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text-class-ham-or-spam.py
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text-class-ham-or-spam.py
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# Imports
import tensorflow_text
import tensorflow as tf
import tensorflow_hub as hub
import pandas as pd
import numpy as np
# from multiprocessing import Pool # , Process
from cerebros.simplecerebrosrandomsearch.simple_cerebros_random_search\
import SimpleCerebrosRandomSearch
import pendulum
from cerebros.units.units import DenseUnit
from cerebros.denseautomlstructuralcomponent.dense_automl_structural_component\
import zero_7_exp_decay, zero_95_exp_decay, simple_sigmoid
from ast import literal_eval
### Global configurables:
# Training data (CSV) having text in one column and a binary label in another
data_file = "SMSCollection.csv"
# Column with a binary label
prediction_target_column = "Class"
# Class in this column that will be considered a "yes" or a positive
# example (e.g. This sample detects "spam messages", and those labeled "spam"
# will be consisered "yes" or a positive detection)
positive_class_label = "spam" # Label
# How many of the samples in the data set to actually use on this training run
number_of_samples_to_use = 200
# Cerebros configurables:
activation = 'gelu'
predecessor_level_connection_affinity_factor_first = 49.9999
predecessor_level_connection_affinity_factor_main = 0.31456
max_consecutive_lateral_connections = 22
p_lateral_connection = 0.39256
num_lateral_connection_tries_per_unit = 10
learning_rate = 0.0000511065
epochs = 6 # [1, 100]
batch_size = 13
maximum_levels = 4 # [3,7]
maximum_units_per_level = 8 # [2,10]
maximum_neurons_per_unit = 5 # [2,20]
# Build BERT base model
# text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
# preprocessor = hub.KerasLayer(
# "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3")
# encoder_inputs = preprocessor(text_input)
###
# preprocessor = hub.load(
# "https://www.kaggle.com/models/tensorflow/bert/TensorFlow2/en-uncased-preprocess/3")
# inp = tf.keras.layers.Input(shape=(), dtype=tf.string)
# text_inputs = [inp]
# tokenize = hub.KerasLayer(preprocessor.tokenize)
# tokenized_inputs = [tokenize(segment) for segment in text_inputs]
# seq_length = 128 # Your choice here.
# bert_pack_inputs = hub.KerasLayer(
# preprocessor.bert_pack_inputs,
# arguments=dict(seq_length=seq_length)) # Optional argument.
# encoder_inputs = bert_pack_inputs(tokenized_inputs)
# ###
# encoder = hub.KerasLayer(
# "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4",
# trainable=True)
# outputs = encoder(encoder_inputs)
# pooled_output = outputs["pooled_output"] # [batch_size, 768].
# sequence_output = outputs["sequence_output"] # [batch_size, seq_length, 768].
# embedding_model = tf.keras.Model(inp, pooled_output)
###
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = hub.KerasLayer(
"https://kaggle.com/models/tensorflow/bert/TensorFlow2/en-uncased-preprocess/3")
encoder_inputs = preprocessor(text_input)
encoder = hub.KerasLayer(
"https://www.kaggle.com/models/tensorflow/bert/TensorFlow2/bert-en-uncased-l-10-h-128-a-2/2",
trainable=True)
outputs = encoder(encoder_inputs)
pooled_output = outputs["pooled_output"] # [batch_size, 128].
sequence_output = outputs["sequence_output"] # [batch_size, seq_length, 128].
embedding_model = tf.keras.Model(text_input, pooled_output)
###
## Load the Data set
raw_text = pd.read_csv(data_file, dtype='object')
raw_text = raw_text.iloc[:number_of_samples_to_use, :]
# One hot encode the label
raw_text[prediction_target_column] =\
raw_text[prediction_target_column]\
.apply(lambda x: 1 if x == positive_class_label else 0)
labels = raw_text.pop(prediction_target_column)
labels = labels.values
data = raw_text.values
labels_tensor = tf.constant(labels, dtype=tf.float32)
data_tensor = tf.constant(data, dtype=tf.string)
TIME = pendulum.now(tz='America/New_York').__str__()[:16]\
.replace('T', '_')\
.replace(':', '_')\
.replace('-', '_')
PROJECT_NAME = f'{TIME}_cerebros_auto_ml_test'
INPUT_SHAPES = [()]
# Cerebros parameters:
training_x = [data_tensor]
train_labels = [labels_tensor]
OUTPUT_SHAPES = [1]
meta_trial_number = str(int(np.random.random() * 10 ** 12))
cerebros_automl = SimpleCerebrosRandomSearch(
unit_type=DenseUnit,
input_shapes=INPUT_SHAPES,
output_shapes=OUTPUT_SHAPES,
training_data=training_x,
labels=train_labels,
validation_split=0.35,
direction='maximize',
metric_to_rank_by="val_binary_accuracy",
minimum_levels=2,
maximum_levels=maximum_levels,
minimum_units_per_level=1,
maximum_units_per_level=maximum_units_per_level,
minimum_neurons_per_unit=1,
maximum_neurons_per_unit=maximum_neurons_per_unit,
activation=activation,
final_activation='sigmoid',
number_of_architecture_moities_to_try=2,
number_of_tries_per_architecture_moity=1,
minimum_skip_connection_depth=1,
maximum_skip_connection_depth=7,
predecessor_level_connection_affinity_factor_first=predecessor_level_connection_affinity_factor_first,
predecessor_level_connection_affinity_factor_first_rounding_rule='ceil',
predecessor_level_connection_affinity_factor_main=predecessor_level_connection_affinity_factor_main,
predecessor_level_connection_affinity_factor_main_rounding_rule='ceil',
predecessor_level_connection_affinity_factor_decay_main=zero_7_exp_decay,
seed=8675309,
max_consecutive_lateral_connections=max_consecutive_lateral_connections,
gate_after_n_lateral_connections=3,
gate_activation_function=simple_sigmoid,
p_lateral_connection=p_lateral_connection,
p_lateral_connection_decay=zero_95_exp_decay,
num_lateral_connection_tries_per_unit=num_lateral_connection_tries_per_unit,
learning_rate=learning_rate,
loss=tf.keras.losses.BinaryCrossentropy(),
metrics=[tf.keras.metrics.BinaryAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall()],
epochs=epochs,
project_name=f"{PROJECT_NAME}_meta_{meta_trial_number}",
# use_multiprocessing_for_multiple_neural_networks=False, # pull this param
model_graphs='model_graphs',
batch_size=batch_size,
meta_trial_number=meta_trial_number,
base_models=[embedding_model],
train_data_dtype=tf.string)
val_binary_accuracy = cerebros_automl.run_random_search()
print(val_binary_accuracy)