Evalueer de classifier

Een getraind logistic regression-model df_fitted is beschikbaar. Er is een DataFrame df_testset met testgegevens voor dit model.

Deze oefening maakt deel uit van de cursus

Introductie tot Spark SQL in Python

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Oefeninstructies

Score het getrainde model op de testgegevens.
Print de Area Under Curve-metriek.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Score the model on test data
testSummary = ____.____(____)

# Print the AUC metric
print("\ntest AUC: %.3f" % testSummary.____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot Spark SQL in Python

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In this chapter you will learn how to create and query a SQL table in Spark. Spark SQL brings the expressiveness of SQL to Spark. You will also learn how to use SQL window functions in Spark. Window functions perform a calculation across rows that are related to the current row. They greatly simplify achieving results that are difficult to express using only joins and traditional aggregations. We'll use window functions to perform running sums, running differences, and other operations that are challenging to perform in basic SQL.

Exercise 1: Creating and querying a SQL table in Spark Exercise 2: Create a SQL table from a dataframe Exercise 3: Determine the column names of a table Exercise 4: Window function SQL Exercise 5: Running sums using window function SQL Exercise 6: Fix the broken query Exercise 7: Dot notation and SQL Exercise 8: Aggregation, step by step Exercise 9: Aggregating the same column twice Exercise 10: Aggregate dot SQL Exercise 11: Convert window function from dot notation to SQL

In this chapter, you will be loading natural language text. Then you will apply a moving window analysis to find frequent word sequences.

Exercise 1: Loading natural language text Exercise 2: Loading a dataframe from a parquet file Exercise 3: Split and explode a text column Exercise 4: Using monotonically_increasing_id()Exercise 5: Moving window analysis Exercise 6: Creating context window feature data Exercise 7: Repartitioning the data Exercise 8: Common word sequences Exercise 9: What type of data is this Exercise 10: Finding common word sequences Exercise 11: Unique 5-tuples in sorted order Exercise 12: Most frequent 3-tuples per chapter

In the previous chapters you learned how to use the expressiveness of window function SQL. However, this expressiveness now makes it important that you understand how to properly cache dataframes and cache SQL tables. It is also important to know how to evaluate your application. You learn how to do do this using the Spark UI. You'll also learn a best practice for logging in Spark. Spark SQL brings with it another useful tool for tuning query performance issues, the query execution plan. You will learn how to use the execution plan for evaluating the provenance of a dataframe.

Exercise 1: Caching Exercise 2: Practice query plans 2 Exercise 3: Practicing caching: part 1 Exercise 4: Practicing caching: the SQL Exercise 5: Practicing caching: putting it all together Exercise 6: Caching and uncaching tables Exercise 7: The Spark UI Exercise 8: Spark UI storage tab Exercise 9: Inspecting cache in the Spark UI Exercise 10: Logging Exercise 11: Practice logging Exercise 12: Practice logging 2 Exercise 13: Query plans Exercise 14: Practice query plans Exercise 15: Practice reading query plans 2

Previous chapters provided you with the tools for loading raw text, tokenizing it, and extracting word sequences. This is already very useful for analysis, but it is also useful for machine learning. What you've learned now comes together by using logistic regression to classify text. By the conclusion of this chapter, you will have loaded raw natural language text data and used it to train a text classifier.

Exercise 1: Extract Transform Select Exercise 2: Oefenen met het maken van een UDF Exercise 3: Oefenen met kolommen van het type array Exercise 4: Featuregegevens maken voor classificatie Exercise 5: Een UDF maken voor vectordata Exercise 6: Een UDF toepassen op vectorgegevens Exercise 7: Tekst omzetten naar vectorformaat Exercise 8: Tekstclassificatie Exercise 9: Voorzie de data van labels Exercise 10: Splits de data Exercise 11: Train de classifier Exercise 12: Voorspellen en evalueren Exercise 13: Evalueer de classifier

Huidige oefening

Exercise 14: Voorspel testgegevens Exercise 15: Samenvatting