jhfjhfj1/autokeras

"""shell
pip install autokeras
"""

import os

import numpy as np
import tensorflow as tf
from sklearn.datasets import load_files

import autokeras as ak

"""
To make this tutorial easy to follow, we just treat IMDB dataset as a
regression dataset. It means we will treat prediction targets of IMDB dataset,
which are 0s and 1s as numerical values, so that they can be directly used as
the regression targets.

## A Simple Example
The first step is to prepare your data. Here we use the [IMDB
dataset](https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification)
as an example.
"""


dataset = tf.keras.utils.get_file(
    fname="aclImdb.tar.gz",
    origin="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",
    extract=True,
)

# set path to dataset
IMDB_DATADIR = os.path.join(os.path.dirname(dataset), "aclImdb")

classes = ["pos", "neg"]
train_data = load_files(
    os.path.join(IMDB_DATADIR, "train"), shuffle=True, categories=classes
)
test_data = load_files(
    os.path.join(IMDB_DATADIR, "test"), shuffle=False, categories=classes
)

x_train = np.array(train_data.data)[:100]
y_train = np.array(train_data.target)[:100]
x_test = np.array(test_data.data)[:100]
y_test = np.array(test_data.target)[:100]

print(x_train.shape)  # (25000,)
print(y_train.shape)  # (25000, 1)
print(x_train[0][:50])  # <START> this film was just brilliant casting <UNK>

"""
The second step is to run the [TextRegressor](/text_regressor).  As a quick
demo, we set epochs to 2.  You can also leave the epochs unspecified for an
adaptive number of epochs.
"""


# Initialize the text regressor.
reg = ak.TextRegressor(
    overwrite=True, max_trials=1  # It tries 10 different models.
)
# Feed the text regressor with training data.
reg.fit(x_train, y_train, epochs=1, batch_size=2)
# Predict with the best model.
predicted_y = reg.predict(x_test)
# Evaluate the best model with testing data.
print(reg.evaluate(x_test, y_test))


"""
## Validation Data
By default, AutoKeras use the last 20% of training data as validation data.  As
shown in the example below, you can use `validation_split` to specify the
percentage.
"""

reg.fit(
    x_train,
    y_train,
    # Split the training data and use the last 15% as validation data.
    validation_split=0.15,
)

"""
You can also use your own validation set instead of splitting it from the
training data with `validation_data`.
"""

split = 5
x_val = x_train[split:]
y_val = y_train[split:]
x_train = x_train[:split]
y_train = y_train[:split]
reg.fit(
    x_train,
    y_train,
    epochs=1,
    # Use your own validation set.
    validation_data=(x_val, y_val),
    batch_size=2,
)

"""
## Customized Search Space
For advanced users, you may customize your search space by using
[AutoModel](/auto_model/#automodel-class) instead of
[TextRegressor](/text_regressor). You can configure the
[TextBlock](/block/#textblock-class) for some high-level configurations. You can
also do not specify these arguments, which would leave the different choices to
be tuned automatically.  See the following example for detail.
"""


input_node = ak.TextInput()
output_node = ak.TextBlock()(input_node)
output_node = ak.RegressionHead()(output_node)
reg = ak.AutoModel(
    inputs=input_node, outputs=output_node, overwrite=True, max_trials=1
)
reg.fit(x_train, y_train, epochs=1, batch_size=2)

"""
## Data Format
The AutoKeras TextRegressor is quite flexible for the data format.

For the text, the input data should be one-dimensional For the regression
targets, it should be a vector of numerical values.  AutoKeras accepts
numpy.ndarray.

We also support using [tf.data.Dataset](
https://www.tensorflow.org/api_docs/python/tf/data/Dataset?version=stable)
format for the training data.
"""


train_set = tf.data.Dataset.from_tensor_slices(((x_train,), (y_train,))).batch(
    2
)
test_set = tf.data.Dataset.from_tensor_slices(((x_test,), (y_test,))).batch(2)

reg = ak.TextRegressor(overwrite=True, max_trials=2)
# Feed the tensorflow Dataset to the regressor.
reg.fit(train_set.take(2), epochs=1)
# Predict with the best model.
predicted_y = reg.predict(test_set.take(2))
# Evaluate the best model with testing data.
print(reg.evaluate(test_set.take(2)))

"""
## Reference
[TextRegressor](/text_regressor),
[AutoModel](/auto_model/#automodel-class),
[TextBlock](/block/#textblock-class),
[ConvBlock](/block/#convblock-class),
[TextInput](/node/#textinput-class),
[RegressionHead](/block/#regressionhead-class).
"""