Remove feature space for now
This commit is contained in:
Francois Chollet
parent
022c095075
commit
807b9f0b3b
@ -1,757 +0,0 @@
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from tensorflow import data as tf_data
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from keras_core import layers
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from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.layers.layer import Layer
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from keras_core.saving import saving_lib
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from keras_core.saving import serialization_lib
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from keras_core.utils.naming import auto_name
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class Cross:
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def __init__(self, feature_names, crossing_dim, output_mode="one_hot"):
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if output_mode not in {"int", "one_hot"}:
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raise ValueError(
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"Invalid value for argument `output_mode`. "
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"Expected one of {'int', 'one_hot'}. "
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f"Received: output_mode={output_mode}"
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)
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self.feature_names = tuple(feature_names)
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self.crossing_dim = crossing_dim
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self.output_mode = output_mode
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@property
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def name(self):
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return "_X_".join(self.feature_names)
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def get_config(self):
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return {
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"feature_names": self.feature_names,
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"crossing_dim": self.crossing_dim,
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"output_mode": self.output_mode,
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}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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class Feature:
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def __init__(self, dtype, preprocessor, output_mode):
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if output_mode not in {"int", "one_hot", "float"}:
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raise ValueError(
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"Invalid value for argument `output_mode`. "
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"Expected one of {'int', 'one_hot', 'float'}. "
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f"Received: output_mode={output_mode}"
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)
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self.dtype = dtype
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if isinstance(preprocessor, dict):
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preprocessor = serialization_lib.deserialize_keras_object(
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preprocessor
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)
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self.preprocessor = preprocessor
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self.output_mode = output_mode
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def get_config(self):
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return {
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"dtype": self.dtype,
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"preprocessor": serialization_lib.serialize_keras_object(
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self.preprocessor
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),
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"output_mode": self.output_mode,
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}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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@keras_core_export("keras_core.utils.FeatureSpace")
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class FeatureSpace(Layer):
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"""One-stop utility for preprocessing and encoding structured data.
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Arguments:
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feature_names: Dict mapping the names of your features to their
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type specification, e.g. `{"my_feature": "integer_categorical"}`
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or `{"my_feature": FeatureSpace.integer_categorical()}`.
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For a complete list of all supported types, see
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"Available feature types" paragraph below.
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output_mode: One of `"concat"` or `"dict"`. In concat mode, all
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features get concatenated together into a single vector.
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In dict mode, the FeatureSpace returns a dict of individually
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encoded features (with the same keys as the input dict keys).
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crosses: List of features to be crossed together, e.g.
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`crosses=[("feature_1", "feature_2")]`. The features will be
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"crossed" by hashing their combined value into
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a fixed-length vector.
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crossing_dim: Default vector size for hashing crossed features.
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Defaults to `32`.
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hashing_dim: Default vector size for hashing features of type
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`"integer_hashed"` and `"string_hashed"`. Defaults to `32`.
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num_discretization_bins: Default number of bins to be used for
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discretizing features of type `"float_discretized"`.
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Defaults to `32`.
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**Available feature types:**
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Note that all features can be referred to by their string name,
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e.g. `"integer_categorical"`. When using the string name, the default
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argument values are used.
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```python
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# Plain float values.
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FeatureSpace.float(name=None)
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# Float values to be preprocessed via featurewise standardization
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# (i.e. via a `keras.layers.Normalization` layer).
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FeatureSpace.float_normalized(name=None)
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# Float values to be preprocessed via linear rescaling
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# (i.e. via a `keras.layers.Rescaling` layer).
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FeatureSpace.float_rescaled(scale=1., offset=0., name=None)
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# Float values to be discretized. By default, the discrete
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# representation will then be one-hot encoded.
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FeatureSpace.float_discretized(
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num_bins, bin_boundaries=None, output_mode="one_hot", name=None)
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# Integer values to be indexed. By default, the discrete
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# representation will then be one-hot encoded.
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FeatureSpace.integer_categorical(
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max_tokens=None, num_oov_indices=1, output_mode="one_hot", name=None)
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# String values to be indexed. By default, the discrete
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# representation will then be one-hot encoded.
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FeatureSpace.string_categorical(
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max_tokens=None, num_oov_indices=1, output_mode="one_hot", name=None)
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# Integer values to be hashed into a fixed number of bins.
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# By default, the discrete representation will then be one-hot encoded.
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FeatureSpace.integer_hashed(num_bins, output_mode="one_hot", name=None)
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# String values to be hashed into a fixed number of bins.
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# By default, the discrete representation will then be one-hot encoded.
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FeatureSpace.string_hashed(num_bins, output_mode="one_hot", name=None)
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```
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Examples:
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**Basic usage with a dict of input data:**
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```python
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raw_data = {
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"float_values": [0.0, 0.1, 0.2, 0.3],
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"string_values": ["zero", "one", "two", "three"],
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"int_values": [0, 1, 2, 3],
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}
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dataset = tf.data.Dataset.from_tensor_slices(raw_data)
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feature_space = FeatureSpace(
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features={
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"float_values": "float_normalized",
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"string_values": "string_categorical",
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"int_values": "integer_categorical",
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},
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crosses=[("string_values", "int_values")],
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output_mode="concat",
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)
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# Before you start using the FeatureSpace,
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# you must `adapt()` it on some data.
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feature_space.adapt(dataset)
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# You can call the FeatureSpace on a dict of data (batched or unbatched).
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output_vector = feature_space(raw_data)
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```
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**Basic usage with `tf.data`:**
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```python
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# Unlabeled data
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preprocessed_ds = unlabeled_dataset.map(feature_space)
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# Labeled data
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preprocessed_ds = labeled_dataset.map(lambda x, y: (feature_space(x), y))
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```
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**Basic usage with the Keras Functional API:**
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```python
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# Retrieve a dict Keras Input objects
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inputs = feature_space.get_inputs()
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# Retrieve the corresponding encoded Keras tensors
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encoded_features = feature_space.get_encoded_features()
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# Build a Functional model
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outputs = keras.layers.Dense(1, activation="sigmoid")(encoded_features)
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model = keras.Model(inputs, outputs)
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```
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**Customizing each feature or feature cross:**
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```python
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feature_space = FeatureSpace(
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features={
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"float_values": FeatureSpace.float_normalized(),
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"string_values": FeatureSpace.string_categorical(max_tokens=10),
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"int_values": FeatureSpace.integer_categorical(max_tokens=10),
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},
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crosses=[
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FeatureSpace.cross(("string_values", "int_values"), crossing_dim=32)
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],
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output_mode="concat",
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)
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```
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**Returning a dict of integer-encoded features:**
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```python
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feature_space = FeatureSpace(
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features={
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"string_values": FeatureSpace.string_categorical(output_mode="int"),
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"int_values": FeatureSpace.integer_categorical(output_mode="int"),
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},
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crosses=[
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FeatureSpace.cross(
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feature_names=("string_values", "int_values"),
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crossing_dim=32,
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output_mode="int",
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)
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],
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output_mode="dict",
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)
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```
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**Specifying your own Keras preprocessing layer:**
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```python
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# Let's say that one of the features is a short text paragraph that
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# we want to encode as a vector (one vector per paragraph) via TF-IDF.
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data = {
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"text": ["1st string", "2nd string", "3rd string"],
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}
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# There's a Keras layer for this: TextVectorization.
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custom_layer = layers.TextVectorization(output_mode="tf_idf")
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# We can use FeatureSpace.feature to create a custom feature
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# that will use our preprocessing layer.
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feature_space = FeatureSpace(
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features={
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"text": FeatureSpace.feature(
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preprocessor=custom_layer, dtype="string", output_mode="float"
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),
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},
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output_mode="concat",
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)
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feature_space.adapt(tf.data.Dataset.from_tensor_slices(data))
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output_vector = feature_space(data)
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```
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**Retrieving the underlying Keras preprocessing layers:**
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```python
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# The preprocessing layer of each feature is available in `.preprocessors`.
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preprocessing_layer = feature_space.preprocessors["feature1"]
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# The crossing layer of each feature cross is available in `.crossers`.
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# It's an instance of keras.layers.HashedCrossing.
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crossing_layer = feature_space.crossers["feature1_X_feature2"]
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```
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**Saving and reloading a FeatureSpace:**
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```python
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feature_space.save("myfeaturespace.keras")
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reloaded_feature_space = keras.models.load_model("myfeaturespace.keras")
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```
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"""
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@classmethod
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def cross(cls, feature_names, crossing_dim, output_mode="one_hot"):
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return Cross(feature_names, crossing_dim, output_mode=output_mode)
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@classmethod
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def feature(cls, dtype, preprocessor, output_mode):
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return Feature(dtype, preprocessor, output_mode)
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@classmethod
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def float(cls, name=None):
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from keras.layers.core import identity
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name = name or auto_name("float")
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preprocessor = identity.Identity(
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dtype="float32", name=f"{name}_preprocessor"
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)
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return Feature(
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dtype="float32", preprocessor=preprocessor, output_mode="float"
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)
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@classmethod
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def float_rescaled(cls, scale=1.0, offset=0.0, name=None):
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name = name or auto_name("float_rescaled")
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preprocessor = layers.Rescaling(
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scale=scale, offset=offset, name=f"{name}_preprocessor"
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)
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return Feature(
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dtype="float32", preprocessor=preprocessor, output_mode="float"
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)
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@classmethod
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def float_normalized(cls, name=None):
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name = name or auto_name("float_normalized")
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preprocessor = layers.Normalization(
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axis=-1, name=f"{name}_preprocessor"
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)
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return Feature(
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dtype="float32", preprocessor=preprocessor, output_mode="float"
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)
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@classmethod
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def float_discretized(
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cls, num_bins, bin_boundaries=None, output_mode="one_hot", name=None
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):
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name = name or auto_name("float_discretized")
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preprocessor = layers.Discretization(
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num_bins=num_bins,
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bin_boundaries=bin_boundaries,
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name=f"{name}_preprocessor",
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)
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return Feature(
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dtype="float32", preprocessor=preprocessor, output_mode=output_mode
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)
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@classmethod
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def integer_categorical(
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cls,
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max_tokens=None,
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num_oov_indices=1,
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output_mode="one_hot",
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name=None,
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):
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name = name or auto_name("integer_categorical")
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preprocessor = layers.IntegerLookup(
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name=f"{name}_preprocessor",
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max_tokens=max_tokens,
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num_oov_indices=num_oov_indices,
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)
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return Feature(
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dtype="int64", preprocessor=preprocessor, output_mode=output_mode
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)
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@classmethod
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def string_categorical(
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cls,
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max_tokens=None,
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num_oov_indices=1,
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output_mode="one_hot",
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name=None,
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):
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name = name or auto_name("string_categorical")
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preprocessor = layers.StringLookup(
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name=f"{name}_preprocessor",
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max_tokens=max_tokens,
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num_oov_indices=num_oov_indices,
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)
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return Feature(
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dtype="string", preprocessor=preprocessor, output_mode=output_mode
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)
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@classmethod
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def string_hashed(cls, num_bins, output_mode="one_hot", name=None):
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name = name or auto_name("string_hashed")
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preprocessor = layers.Hashing(
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name=f"{name}_preprocessor", num_bins=num_bins
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)
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return Feature(
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dtype="string", preprocessor=preprocessor, output_mode=output_mode
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)
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@classmethod
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def integer_hashed(cls, num_bins, output_mode="one_hot", name=None):
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name = name or auto_name("integer_hashed")
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preprocessor = layers.Hashing(
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name=f"{name}_preprocessor", num_bins=num_bins
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)
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return Feature(
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dtype="int64", preprocessor=preprocessor, output_mode=output_mode
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)
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def __init__(
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self,
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features,
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output_mode="concat",
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crosses=None,
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crossing_dim=32,
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hashing_dim=32,
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num_discretization_bins=32,
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name=None,
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):
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super().__init__(name=name)
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if not features:
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raise ValueError("The `features` argument cannot be None or empty.")
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self.crossing_dim = crossing_dim
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self.hashing_dim = hashing_dim
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self.num_discretization_bins = num_discretization_bins
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self.features = {
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name: self._standardize_feature(name, value)
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for name, value in features.items()
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}
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self.crosses = []
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if crosses:
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feature_set = set(features.keys())
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for cross in crosses:
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if isinstance(cross, dict):
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cross = serialization_lib.deserialize_keras_object(cross)
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if isinstance(cross, Cross):
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self.crosses.append(cross)
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else:
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if not crossing_dim:
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raise ValueError(
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"When specifying `crosses`, the argument "
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"`crossing_dim` "
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"(dimensionality of the crossing space) "
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"should be specified as well."
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)
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for key in cross:
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if key not in feature_set:
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raise ValueError(
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"All features referenced "
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"in the `crosses` argument "
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"should be present in the `features` dict. "
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f"Received unknown features: {cross}"
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)
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self.crosses.append(Cross(cross, crossing_dim=crossing_dim))
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self.crosses_by_name = {cross.name: cross for cross in self.crosses}
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if output_mode not in {"dict", "concat"}:
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raise ValueError(
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"Invalid value for argument `output_mode`. "
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"Expected one of {'dict', 'concat'}. "
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f"Received: output_mode={output_mode}"
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)
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self.output_mode = output_mode
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self.inputs = {
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name: self._feature_to_input(name, value)
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for name, value in self.features.items()
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}
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self.preprocessors = {
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name: value.preprocessor for name, value in self.features.items()
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}
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self.encoded_features = None
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self.crossers = {
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cross.name: self._cross_to_crosser(cross) for cross in self.crosses
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}
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self.one_hot_encoders = {}
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self.built = False
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self._is_adapted = False
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self.concat = None
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self._preprocessed_features_names = None
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self._crossed_features_names = None
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def _feature_to_input(self, name, feature):
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return layers.Input(shape=(1,), dtype=feature.dtype, name=name)
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def _standardize_feature(self, name, feature):
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if isinstance(feature, Feature):
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return feature
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if isinstance(feature, dict):
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return serialization_lib.deserialize_keras_object(feature)
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if feature == "float":
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return self.float(name=name)
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elif feature == "float_normalized":
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return self.float_normalized(name=name)
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elif feature == "float_rescaled":
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return self.float_rescaled(name=name)
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elif feature == "float_discretized":
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return self.float_discretized(
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name=name, num_bins=self.num_discretization_bins
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)
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elif feature == "integer_categorical":
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return self.integer_categorical(name=name)
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elif feature == "string_categorical":
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return self.string_categorical(name=name)
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elif feature == "integer_hashed":
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return self.integer_hashed(self.hashing_dim, name=name)
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elif feature == "string_hashed":
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return self.string_hashed(self.hashing_dim, name=name)
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else:
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raise ValueError(f"Invalid feature type: {feature}")
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def _cross_to_crosser(self, cross):
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return layers.HashedCrossing(cross.crossing_dim, name=cross.name)
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def _list_adaptable_preprocessors(self):
|
||||
adaptable_preprocessors = []
|
||||
for name in self.features.keys():
|
||||
preprocessor = self.preprocessors[name]
|
||||
# Special case: a Normalization layer with preset mean/variance.
|
||||
# Not adaptable.
|
||||
if isinstance(preprocessor, layers.Normalization):
|
||||
if preprocessor.input_mean is not None:
|
||||
continue
|
||||
if hasattr(preprocessor, "adapt"):
|
||||
adaptable_preprocessors.append(name)
|
||||
return adaptable_preprocessors
|
||||
|
||||
def adapt(self, dataset):
|
||||
if not isinstance(dataset, tf_data.Dataset):
|
||||
raise ValueError(
|
||||
"`adapt()` can only be called on a tf.data.Dataset. "
|
||||
f"Received instead: {dataset} (of type {type(dataset)})"
|
||||
)
|
||||
|
||||
for name in self._list_adaptable_preprocessors():
|
||||
# Call adapt() on each individual adaptable layer.
|
||||
|
||||
# TODO: consider rewriting this to instead iterate on the
|
||||
# dataset once, split each batch into individual features,
|
||||
# and call the layer's `_adapt_function` on each batch
|
||||
# to simulate the behavior of adapt() in a more performant fashion.
|
||||
|
||||
feature_dataset = dataset.map(lambda x: x[name])
|
||||
preprocessor = self.preprocessors[name]
|
||||
# TODO: consider adding an adapt progress bar.
|
||||
# Sample 1 element to check the rank
|
||||
for x in feature_dataset.take(1):
|
||||
pass
|
||||
if x.shape.rank == 0:
|
||||
# The dataset yields unbatched scalars; batch it.
|
||||
feature_dataset = feature_dataset.batch(32)
|
||||
if x.shape.rank in {0, 1}:
|
||||
# If the rank is 1, add a dimension
|
||||
# so we can reduce on axis=-1.
|
||||
# Note: if rank was previously 0, it is now 1.
|
||||
feature_dataset = feature_dataset.map(
|
||||
lambda x: ops.expand_dims(x, -1)
|
||||
)
|
||||
preprocessor.adapt(feature_dataset)
|
||||
self._is_adapted = True
|
||||
self.get_encoded_features() # Finish building the layer
|
||||
self.built = True
|
||||
|
||||
def get_inputs(self):
|
||||
self._check_if_built()
|
||||
return self.inputs
|
||||
|
||||
def get_encoded_features(self):
|
||||
self._check_if_adapted()
|
||||
|
||||
if self.encoded_features is None:
|
||||
preprocessed_features = self._preprocess_features(self.inputs)
|
||||
crossed_features = self._cross_features(preprocessed_features)
|
||||
merged_features = self._merge_features(
|
||||
preprocessed_features, crossed_features
|
||||
)
|
||||
self.encoded_features = merged_features
|
||||
return self.encoded_features
|
||||
|
||||
def _preprocess_features(self, features):
|
||||
return {
|
||||
name: self.preprocessors[name](features[name])
|
||||
for name in features.keys()
|
||||
}
|
||||
|
||||
def _cross_features(self, features):
|
||||
all_outputs = {}
|
||||
for cross in self.crosses:
|
||||
inputs = [features[name] for name in cross.feature_names]
|
||||
outputs = self.crossers[cross.name](inputs)
|
||||
all_outputs[cross.name] = outputs
|
||||
return all_outputs
|
||||
|
||||
def _merge_features(self, preprocessed_features, crossed_features):
|
||||
if not self._preprocessed_features_names:
|
||||
self._preprocessed_features_names = sorted(
|
||||
preprocessed_features.keys()
|
||||
)
|
||||
self._crossed_features_names = sorted(crossed_features.keys())
|
||||
|
||||
all_names = (
|
||||
self._preprocessed_features_names + self._crossed_features_names
|
||||
)
|
||||
all_features = [
|
||||
preprocessed_features[name]
|
||||
for name in self._preprocessed_features_names
|
||||
] + [crossed_features[name] for name in self._crossed_features_names]
|
||||
|
||||
if self.output_mode == "dict":
|
||||
output_dict = {}
|
||||
else:
|
||||
features_to_concat = []
|
||||
|
||||
if self.built:
|
||||
# Fast mode.
|
||||
for name, feature in zip(all_names, all_features):
|
||||
encoder = self.one_hot_encoders.get(name, None)
|
||||
if encoder:
|
||||
feature = encoder(feature)
|
||||
if self.output_mode == "dict":
|
||||
output_dict[name] = feature
|
||||
else:
|
||||
features_to_concat.append(feature)
|
||||
if self.output_mode == "dict":
|
||||
return output_dict
|
||||
else:
|
||||
return self.concat(features_to_concat)
|
||||
|
||||
# If the object isn't built,
|
||||
# we create the encoder and concat layers below
|
||||
all_specs = [
|
||||
self.features[name] for name in self._preprocessed_features_names
|
||||
] + [
|
||||
self.crosses_by_name[name] for name in self._crossed_features_names
|
||||
]
|
||||
for name, feature, spec in zip(all_names, all_features, all_specs):
|
||||
dtype = feature.dtype
|
||||
|
||||
if spec.output_mode == "one_hot":
|
||||
preprocessor = self.preprocessors.get(
|
||||
name
|
||||
) or self.crossers.get(name)
|
||||
cardinality = None
|
||||
if not feature.dtype.startswith("int"):
|
||||
raise ValueError(
|
||||
f"Feature '{name}' has `output_mode='one_hot'`. "
|
||||
"Thus its preprocessor should return an int64 dtype. "
|
||||
f"Instead it returns a {dtype} dtype."
|
||||
)
|
||||
|
||||
if isinstance(
|
||||
preprocessor, (layers.IntegerLookup, layers.StringLookup)
|
||||
):
|
||||
cardinality = preprocessor.vocabulary_size()
|
||||
elif isinstance(preprocessor, layers.CategoryEncoding):
|
||||
cardinality = preprocessor.num_tokens
|
||||
elif isinstance(preprocessor, layers.Discretization):
|
||||
cardinality = preprocessor.num_bins
|
||||
elif isinstance(
|
||||
preprocessor, (layers.HashedCrossing, layers.Hashing)
|
||||
):
|
||||
cardinality = preprocessor.num_bins
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Feature '{name}' has `output_mode='one_hot'`. "
|
||||
"However it isn't a standard feature and the "
|
||||
"dimensionality of its output space is not known, "
|
||||
"thus it cannot be one-hot encoded. "
|
||||
"Try using `output_mode='int'`."
|
||||
)
|
||||
if cardinality is not None:
|
||||
encoder = layers.CategoryEncoding(
|
||||
num_tokens=cardinality, output_mode="multi_hot"
|
||||
)
|
||||
self.one_hot_encoders[name] = encoder
|
||||
feature = encoder(feature)
|
||||
|
||||
if self.output_mode == "concat":
|
||||
dtype = feature.dtype
|
||||
if dtype.startswith("int") or dtype == "string":
|
||||
raise ValueError(
|
||||
f"Cannot concatenate features because feature '{name}' "
|
||||
f"has not been encoded (it has dtype {dtype}). "
|
||||
"Consider using `output_mode='dict'`."
|
||||
)
|
||||
features_to_concat.append(feature)
|
||||
else:
|
||||
output_dict[name] = feature
|
||||
|
||||
if self.output_mode == "concat":
|
||||
self.concat = layers.Concatenate(axis=-1)
|
||||
return self.concat(features_to_concat)
|
||||
else:
|
||||
return output_dict
|
||||
|
||||
def _check_if_adapted(self):
|
||||
if not self._is_adapted:
|
||||
if not self._list_adaptable_preprocessors():
|
||||
self._is_adapted = True
|
||||
else:
|
||||
raise ValueError(
|
||||
"You need to call `.adapt(dataset)` on the FeatureSpace "
|
||||
"before you can start using it."
|
||||
)
|
||||
|
||||
def _check_if_built(self):
|
||||
if not self.built:
|
||||
self._check_if_adapted()
|
||||
# Finishes building
|
||||
self.get_encoded_features()
|
||||
self.built = True
|
||||
|
||||
def __call__(self, data):
|
||||
self._check_if_built()
|
||||
if not isinstance(data, dict):
|
||||
raise ValueError(
|
||||
"A FeatureSpace can only be called with a dict. "
|
||||
f"Received: data={data} (of type {type(data)}"
|
||||
)
|
||||
|
||||
data = {
|
||||
key: ops.convert_to_tensor(value) for key, value in data.items()
|
||||
}
|
||||
rebatched = False
|
||||
for name, x in data.items():
|
||||
if x.shape.rank == 0:
|
||||
data[name] = ops.reshape(x, (1, 1))
|
||||
rebatched = True
|
||||
elif x.shape.rank == 1:
|
||||
data[name] = ops.expand_dims(x, -1)
|
||||
|
||||
preprocessed_data = self._preprocess_features(data)
|
||||
crossed_data = self._cross_features(preprocessed_data)
|
||||
merged_data = self._merge_features(preprocessed_data, crossed_data)
|
||||
if rebatched:
|
||||
if self.output_mode == "concat":
|
||||
assert merged_data.shape[0] == 1
|
||||
return ops.squeeze(merged_data, axis=0)
|
||||
else:
|
||||
for name, x in merged_data.items():
|
||||
if x.shape.rank == 2 and x.shape[0] == 1:
|
||||
merged_data[name] = ops.squeeze(x, axis=0)
|
||||
return merged_data
|
||||
|
||||
def get_config(self):
|
||||
return {
|
||||
"features": serialization_lib.serialize_keras_object(self.features),
|
||||
"output_mode": self.output_mode,
|
||||
"crosses": serialization_lib.serialize_keras_object(self.crosses),
|
||||
"crossing_dim": self.crossing_dim,
|
||||
"hashing_dim": self.hashing_dim,
|
||||
"num_discretization_bins": self.num_discretization_bins,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_config(cls, config):
|
||||
return cls(**config)
|
||||
|
||||
def get_build_config(self):
|
||||
return {
|
||||
name: feature.preprocessor.get_build_config()
|
||||
for name, feature in self.features.items()
|
||||
}
|
||||
|
||||
def build_from_config(self, config):
|
||||
for name in config.keys():
|
||||
self.features[name].preprocessor.build_from_config(config[name])
|
||||
self._is_adapted = True
|
||||
|
||||
def save(self, filepath):
|
||||
"""Save the `FeatureSpace` instance to a `.keras` file.
|
||||
|
||||
You can reload it via `keras.models.load_model()`:
|
||||
|
||||
```python
|
||||
feature_space.save("myfeaturespace.keras")
|
||||
reloaded_feature_space = keras.models.load_model("myfeaturespace.keras")
|
||||
```
|
||||
"""
|
||||
saving_lib.save_model(self, filepath)
|
||||
|
||||
def save_own_variables(self, store):
|
||||
return
|
||||
|
||||
def load_own_variables(self, store):
|
||||
return
|
||||
@ -1,378 +0,0 @@
|
||||
# from keras_core import testing
|
||||
# from keras_core.utils import feature_space
|
||||
# from keras_core import operations as ops
|
||||
# from tensorflow import nest
|
||||
# from tensorflow import data as tf_data
|
||||
# from keras_core import layers
|
||||
# from keras_core import models
|
||||
# import os
|
||||
|
||||
|
||||
# class FeatureSpaceTest(testing.TestCase):
|
||||
# def _get_train_data_dict(
|
||||
# self, as_dataset=False, as_tf_tensors=False, as_labeled_dataset=False
|
||||
# ):
|
||||
# data = {
|
||||
# "float_1": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
|
||||
# "float_2": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
|
||||
# "float_3": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
|
||||
# "string_1": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
|
||||
# "string_2": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
|
||||
# "int_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
|
||||
# "int_2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
|
||||
# "int_3": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
|
||||
# }
|
||||
# if as_dataset:
|
||||
# return tf_data.Dataset.from_tensor_slices(data)
|
||||
# elif as_tf_tensors:
|
||||
# return nest.map_structure(ops.convert_to_tensor, data)
|
||||
# elif as_labeled_dataset:
|
||||
# labels = [0, 1, 0, 1, 0, 0, 1, 0, 1, 1]
|
||||
# return tf_data.Dataset.from_tensor_slices((data, labels))
|
||||
# return data
|
||||
|
||||
# def test_basic_usage(self):
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "float_1": "float",
|
||||
# "float_2": "float_normalized",
|
||||
# "float_3": "float_discretized",
|
||||
# "string_1": "string_categorical",
|
||||
# "string_2": "string_hashed",
|
||||
# "int_1": "integer_categorical",
|
||||
# "int_2": "integer_hashed",
|
||||
# "int_3": "integer_categorical",
|
||||
# },
|
||||
# crosses=[("float_3", "string_1"), ("string_2", "int_2")],
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# # Test unbatched adapt
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
# # Test batched adapt
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True).batch(4))
|
||||
|
||||
# # Test unbatched call on raw data
|
||||
# data = {
|
||||
# key: value[0] for key, value in self._get_train_data_dict().items()
|
||||
# }
|
||||
# out = fs(data)
|
||||
# self.assertEqual(out.shape, [195])
|
||||
|
||||
# # Test unbatched call on TF tensors
|
||||
# data = self._get_train_data_dict(as_tf_tensors=True)
|
||||
# data = {key: value[0] for key, value in data.items()}
|
||||
# out = fs(data)
|
||||
# self.assertEqual(out.shape, [195])
|
||||
|
||||
# # Test batched call on raw data
|
||||
# out = fs(self._get_train_data_dict())
|
||||
# self.assertEqual(out.shape, [10, 195])
|
||||
|
||||
# # Test batched call on TF tensors
|
||||
# out = fs(self._get_train_data_dict(as_tf_tensors=True))
|
||||
# self.assertEqual(out.shape, [10, 195])
|
||||
|
||||
# def test_output_mode_dict(self):
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "float_1": "float",
|
||||
# "float_2": "float_normalized",
|
||||
# "float_3": "float_discretized",
|
||||
# "string_1": "string_categorical",
|
||||
# "string_2": "string_hashed",
|
||||
# "int_1": "integer_categorical",
|
||||
# "int_2": "integer_hashed",
|
||||
# "int_3": "integer_categorical",
|
||||
# },
|
||||
# crosses=[("float_3", "string_1"), ("string_2", "int_2")],
|
||||
# output_mode="dict",
|
||||
# )
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
|
||||
# # Test unbatched call on raw data
|
||||
# data = {
|
||||
# key: value[0] for key, value in self._get_train_data_dict().items()
|
||||
# }
|
||||
# out = fs(data)
|
||||
# self.assertIsInstance(out, dict)
|
||||
# self.assertLen(out, 10)
|
||||
# self.assertEqual(out["string_1"].shape, [11])
|
||||
# self.assertEqual(out["int_2"].shape, [32])
|
||||
# self.assertEqual(out["string_2_X_int_2"].shape, [32])
|
||||
|
||||
# # Test batched call on raw data
|
||||
# out = fs(self._get_train_data_dict())
|
||||
# self.assertIsInstance(out, dict)
|
||||
# self.assertLen(out, 10)
|
||||
# self.assertEqual(out["string_1"].shape, [10, 11])
|
||||
# self.assertEqual(out["int_2"].shape, [10, 32])
|
||||
# self.assertEqual(out["string_2_X_int_2"].shape, [10, 32])
|
||||
|
||||
# # Test batched call on TF tensors
|
||||
# out = fs(self._get_train_data_dict(as_tf_tensors=True))
|
||||
# self.assertIsInstance(out, dict)
|
||||
# self.assertLen(out, 10)
|
||||
# self.assertEqual(out["string_1"].shape, [10, 11])
|
||||
# self.assertEqual(out["int_2"].shape, [10, 32])
|
||||
# self.assertEqual(out["string_2_X_int_2"].shape, [10, 32])
|
||||
|
||||
# def test_output_mode_dict_of_ints(self):
|
||||
# cls = feature_space.FeatureSpace
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "float_1": "float",
|
||||
# "float_2": "float_normalized",
|
||||
# "float_3": "float_discretized",
|
||||
# "string_1": cls.string_categorical(output_mode="int"),
|
||||
# "string_2": cls.string_hashed(num_bins=32, output_mode="int"),
|
||||
# "int_1": cls.integer_categorical(output_mode="int"),
|
||||
# "int_2": cls.integer_hashed(num_bins=32, output_mode="int"),
|
||||
# "int_3": cls.integer_categorical(output_mode="int"),
|
||||
# },
|
||||
# crosses=[
|
||||
# cls.cross(
|
||||
# ("float_3", "string_1"), output_mode="int", crossing_dim=32
|
||||
# ),
|
||||
# cls.cross(
|
||||
# ("string_2", "int_2"), output_mode="int", crossing_dim=32
|
||||
# ),
|
||||
# ],
|
||||
# output_mode="dict",
|
||||
# )
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
# data = {
|
||||
# key: value[0] for key, value in self._get_train_data_dict().items()
|
||||
# }
|
||||
# out = fs(data)
|
||||
# self.assertIsInstance(out, dict)
|
||||
# self.assertLen(out, 10)
|
||||
# self.assertEqual(out["string_1"].shape, [1])
|
||||
# self.assertEqual(out["string_1"].dtype.name, "int64")
|
||||
# self.assertEqual(out["int_2"].shape, [1])
|
||||
# self.assertEqual(out["int_2"].dtype.name, "int64")
|
||||
# self.assertEqual(out["string_2_X_int_2"].shape, [1])
|
||||
# self.assertEqual(out["string_2_X_int_2"].dtype.name, "int64")
|
||||
|
||||
# def test_functional_api_sync_processing(self):
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "float_1": "float",
|
||||
# "float_2": "float_normalized",
|
||||
# "float_3": "float_discretized",
|
||||
# "string_1": "string_categorical",
|
||||
# "string_2": "string_hashed",
|
||||
# "int_1": "integer_categorical",
|
||||
# "int_2": "integer_hashed",
|
||||
# "int_3": "integer_categorical",
|
||||
# },
|
||||
# crosses=[("float_3", "string_1"), ("string_2", "int_2")],
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
# inputs = fs.get_inputs()
|
||||
# features = fs.get_encoded_features()
|
||||
# outputs = layers.Dense(1)(features)
|
||||
# model = models.Model(inputs=inputs, outputs=outputs)
|
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# model.compile("adam", "mse")
|
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# ds = self._get_train_data_dict(as_labeled_dataset=True)
|
||||
# model.fit(ds.batch(4))
|
||||
# model.evaluate(ds.batch(4))
|
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# ds = self._get_train_data_dict(as_dataset=True)
|
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# model.predict(ds.batch(4))
|
||||
|
||||
# def test_tf_data_async_processing(self):
|
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# fs = feature_space.FeatureSpace(
|
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# features={
|
||||
# "float_1": "float",
|
||||
# "float_2": "float_normalized",
|
||||
# "float_3": "float_discretized",
|
||||
# "string_1": "string_categorical",
|
||||
# "string_2": "string_hashed",
|
||||
# "int_1": "integer_categorical",
|
||||
# "int_2": "integer_hashed",
|
||||
# "int_3": "integer_categorical",
|
||||
# },
|
||||
# crosses=[("float_3", "string_1"), ("string_2", "int_2")],
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
# features = fs.get_encoded_features()
|
||||
# outputs = layers.Dense(1)(features)
|
||||
# model = models.Model(inputs=features, outputs=outputs)
|
||||
# model.compile("adam", "mse")
|
||||
# ds = self._get_train_data_dict(as_labeled_dataset=True)
|
||||
# # Try map before batch
|
||||
# ds = ds.map(lambda x, y: (fs(x), y))
|
||||
# model.fit(ds.batch(4))
|
||||
# # Try map after batch
|
||||
# ds = self._get_train_data_dict(as_labeled_dataset=True)
|
||||
# ds = ds.batch(4)
|
||||
# ds = ds.map(lambda x, y: (fs(x), y))
|
||||
# model.evaluate(ds)
|
||||
# ds = self._get_train_data_dict(as_dataset=True)
|
||||
# ds = ds.map(fs)
|
||||
# model.predict(ds.batch(4))
|
||||
|
||||
# def test_advanced_usage(self):
|
||||
# cls = feature_space.FeatureSpace
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "float_1": cls.float(),
|
||||
# "float_2": cls.float_normalized(),
|
||||
# "float_3": cls.float_discretized(num_bins=3),
|
||||
# "string_1": cls.string_categorical(max_tokens=5),
|
||||
# "string_2": cls.string_hashed(num_bins=32),
|
||||
# "int_1": cls.integer_categorical(
|
||||
# max_tokens=5, num_oov_indices=2
|
||||
# ),
|
||||
# "int_2": cls.integer_hashed(num_bins=32),
|
||||
# "int_3": cls.integer_categorical(max_tokens=5),
|
||||
# },
|
||||
# crosses=[
|
||||
# cls.cross(("float_3", "string_1"), crossing_dim=32),
|
||||
# cls.cross(("string_2", "int_2"), crossing_dim=32),
|
||||
# ],
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
# data = {
|
||||
# key: value[0] for key, value in self._get_train_data_dict().items()
|
||||
# }
|
||||
# out = fs(data)
|
||||
# self.assertEqual(out.shape, [148])
|
||||
|
||||
# def test_manual_kpl(self):
|
||||
# data = {
|
||||
# "text": ["1st string", "2nd string", "3rd string"],
|
||||
# }
|
||||
# cls = feature_space.FeatureSpace
|
||||
|
||||
# # Test with a tf-idf TextVectorization layer
|
||||
# tv = layers.TextVectorization(output_mode="tf_idf")
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "text": cls.feature(
|
||||
# preprocessor=tv, dtype="string", output_mode="float"
|
||||
# ),
|
||||
# },
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# fs.adapt(tf_data.Dataset.from_tensor_slices(data))
|
||||
# out = fs(data)
|
||||
# self.assertEqual(out.shape, [3, 5])
|
||||
|
||||
# def test_no_adapt(self):
|
||||
# data = {
|
||||
# "int_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
|
||||
# }
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# {
|
||||
# "int_1": "integer_hashed",
|
||||
# },
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# out = fs(data)
|
||||
# self.assertEqual(out.shape, [10, 32])
|
||||
|
||||
# def test_saving(self):
|
||||
# cls = feature_space.FeatureSpace
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "float_1": cls.float(),
|
||||
# "float_2": cls.float_normalized(),
|
||||
# "float_3": cls.float_discretized(num_bins=3),
|
||||
# "string_1": cls.string_categorical(max_tokens=5),
|
||||
# "string_2": cls.string_hashed(num_bins=32),
|
||||
# "int_1": cls.integer_categorical(
|
||||
# max_tokens=5, num_oov_indices=2
|
||||
# ),
|
||||
# "int_2": cls.integer_hashed(num_bins=32),
|
||||
# "int_3": cls.integer_categorical(max_tokens=5),
|
||||
# },
|
||||
# crosses=[
|
||||
# cls.cross(("float_3", "string_1"), crossing_dim=32),
|
||||
# cls.cross(("string_2", "int_2"), crossing_dim=32),
|
||||
# ],
|
||||
# output_mode="concat",
|
||||
# )
|
||||
# fs.adapt(self._get_train_data_dict(as_dataset=True))
|
||||
# data = {
|
||||
# key: value[0] for key, value in self._get_train_data_dict().items()
|
||||
# }
|
||||
# ref_out = fs(data)
|
||||
|
||||
# temp_filepath = os.path.join(self.get_temp_dir(), "fs.keras")
|
||||
# fs.save(temp_filepath)
|
||||
# fs = models.models.load_model(temp_filepath)
|
||||
|
||||
# # Save again immediately after loading to test idempotency
|
||||
# temp_filepath = os.path.join(self.get_temp_dir(), "fs2.keras")
|
||||
# fs.save(temp_filepath)
|
||||
|
||||
# # Test correctness of the first saved FS
|
||||
# out = fs(data)
|
||||
# self.assertAllClose(out, ref_out)
|
||||
|
||||
# inputs = fs.get_inputs()
|
||||
# outputs = fs.get_encoded_features()
|
||||
# model = models.Model(inputs=inputs, outputs=outputs)
|
||||
# ds = self._get_train_data_dict(as_dataset=True)
|
||||
# out = model.predict(ds.batch(4))
|
||||
# self.assertAllClose(out[0], ref_out)
|
||||
|
||||
# # Test correctness of the re-saved FS
|
||||
# fs = models.models.load_model(temp_filepath)
|
||||
# out = fs(data)
|
||||
# self.assertAllClose(out, ref_out)
|
||||
|
||||
# def test_errors(self):
|
||||
# # Test no features
|
||||
# with self.assertRaisesRegex(ValueError, "cannot be None or empty"):
|
||||
# feature_space.FeatureSpace(features={})
|
||||
# # Test no crossing dim
|
||||
# with self.assertRaisesRegex(ValueError, "`crossing_dim`"):
|
||||
# feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "f1": "integer_categorical",
|
||||
# "f2": "integer_categorical",
|
||||
# },
|
||||
# crosses=[("f1", "f2")],
|
||||
# crossing_dim=None,
|
||||
# )
|
||||
# # Test wrong cross feature name
|
||||
# with self.assertRaisesRegex(ValueError, "should be present in "):
|
||||
# feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "f1": "integer_categorical",
|
||||
# "f2": "integer_categorical",
|
||||
# },
|
||||
# crosses=[("f1", "unknown")],
|
||||
# crossing_dim=32,
|
||||
# )
|
||||
# # Test wrong output mode
|
||||
# with self.assertRaisesRegex(ValueError, "for argument `output_mode`"):
|
||||
# feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "f1": "integer_categorical",
|
||||
# "f2": "integer_categorical",
|
||||
# },
|
||||
# output_mode="unknown",
|
||||
# )
|
||||
# # Test call before adapt
|
||||
# with self.assertRaisesRegex(ValueError, "You need to call `.adapt"):
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "f1": "integer_categorical",
|
||||
# "f2": "integer_categorical",
|
||||
# }
|
||||
# )
|
||||
# fs({"f1": [0], "f2": [0]})
|
||||
# # Test get_encoded_features before adapt
|
||||
# with self.assertRaisesRegex(ValueError, "You need to call `.adapt"):
|
||||
# fs = feature_space.FeatureSpace(
|
||||
# features={
|
||||
# "f1": "integer_categorical",
|
||||
# "f2": "integer_categorical",
|
||||
# }
|
||||
# )
|
||||
# fs.get_encoded_features()
|
||||
Loading…
Reference in New Issue
Block a user