microsoftml.categorical_hash: Hashes and converts a text column into categories
microsoftml.categorical_hash(cols: [str, dict, list], hash_bits: int = 16, seed: int = 314489979, ordered: bool = True, invert_hash: int = 0, output_kind: ['Bag', 'Ind', 'Key', 'Bin'] = 'Bag', **kargs)
Categorical hash transform that can be performed on data before training a model.
categorical_hash converts a categorical value into an indicator
array by hashing the value and using the hash as an index in the bag. If
the input column is a vector, a single indicator bag is returned for it.
categorical_hash does not currently support handling factor data.
A character string or list of variable names to transform. If
dict, the keys represent the names of new variables to be created.
An integer specifying the number of bits to hash into. Must be between 1 and 30, inclusive. The default value is 16.
An integer specifying the hashing seed. The default value is 314489979.
True to include the position of each term in the
False. The default value is
An integer specifying the limit on the number of keys
that can be used to generate the slot name.
0 means no invert
-1 means no limit. While a zero value gives better
performance, a non-zero value is needed to get meaningful coefficent names.
The default value is
A character string that specifies the kind of output kind.
"Bag": Outputs a multi-set vector. If the input column is a vector of categories, the output contains one vector, where the value in each slot is the number of occurrences of the category in the input vector. If the input column contains a single category, the indicator vector and the bag vector are equivalent
"Ind": Outputs an indicator vector. The input column is a vector of categories, and the output contains one indicator vector per slot in the input column.
"Key: Outputs an index. The output is an integer id (between 1 and the number of categories in the dictionary) of the category.
"Bin: Outputs a vector which is the binary representation of the category.
The default value is
Additional arguments sent to the compute engine.
an object defining the transform.
''' Example on rx_logistic_regression and categorical_hash. ''' import numpy import pandas from microsoftml import rx_logistic_regression, categorical_hash, rx_predict from microsoftml.datasets.datasets import get_dataset movie_reviews = get_dataset("movie_reviews") train_reviews = pandas.DataFrame(data=dict( review=[ "This is great", "I hate it", "Love it", "Do not like it", "Really like it", "I hate it", "I like it a lot", "I kind of hate it", "I do like it", "I really hate it", "It is very good", "I hate it a bunch", "I love it a bunch", "I hate it", "I like it very much", "I hate it very much.", "I really do love it", "I really do hate it", "Love it!", "Hate it!", "I love it", "I hate it", "I love it", "I hate it", "I love it"], like=[True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True, False, True])) test_reviews = pandas.DataFrame(data=dict( review=[ "This is great", "I hate it", "Love it", "Really like it", "I hate it", "I like it a lot", "I love it", "I do like it", "I really hate it", "I love it"])) # Use a categorical hash transform. out_model = rx_logistic_regression("like ~ reviewCat", data=train_reviews, ml_transforms=[categorical_hash(cols=dict(reviewCat="review"))]) # Weights are similar to categorical. print(out_model.coef_) # Use the model to score. source_out_df = rx_predict(out_model, data=test_reviews, extra_vars_to_write=["review"]) print(source_out_df.head())
Not adding a normalizer. Beginning processing data. Rows Read: 25, Read Time: 0, Transform Time: 0 Beginning processing data. Beginning processing data. Rows Read: 25, Read Time: 0, Transform Time: 0 Beginning processing data. LBFGS multi-threading will attempt to load dataset into memory. In case of out-of-memory issues, turn off multi-threading by setting trainThreads to 1. Warning: Too few instances to use 4 threads, decreasing to 1 thread(s) Beginning optimization num vars: 65537 improvement criterion: Mean Improvement L1 regularization selected 3 of 65537 weights. Not training a calibrator because it is not needed. Elapsed time: 00:00:00.1209392 Elapsed time: 00:00:00.0190134 OrderedDict([('(Bias)', 0.2132447361946106), ('f1783', -0.7939924597740173), ('f38537', 0.1968022584915161)]) Beginning processing data. Rows Read: 10, Read Time: 0, Transform Time: 0 Beginning processing data. Elapsed time: 00:00:00.0284223 Finished writing 10 rows. Writing completed. review PredictedLabel Score Probability 0 This is great True 0.213245 0.553110 1 I hate it False -0.580748 0.358761 2 Love it True 0.213245 0.553110 3 Really like it True 0.213245 0.553110 4 I hate it False -0.580748 0.358761