malignant based on '
'image measurements.')
model_card.model_details.owners = [
{'name': 'Model Cards Team', 'contact': 'model-cards@google.com'}
]
model_card.model_details.references = [
'https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)',
'https://minds.wisconsin.edu/bitstream/handle/1793/59692/TR1131.pdf'
]
model_card.model_details.version.name = str(uuid.uuid4())
model_card.model_details.version.date = str(date.today())
model_card.considerations.ethical_considerations = [{
'name': ('Manual selection of image sections to digitize could create '
'selection bias'),
'mitigation_strategy': 'Automate the selection process'
}]
model_card.considerations.limitations = ['Breast cancer diagnosis']
model_card.considerations.use_cases = ['Breast cancer diagnosis']
model_card.considerations.users = ['Medical professionals', 'ML researchers']
model_card.model_parameters.data.train.graphics.description = (
f'{len(X_train)} rows with {len(X_train.columns)} features')
model_card.model_parameters.data.train.graphics.collection = [
{'image': mean_radius_train},
{'image': mean_texture_train}
]
model_card.model_parameters.data.eval.graphics.description = (
f'{len(X_test)} rows with {len(X_test.columns)} features')
model_card.model_parameters.data.eval.graphics.collection = [
{'image': mean_radius_test},
{'image': mean_texture_test}
]
model_card.quantitative_analysis.graphics.description = (
'ROC curve and confusion matrix')
model_card.quantitative_analysis.graphics.collection = [
{'image': roc_curve},
{'image': confusion_matrix}
]
mct.update_model_card_json(model_card)
# Return the model card document as an HTML page
html = mct.export_format()
display.display(display.HTML(html))