-
-
Save quydx/51d22ce08aae71828a6d8fc4b509748f to your computer and use it in GitHub Desktop.
Use trained sklearn model with pyspark
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| from pyspark import SparkContext | |
| import numpy as np | |
| from sklearn import ensemble | |
| def batch(xs): | |
| yield list(xs) | |
| N = 1000 | |
| train_x = np.random.randn(N, 10) | |
| train_y = np.random.binomial(1, 0.5, N) | |
| model = ensemble.RandomForestClassifier(10).fit(train_x, train_y) | |
| test_x = np.random.randn(N * 100, 10) | |
| sc = SparkContext() | |
| n_partitions = 10 | |
| rdd = sc.parallelize(test_x, n_partitions).zipWithIndex() | |
| b_model = sc.broadcast(model) | |
| result = rdd.mapPartitions(batch) \ | |
| .map(lambda xs: ([x[0] for x in xs], [x[1] for x in xs])) \ | |
| .flatMap(lambda x: zip(x[1], b_model.value.predict(x[0]))) | |
| print(result.take(100)) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment