Calcolare la conviction con una funzione

Dopo il successo del progetto pilota, la fondatrice della start-up di ebook decide di assumerti per un progetto molto più grande. Ti chiede se sei in grado di calcolare la conviction per ogni coppia di libri nel dataset goodreads-10k, così da usare queste informazioni per decidere quali libri posizionare più vicini tra loro sul sito.

Accetti l’incarico, ma ti rendi conto che ti serve un modo più efficiente per calcolare la conviction, dato che dovrai farlo molte volte. Decidi quindi di scrivere una funzione che la calcoli. Prenderà in input due colonne di un DataFrame pandas, un antecedente e un conseguente, e restituirà la metrica di conviction. Nota che pandas è disponibile come pd e numpy è disponibile come np.

Questo esercizio fa parte del corso

Analisi del carrello in Python

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Istruzioni dell'esercizio

Calcola il supporto per l’antecedente e assegnalo a supportA.
Calcola il supporto per NOT conseguente.
Calcola il supporto per antecedente e NOT conseguente.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

def conviction(antecedent, consequent):
	# Compute support for antecedent AND consequent
	supportAC = np.logical_and(antecedent, consequent).mean()

	# Compute support for antecedent
	supportA = ____.____()

	# Compute support for NOT consequent
	supportnC = 1.0 - ____.____()

	# Compute support for antecedent and NOT consequent
	supportAnC = ____ - supportAC

    # Return conviction
	return supportA * supportnC / supportAnC

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analisi del carrello in Python

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In this chapter, you’ll learn the basics of Market Basket Analysis: association rules, metrics, and pruning. You’ll then apply these concepts to help a small grocery store improve its promotional and product placement efforts.

Exercise 1: What is market basket analysis?Exercise 2: The basics of market basket analysis Exercise 3: Cross-selling products Exercise 4: Identifying association rules Exercise 5: Multiple antecedents and consequents Exercise 6: Preparing data for market basket analysis Exercise 7: Generating association rules Exercise 8: The simplest metric Exercise 9: One-hot encoding transaction data Exercise 10: Computing the support metric

Association rules tell us that two or more items are related. Metrics allow us to quantify the usefulness of those relationships. In this chapter, you’ll apply six metrics to evaluate association rules: supply, confidence, lift, conviction, leverage, and Zhang's metric. You’ll then use association rules and metrics to assist a library and an e-book seller.

Exercise 1: Confidenza e lift Exercise 2: Consigliare libri con il supporto Exercise 3: Raffinare il supporto con la confidence Exercise 4: Ulteriore affinamento con il lift Exercise 5: Leverage e conviction Exercise 6: Lift vs leverage Exercise 7: Calcolare la conviction Exercise 8: Calcolare la conviction con una funzione

Esercizio in corso

Exercise 9: Promuovere ebook con conviction Exercise 10: Associazione e dissociazione Exercise 11: Calcolare associazione e dissociazione Exercise 12: Definire la metrica di Zhang Exercise 13: Applicare la metrica di Zhang Exercise 14: Regole avanzate Exercise 15: Filtrare con support e conviction Exercise 16: Usare il filtraggio multi-metrica per la promozione incrociata dei libri

The fundamental problem of Market Basket Analysis is determining how to translate vast amounts of customer decisions into a small number of useful rules. This process typically starts with the application of the Apriori algorithm and involves the use of additional strategies, such as pruning and aggregation. In this chapter, you’ll learn how to use these methods and will ultimately apply them in exercises where you assist a retailer in selecting a physical store layout and performing product cross-promotions.

Exercise 1: Aggregation Exercise 2: Performing aggregation Exercise 3: Defining an aggregation function Exercise 4: The Apriori algorithm Exercise 5: Pruning and Apriori Exercise 6: Identifying frequent itemsets with Apriori Exercise 7: Selecting a support threshold Exercise 8: Basic Apriori results pruning Exercise 9: Generating association rules Exercise 10: Pruning with lift Exercise 11: Pruning with confidence Exercise 12: Advanced Apriori results pruning Exercise 13: Aggregation and filtering Exercise 14: Applying Zhang's rule Exercise 15: Advanced filtering with multiple metrics

In this final chapter, you’ll learn how visualizations are used to guide the pruning process and summarize final results, which will typically take the form of itemsets or rules. You’ll master the three most useful visualizations -- heatmaps, scatterplots, and parallel coordinates plots – and will apply them to assist a movie streaming service.

Exercise 1: Heatmaps Exercise 2: Visualizing itemset support Exercise 3: Heatmaps with lift Exercise 4: Interpreting heatmaps Exercise 5: Scatterplots Exercise 6: Pruning with scatterplots Exercise 7: Optimality of the support-confidence border Exercise 8: Parallel coordinates plot Exercise 9: Using parallel coordinates to visualize rules Exercise 10: Refining a parallel coordinates plot Exercise 11: Congratulations!