import os

from google.cloud import bigquery
import pandas as pd

%load_ext google.cloud.bigquery

"""
Explanation: Keras for Text Classification
Learning Objectives

Learn how to create a text classification datasets using BigQuery.
Learn how to tokenize and integerize a corpus of text for training in Keras.
Learn how to do one-hot-encodings in Keras.
Learn how to use embedding layers to represent words in Keras.
Learn about the bag-of-word representation for sentences.
Learn how to use DNN/CNN/RNN model to classify text in keras.

Introduction
In this notebook, we will implement text models to recognize the probable source (Github, Tech-Crunch, or The New-York Times) of the titles we have in the title dataset we constructed in the first task of the lab.
In the next step, we will load and pre-process the texts and labels so that they are suitable to be fed to a Keras model. For the texts of the titles we will learn how to split them into a list of tokens, and then how to map each token to an integer using the Keras Tokenizer class. What will be fed to our Keras models will be batches of padded list of integers representing the text. For the labels, we will learn how to one-hot-encode each of the 3 classes into a 3 dimensional basis vector.
Then we will explore a few possible models to do the title classification. All models will be fed padded list of integers, and all models will start with a Keras Embedding layer that transforms the integer representing the words into dense vectors.
The first model will be a simple bag-of-word DNN model that averages up the word vectors and feeds the tensor that results to further dense layers. Doing so means that we forget the word order (and hence that we consider sentences as a “bag-of-words”). In the second and in the third model we will keep the information about the word order using a simple RNN and a simple CNN allowing us to achieve the same performance as with the DNN model but in much fewer epochs.
Each learning objective will correspond to a #TODO in the student lab notebook -- try to complete that notebook first before reviewing this solution notebook.
End of explanation
"""


PROJECT = "cloud-training-demos"  # Replace with your PROJECT
BUCKET = PROJECT  # defaults to PROJECT
REGION = "us-central1"  # Replace with your REGION
SEED = 0

"""
Explanation: Replace the variable values in the cell below:
End of explanation
"""


%%bigquery --project $PROJECT

SELECT
    url, title, score
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    LENGTH(title) > 10
    AND score > 10
    AND LENGTH(url) > 0
LIMIT 10

"""
Explanation: Create a Dataset from BigQuery
Hacker news headlines are available as a BigQuery public dataset. The dataset contains all headlines from the sites inception in October 2006 until October 2015. 
Here is a sample of the dataset:
End of explanation
"""


%%bigquery --project $PROJECT

SELECT
    ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.'))[OFFSET(1)] AS source,
    COUNT(title) AS num_articles
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    REGEXP_CONTAINS(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.com$')
    AND LENGTH(title) > 10
GROUP BY
    source
ORDER BY num_articles DESC
  LIMIT 100

"""
Explanation: Let's do some regular expression parsing in BigQuery to get the source of the newspaper article from the URL. For example, if the url is http://mobile.nytimes.com/...., I want to be left with <i>nytimes</i>
End of explanation
"""


regex = '.*://(.[^/]+)/'


sub_query = """
SELECT
    title,
    ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '{0}'), '.'))[OFFSET(1)] AS source
    
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    REGEXP_CONTAINS(REGEXP_EXTRACT(url, '{0}'), '.com$')
    AND LENGTH(title) > 10
""".format(regex)


query = """
SELECT 
    LOWER(REGEXP_REPLACE(title, '[^a-zA-Z0-9 $.-]', ' ')) AS title,
    source
FROM
  ({sub_query})
WHERE (source = 'github' OR source = 'nytimes' OR source = 'techcrunch')
""".format(sub_query=sub_query)

print(query)

"""
Explanation: Now that we have good parsing of the URL to get the source, let's put together a dataset of source and titles. This will be our labeled dataset for machine learning.
End of explanation
"""


bq = bigquery.Client(project=PROJECT)
title_dataset = bq.query(query).to_dataframe()
title_dataset.head()

"""
Explanation: For ML training, we usually need to split our dataset into training and evaluation datasets (and perhaps an independent test dataset if we are going to do model or feature selection based on the evaluation dataset). AutoML however figures out on its own how to create these splits, so we won't need to do that here.
End of explanation
"""


print("The full dataset contains {n} titles".format(n=len(title_dataset)))

"""
Explanation: AutoML for text classification requires that
* the dataset be in csv form with 
* the first column being the texts to classify or a GCS path to the text 
* the last colum to be the text labels
The dataset we pulled from BiqQuery satisfies these requirements.
End of explanation
"""


title_dataset.source.value_counts()

"""
Explanation: Let's make sure we have roughly the same number of labels for each of our three labels:
End of explanation
"""


DATADIR = './data/'

if not os.path.exists(DATADIR):
    os.makedirs(DATADIR)

FULL_DATASET_NAME = 'titles_full.csv'
FULL_DATASET_PATH = os.path.join(DATADIR, FULL_DATASET_NAME)

# Let's shuffle the data before writing it to disk.
title_dataset = title_dataset.sample(n=len(title_dataset))

title_dataset.to_csv(
    FULL_DATASET_PATH, header=False, index=False, encoding='utf-8')

"""
Explanation: Finally we will save our data, which is currently in-memory, to disk.
We will create a csv file containing the full dataset and another containing only 1000 articles for development.
Note: It may take a long time to train AutoML on the full dataset, so we recommend to use the sample dataset for the purpose of learning the tool.
End of explanation
"""


sample_title_dataset = title_dataset.sample(n=1000)
sample_title_dataset.source.value_counts()

"""
Explanation: Now let's sample 1000 articles from the full dataset and make sure we have enough examples for each label in our sample dataset (see here for further details on how to prepare data for AutoML).
End of explanation
"""


SAMPLE_DATASET_NAME = 'titles_sample.csv'
SAMPLE_DATASET_PATH = os.path.join(DATADIR, SAMPLE_DATASET_NAME)

sample_title_dataset.to_csv(
    SAMPLE_DATASET_PATH, header=False, index=False, encoding='utf-8')

sample_title_dataset.head()

import os
import shutil

import pandas as pd
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard, EarlyStopping
from tensorflow.keras.layers import (
    Embedding,
    Flatten,
    GRU,
    Conv1D,
    Lambda,
    Dense,
)
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical


print(tf.__version__)

%matplotlib inline

"""
Explanation: Let's write the sample datatset to disk.
End of explanation
"""


LOGDIR = "./text_models"
DATA_DIR = "./data"

"""
Explanation: Let's start by specifying where the information about the trained models will be saved as well as where our dataset is located:
End of explanation
"""


DATASET_NAME = "titles_full.csv"
TITLE_SAMPLE_PATH = os.path.join(DATA_DIR, DATASET_NAME)
COLUMNS = ['title', 'source']

titles_df = pd.read_csv(TITLE_SAMPLE_PATH, header=None, names=COLUMNS)
titles_df.head()

"""
Explanation: Loading the dataset
Our dataset consists of titles of articles along with the label indicating from which source these articles have been taken from (GitHub, Tech-Crunch, or the New-York Times).
End of explanation
"""


tokenizer = Tokenizer()
tokenizer.fit_on_texts(titles_df.title)

integerized_titles = tokenizer.texts_to_sequences(titles_df.title)
integerized_titles[:3]

VOCAB_SIZE = len(tokenizer.index_word)
VOCAB_SIZE

DATASET_SIZE = tokenizer.document_count
DATASET_SIZE

MAX_LEN = max(len(sequence) for sequence in integerized_titles)
MAX_LEN

"""
Explanation: Integerize the texts
The first thing we need to do is to find how many words we have in our dataset (VOCAB_SIZE), how many titles we have (DATASET_SIZE), and what the maximum length of the titles we have (MAX_LEN) is. Keras offers the Tokenizer class in its keras.preprocessing.text module to help us with that:
End of explanation
"""


# TODO 1
def create_sequences(texts, max_len=MAX_LEN):
    sequences = tokenizer.texts_to_sequences(texts)
    padded_sequences = pad_sequences(sequences, max_len, padding='post')
    return padded_sequences

sequences = create_sequences(titles_df.title[:3])
sequences

titles_df.source[:4]

"""
Explanation: Let's now implement a function create_sequence that will 
* take as input our titles as well as the maximum sentence length and 
* returns a list of the integers corresponding to our tokens padded to the sentence maximum length
Keras has the helper functions pad_sequence for that on the top of the tokenizer methods.
End of explanation
"""


CLASSES = {
    'github': 0,
    'nytimes': 1,
    'techcrunch': 2
}
N_CLASSES = len(CLASSES)

# TODO 2
def encode_labels(sources):
    classes = [CLASSES[source] for source in sources]
    one_hots = to_categorical(classes)
    return one_hots

encode_labels(titles_df.source[:4])

"""
Explanation: We now need to write a function that 
* takes a title source and
* returns the corresponding one-hot encoded vector
Keras to_categorical is handy for that.
End of explanation
"""


N_TRAIN = int(DATASET_SIZE * 0.80)

titles_train, sources_train = (
    titles_df.title[:N_TRAIN], titles_df.source[:N_TRAIN])

titles_valid, sources_valid = (
    titles_df.title[N_TRAIN:], titles_df.source[N_TRAIN:])

"""
Explanation: Preparing the train/test splits
Let's split our data into train and test splits:
End of explanation
"""


sources_train.value_counts()

sources_valid.value_counts()

"""
Explanation: To be on the safe side, we verify that the train and test splits
have roughly the same number of examples per classes.
Since it is the case, accuracy will be a good metric to use to measure
the performance of our models.
End of explanation
"""


X_train, Y_train = create_sequences(titles_train), encode_labels(sources_train)
X_valid, Y_valid = create_sequences(titles_valid), encode_labels(sources_valid)

X_train[:3]

Y_train[:3]

"""
Explanation: Using create_sequence and encode_labels, we can now prepare the
training and validation data to feed our models.
The features will be
padded list of integers and the labels will be one-hot-encoded 3D vectors.
End of explanation
"""


def build_dnn_model(embed_dim):

    model = Sequential([
        Embedding(VOCAB_SIZE + 1, embed_dim, input_shape=[MAX_LEN]), # TODO 3
        Lambda(lambda x: tf.reduce_mean(x, axis=1)), # TODO 4
        Dense(N_CLASSES, activation='softmax') # TODO 5
    ])

    model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )
    return model

"""
Explanation: Building a DNN model
The build_dnn_model function below returns a compiled Keras model that implements a simple embedding layer transforming the word integers into dense vectors, followed by a Dense softmax layer that returns the probabilities for each class.
Note that we need to put a custom Keras Lambda layer in between the Embedding layer and the Dense softmax layer to do an average of the word vectors returned by the embedding layer. This is the average that's fed to the dense softmax layer. By doing so, we create a model that is simple but that loses information about the word order, creating a model that sees sentences as "bag-of-words".
End of explanation
"""


%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, 'dnn')
shutil.rmtree(MODEL_DIR, ignore_errors=True)

BATCH_SIZE = 300
EPOCHS = 100
EMBED_DIM = 10
PATIENCE = 0

dnn_model = build_dnn_model(embed_dim=EMBED_DIM)

dnn_history = dnn_model.fit(
    X_train, Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(dnn_history.history)[['loss', 'val_loss']].plot()
pd.DataFrame(dnn_history.history)[['accuracy', 'val_accuracy']].plot()

dnn_model.summary()

"""
Explanation: Below we train the model on 100 epochs but adding an EarlyStopping callback that will stop the training as soon as the validation loss has not improved after a number of steps specified by PATIENCE . Note that we also give the model.fit method a Tensorboard callback so that we can later compare all the models using TensorBoard.
End of explanation
"""


def build_rnn_model(embed_dim, units):

    model = Sequential([
        Embedding(VOCAB_SIZE + 1, embed_dim, input_shape=[MAX_LEN], mask_zero=True), # TODO 3
        GRU(units), # TODO 5
        Dense(N_CLASSES, activation='softmax')
    ])

    model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )
    return model

"""
Explanation: Building a RNN model
The build_dnn_model function below returns a compiled Keras model that implements a simple RNN model with a single GRU layer, which now takes into account the word order in the sentence.
The first and last layers are the same as for the simple DNN model.
Note that we set mask_zero=True in the Embedding layer so that the padded words (represented by a zero) are ignored by this and the subsequent layers.
End of explanation
"""


%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, 'rnn')
shutil.rmtree(MODEL_DIR, ignore_errors=True)

EPOCHS = 100
BATCH_SIZE = 300
EMBED_DIM = 10
UNITS = 16
PATIENCE = 0

rnn_model = build_rnn_model(embed_dim=EMBED_DIM, units=UNITS)

history = rnn_model.fit(
    X_train, Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(history.history)[['loss', 'val_loss']].plot()
pd.DataFrame(history.history)[['accuracy', 'val_accuracy']].plot()

rnn_model.summary()

"""
Explanation: Let's train the model with early stoping as above. 
Observe that we obtain the same type of accuracy as with the DNN model, but in less epochs (~3 v.s. ~20 epochs):
End of explanation
"""


def build_cnn_model(embed_dim, filters, ksize, strides):

    model = Sequential([
        Embedding(
            VOCAB_SIZE + 1,
            embed_dim,
            input_shape=[MAX_LEN],
            mask_zero=True), # TODO 3
        Conv1D( # TODO 5
            filters=filters,
            kernel_size=ksize,
            strides=strides,
            activation='relu',
        ),
        Flatten(), # TODO 5
        Dense(N_CLASSES, activation='softmax')
    ])

    model.compile(
        optimizer='adam',
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )
    return model

"""
Explanation: Build a CNN model
The build_dnn_model function below returns a compiled Keras model that implements a simple CNN model with a single Conv1D layer, which now takes into account the word order in the sentence.
The first and last layers are the same as for the simple DNN model, but we need to add a Flatten layer betwen the convolution and the softmax layer.
Note that we set mask_zero=True in the Embedding layer so that the padded words (represented by a zero) are ignored by this and the subsequent layers.
End of explanation
"""


%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, 'cnn')
shutil.rmtree(MODEL_DIR, ignore_errors=True)

EPOCHS = 100
BATCH_SIZE = 300
EMBED_DIM = 5
FILTERS = 200
STRIDES = 2
KSIZE = 3
PATIENCE = 0


cnn_model = build_cnn_model(
    embed_dim=EMBED_DIM,
    filters=FILTERS,
    strides=STRIDES,
    ksize=KSIZE,
)

cnn_history = cnn_model.fit(
    X_train, Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(cnn_history.history)[['loss', 'val_loss']].plot()
pd.DataFrame(cnn_history.history)[['accuracy', 'val_accuracy']].plot()

cnn_model.summary()

"""
Explanation: Let's train the model. 
Again we observe that we get the same kind of accuracy as with the DNN model but in many fewer steps.
End of explanation
"""


