Aprende Evaluating Deduplication Results | Deduplication Strategies

Evaluating deduplication results means checking how accurately your process finds and removes duplicates without deleting unique records. You use three metrics:

Precision: the proportion of records flagged as duplicates that were truly duplicates. High precision means few false positives.
Recall: the proportion of all actual duplicates that were correctly identified and removed. High recall means few true duplicates were missed.
F1-score: the harmonic mean of precision and recall, giving you a single value to compare deduplication strategies.

Track counts before and after deduplication—like total records, detected duplicates, and true duplicates removed—to calculate these metrics.


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import pandas as pd
from difflib import SequenceMatcher
from sklearn.metrics import precision_score, recall_score, f1_score

# Two datasets from different sources
df_a = pd.DataFrame({
    "id_a": [1, 2, 3, 4],
    "name": ["Apple iPhone 14", "Samsung Galaxy S22", "Sony WH1000 XM5", "Dell Inspiron 15"],
    "price": [999, 899, 350, 700]
})

df_b = pd.DataFrame({
    "id_b": ["A", "B", "C", "D"],
    "name": ["Iphone 14", "Galaxy S-22", "Sony WH-1000XM5", "Inspiron 15 DELL"],
    "price": [995, 900, 349, 705]
})

# Ground truth for duplicates (1 = duplicate pair, 0 = not duplicate)
y_true = [1, 1, 1, 0]  # Last pair intentionally marked as non-duplicate

# Generate similarity features
def name_similarity(a, b):
    return SequenceMatcher(None, a, b).ratio()

def price_difference(a, b):
    return abs(a - b) / max(a, b)

pairs = []
for i in range(len(df_a)):
    sim_name = name_similarity(df_a.loc[i, "name"], df_b.loc[i, "name"])
    diff_price = price_difference(df_a.loc[i, "price"], df_b.loc[i, "price"])

    pairs.append([sim_name, diff_price])

pairs_df = pd.DataFrame(pairs, columns=["name_similarity", "price_diff"])

# Simple duplicate classification rule
# High name similarity AND low price difference → duplicate
y_pred = []
for _, row in pairs_df.iterrows():
    if row["name_similarity"] > 0.75 and row["price_diff"] < 0.05:
        y_pred.append(1)
    else:
        y_pred.append(0)

# Metrics
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)

print("Similarity pairs:")
print(pairs_df)
print("\nMetrics:")
print(f"Precision: {precision:.2f}")
print(f"Recall:    {recall:.2f}")
print(f"F1-score:  {f1:.2f}")

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¡Gracias por tus comentarios!

Sección 2. Capítulo 2

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Suggested prompts:

Can you explain how to interpret the precision, recall, and F1-score values in this context?

How do I calculate these metrics using the table data provided?

What are some ways to improve deduplication performance based on these results?

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Evaluating deduplication results means checking how accurately your process finds and removes duplicates without deleting unique records. You use three metrics:

Precision: the proportion of records flagged as duplicates that were truly duplicates. High precision means few false positives.
Recall: the proportion of all actual duplicates that were correctly identified and removed. High recall means few true duplicates were missed.
F1-score: the harmonic mean of precision and recall, giving you a single value to compare deduplication strategies.

Track counts before and after deduplication—like total records, detected duplicates, and true duplicates removed—to calculate these metrics.


              1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556
            
import pandas as pd
from difflib import SequenceMatcher
from sklearn.metrics import precision_score, recall_score, f1_score

# Two datasets from different sources
df_a = pd.DataFrame({
    "id_a": [1, 2, 3, 4],
    "name": ["Apple iPhone 14", "Samsung Galaxy S22", "Sony WH1000 XM5", "Dell Inspiron 15"],
    "price": [999, 899, 350, 700]
})

df_b = pd.DataFrame({
    "id_b": ["A", "B", "C", "D"],
    "name": ["Iphone 14", "Galaxy S-22", "Sony WH-1000XM5", "Inspiron 15 DELL"],
    "price": [995, 900, 349, 705]
})

# Ground truth for duplicates (1 = duplicate pair, 0 = not duplicate)
y_true = [1, 1, 1, 0]  # Last pair intentionally marked as non-duplicate

# Generate similarity features
def name_similarity(a, b):
    return SequenceMatcher(None, a, b).ratio()

def price_difference(a, b):
    return abs(a - b) / max(a, b)

pairs = []
for i in range(len(df_a)):
    sim_name = name_similarity(df_a.loc[i, "name"], df_b.loc[i, "name"])
    diff_price = price_difference(df_a.loc[i, "price"], df_b.loc[i, "price"])

    pairs.append([sim_name, diff_price])

pairs_df = pd.DataFrame(pairs, columns=["name_similarity", "price_diff"])

# Simple duplicate classification rule
# High name similarity AND low price difference → duplicate
y_pred = []
for _, row in pairs_df.iterrows():
    if row["name_similarity"] > 0.75 and row["price_diff"] < 0.05:
        y_pred.append(1)
    else:
        y_pred.append(0)

# Metrics
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)

print("Similarity pairs:")
print(pairs_df)
print("\nMetrics:")
print(f"Precision: {precision:.2f}")
print(f"Recall:    {recall:.2f}")
print(f"F1-score:  {f1:.2f}")

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 2. Capítulo 2