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DOC fix dollar sign to euro sign #29020

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Merged
merged 2 commits into from
May 14, 2024
Merged

DOC fix dollar sign to euro sign #29020

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9152b7a
DOC fix dolor sign to euro sign
adrinjalali May 14, 2024
dd0eb13
Update examples/model_selection/plot_cost_sensitive_learning.py
adrinjalali May 14, 2024
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22 changes: 11 additions & 11 deletions examples/model_selection/plot_cost_sensitive_learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -489,7 +489,7 @@ def plot_roc_pr_curves(vanilla_model, tuned_model, *, title):
_, ax = plt.subplots()
ax.hist(amount_fraud, bins=100)
ax.set_title("Amount of fraud transaction")
_ = ax.set_xlabel("Amount ($)")
_ = ax.set_xlabel("Amount (€)")

# %%
# Addressing the problem with a business metric
Expand All @@ -501,8 +501,8 @@ def plot_roc_pr_curves(vanilla_model, tuned_model, *, title):
# transaction result in a loss of the amount of the transaction. As stated in [2]_, the
# gain and loss related to refusals (of fraudulent and legitimate transactions) are not
# trivial to define. Here, we define that a refusal of a legitimate transaction is
# estimated to a loss of $5 while the refusal of a fraudulent transaction is estimated
# to a gain of $50 dollars and the amount of the transaction. Therefore, we define the
# estimated to a loss of 5€ while the refusal of a fraudulent transaction is estimated
# to a gain of 50€ and the amount of the transaction. Therefore, we define the
# following function to compute the total benefit of a given decision:


Expand Down Expand Up @@ -557,22 +557,22 @@ def business_metric(y_true, y_pred, amount):
benefit_cost = business_scorer(
easy_going_classifier, data_test, target_test, amount=amount_test
)
print(f"Benefit/cost of our easy-going classifier: ${benefit_cost:,.2f}")
print(f"Benefit/cost of our easy-going classifier: {benefit_cost:,.2f}€")

# %%
# A classifier that predict all transactions as legitimate would create a profit of
# around $220,000. We make the same evaluation for a classifier that predicts all
# around 220,000.€ We make the same evaluation for a classifier that predicts all
# transactions as fraudulent.
intolerant_classifier = DummyClassifier(strategy="constant", constant=1)
intolerant_classifier.fit(data_train, target_train)
benefit_cost = business_scorer(
intolerant_classifier, data_test, target_test, amount=amount_test
)
print(f"Benefit/cost of our intolerant classifier: ${benefit_cost:,.2f}")
print(f"Benefit/cost of our intolerant classifier: {benefit_cost:,.2f}€")

# %%
# Such a classifier create a loss of around $670,000. A predictive model should allow
# us to make a profit larger than $220,000. It is interesting to compare this business
# Such a classifier create a loss of around 670,000.€ A predictive model should allow
# us to make a profit larger than 220,000.€ It is interesting to compare this business
# metric with another "standard" statistical metric such as the balanced accuracy.
from sklearn.metrics import get_scorer

Expand Down Expand Up @@ -607,7 +607,7 @@ def business_metric(y_true, y_pred, amount):

print(
"Benefit/cost of our logistic regression: "
f"${business_scorer(model, data_test, target_test, amount=amount_test):,.2f}"
f"{business_scorer(model, data_test, target_test, amount=amount_test):,.2f}€"
)
print(
"Balanced accuracy of our logistic regression: "
Expand Down Expand Up @@ -645,7 +645,7 @@ def business_metric(y_true, y_pred, amount):
# %%
print(
"Benefit/cost of our logistic regression: "
f"${business_scorer(tuned_model, data_test, target_test, amount=amount_test):,.2f}"
f"{business_scorer(tuned_model, data_test, target_test, amount=amount_test):,.2f}€"
)
print(
"Balanced accuracy of our logistic regression: "
Expand Down Expand Up @@ -691,7 +691,7 @@ def business_metric(y_true, y_pred, amount):
business_score = business_scorer(
model_fixed_threshold, data_test, target_test, amount=amount_test
)
print(f"Benefit/cost of our logistic regression: ${business_score:,.2f}")
print(f"Benefit/cost of our logistic regression: {business_score:,.2f}€")
print(
"Balanced accuracy of our logistic regression: "
f"{balanced_accuracy_scorer(model_fixed_threshold, data_test, target_test):.3f}"
Expand Down
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