mirror of
https://github.com/twitter/the-algorithm.git
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d2da7e56ab
removes a pair of brackets, making the intent slightly clearer Convert for loop into dictionary comprehension (dict-comprehension)
93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
from .parsers import LollyModelFeaturesParser
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class TFModelInitializerBuilder:
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def __init__(self, model_features_parser=LollyModelFeaturesParser()):
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self._model_features_parser = model_features_parser
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def build(self, lolly_model_reader):
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'''
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:param lolly_model_reader: LollyModelReader instance
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:return: tf_model_initializer dictionary of the following format:
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{
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"features": {
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"bias": 0.0,
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"binary": {
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# (feature name : feature weight) pairs
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"feature_name_1": 0.0,
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...
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"feature_nameN": 0.0
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},
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"discretized": {
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# (feature name : index aligned lists of bin_boundaries and weights
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"feature_name_1": {
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"bin_boundaries": [1, ..., inf],
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"weights": [0.0, ..., 0.0]
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}
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...
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"feature_name_K": {
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"bin_boundaries": [1, ..., inf],
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"weights": [0.0, ..., 0.0]
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}
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}
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}
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}
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'''
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tf_model_initializer = {
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"features": {}
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}
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features = self._model_features_parser.parse(lolly_model_reader)
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tf_model_initializer["features"]["bias"] = features["bias"]
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self._set_discretized_features(features["discretized"], tf_model_initializer)
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self._dedup_binary_features(features["binary"], features["discretized"])
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tf_model_initializer["features"]["binary"] = features["binary"]
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return tf_model_initializer
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def _set_discretized_features(self, discretized_features, tf_model_initializer):
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if len(discretized_features) == 0:
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return
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num_bins = max(len(bins) for bins in discretized_features.values())
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bin_boundaries_and_weights = {
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feature_name: self._extract_bin_boundaries_and_weights(
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discretized_features[feature_name], num_bins)
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for feature_name in discretized_features
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}
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tf_model_initializer["features"]["discretized"] = bin_boundaries_and_weights
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def _dedup_binary_features(self, binary_features, discretized_features):
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[binary_features.pop(feature_name) for feature_name in discretized_features]
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def _extract_bin_boundaries_and_weights(self, discretized_feature_buckets, num_bins):
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bin_boundary_weight_pairs = []
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for bucket in discretized_feature_buckets:
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bin_boundary_weight_pairs.append([bucket[0], bucket[2]])
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# The default DBv2 HashingDiscretizer bin membership interval is (a, b]
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#
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# The Earlybird Lolly prediction engine discretizer bin membership interval is [a, b)
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#
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# Thus, convert (a, b] to [a, b) by inverting the bin boundaries.
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for bin_boundary_weight_pair in bin_boundary_weight_pairs:
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if bin_boundary_weight_pair[0] < float("inf"):
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bin_boundary_weight_pair[0] *= -1
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while len(bin_boundary_weight_pairs) < num_bins:
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bin_boundary_weight_pairs.append([float("inf"), float(0)])
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bin_boundary_weight_pairs.sort(key=lambda bin_boundary_weight_pair: bin_boundary_weight_pair[0])
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bin_boundaries, weights = list(zip(*bin_boundary_weight_pairs))
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return {
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"bin_boundaries": bin_boundaries,
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"weights": weights
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}
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