!pip install fairness-indicators

%tensorflow_version 2.x
import os
import tempfile
import apache_beam as beam
import numpy as np
import pandas as pd
from datetime import datetime

import tensorflow_hub as hub
import tensorflow as tf
import tensorflow_model_analysis as tfma
import tensorflow_data_validation as tfdv
from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
from tensorflow_model_analysis.addons.fairness.view import widget_view

from witwidget.notebook.visualization import WitConfigBuilder
from witwidget.notebook.visualization import WitWidget

"""
Explanation: Fairness Indicators Example Colab
Overview
In this activity, you'll use Fairness Indicators to explore the Civil Comments dataset. Fairness Indicators is a suite of tools built on top of TensorFlow Model Analysis that enable regular evaluation of fairness metrics in product pipelines. This Introductory Video provides more details and context on the real-world scenario we are presenting here, one of primary motivations for creating Fairness Indicators.
About the Dataset
In this exercise, you'll work with the Civil Comments dataset, approximately 2 million public comments made public by the Civil Comments platform in 2017 for ongoing research. This effort was sponsored by Jigsaw, who have hosted competitions on Kaggle to help classify toxic comments as well as minimize unintended model bias. 
Each individual text comment in the dataset has a toxicity label, with the label being 1 if the comment is toxic and 0 if the comment is non-toxic. Within the data, a subset of comments are labeled with a variety of identity attributes, including categories for gender, sexual orientation, religion, and race or ethnicity.
About the Tools
TensorFlow Model Analysis is a library for evaluating both TensorFlow and non-TensorFlow machine learning models. It allows users to evaluate their models on large amounts of data in a distributed manner, computing in-graph and other metrics over different slices of data and visualized in notebooks. 
Fairness Indicators is built on top of TFMA. With Fairness Indicators, users will be able to: 

Evaluate model performance, sliced across defined groups of users
Feel confident about results with confidence intervals and evaluations at multiple thresholds

Fairness Indicators is packaged with TensorFlow Data Validation and What-If Tool to allow users to:

Evaluate the distribution of datasets
Dive deep into individual slices to explore root causes and opportunities for improvement with the What-If Tool

Importing
Run the following code to install the fairness_indicators library. This package contains the tools we'll be using in this exercise. Restart Runtime may be requested but is not necessary.
End of explanation
"""


train_tf_file = tf.keras.utils.get_file('train.tf', 'https://storage.googleapis.com/civil_comments_dataset/train.tfrecord')
validate_tf_file = tf.keras.utils.get_file('validate.tf', 'https://storage.googleapis.com/civil_comments_dataset/validate.tfrecord')

stats = tfdv.generate_statistics_from_tfrecord(data_location=train_tf_file)
tfdv.visualize_statistics(stats)

"""
Explanation: Download and Understand the Data
In this exercise, you'll work with the Civil Comments dataset, approximately 2 million public comments made public by the Civil Comments platform in 2017. Additionally, a subset of comments have been labelled with a variety of identity attributes, representing the identities that are mentioned in the comment.
We've hosted the dataset on Google Cloud Platform for convenience. Run the following code to download the data from GCP, the data will take about a minute to download and analyze.
TensorFlow Data Validation is one tool you can use to analyze your data. You can use it to find potential problems in your data, such as missing values and data imbalances, that can lead to Fairness disparities.
End of explanation
"""


BASE_DIR = tempfile.gettempdir()

TEXT_FEATURE = 'comment_text'
LABEL = 'toxicity'
FEATURE_MAP = {
    # Label:
    LABEL: tf.io.FixedLenFeature([], tf.float32),
    # Text:
    TEXT_FEATURE:  tf.io.FixedLenFeature([], tf.string),

    # Identities:
    'sexual_orientation':tf.io.VarLenFeature(tf.string),
    'gender':tf.io.VarLenFeature(tf.string),
    'religion':tf.io.VarLenFeature(tf.string),
    'race':tf.io.VarLenFeature(tf.string),
    'disability':tf.io.VarLenFeature(tf.string),
}

"""
Explanation: There are several interesting things that we may want to note in this data. The first is that the toxicity label, which is what we are predicting, is unbalanced. Only 8% of examples in the training set are toxic, which means that a classifier could get 92% accuracy by predicting that all comments are non-toxic.
For the fields relating to identity terms note that out of 1.08 million training examples, only 6582 examples deal with homosexuality, and those related to bisexuality are even more rare. This might indicate that performance on these slices may suffer due to lack of training data.
Defining Constants
Here, we define the feature map that will be used to parse the data. Each example will have a label, comment text, and identity features sexual orientation, gender, religion, race, and disability that are associated with the text. See this page to learn more about the data schema.
End of explanation
"""


def train_input_fn():
  def parse_function(serialized):
    parsed_example = tf.io.parse_single_example(
        serialized=serialized, features=FEATURE_MAP)
    # Adds a weight column to deal with unbalanced classes.
    parsed_example['weight'] = tf.add(parsed_example[LABEL], 0.1)
    return (parsed_example,
            parsed_example[LABEL])
  train_dataset = tf.data.TFRecordDataset(
      filenames=[train_tf_file]).map(parse_function).batch(512)
  return train_dataset

"""
Explanation: Train the Model
First, set up the input function to feed data into the model. Note that since we identified a class imbalance by our earlier TensorFlow Data Validation run, we will add a weight column to each example and upweight the toxic examples to account for this. We only use identity features during the evaluation phase, as only the comments are fed into the model at training time.
End of explanation
"""


model_dir = os.path.join(BASE_DIR, 'train', datetime.now().strftime(
    "%Y%m%d-%H%M%S"))

embedded_text_feature_column = hub.text_embedding_column(
    key=TEXT_FEATURE,
    module_spec='https://tfhub.dev/google/nnlm-en-dim128/1')

classifier = tf.estimator.DNNClassifier(
    hidden_units=[500, 100],
    weight_column='weight',
    feature_columns=[embedded_text_feature_column],
    optimizer=tf.optimizers.Adagrad(learning_rate=0.003),
    n_classes=2,
    model_dir=model_dir)

classifier.train(input_fn=train_input_fn, steps=1000)

"""
Explanation: Next, create a deep neural network model, and train it on the data:
End of explanation
"""


def eval_input_receiver_fn():
  serialized_tf_example = tf.compat.v1.placeholder(
      dtype=tf.string, shape=[None], name='input_example_placeholder')

  # This *must* be a dictionary containing a single key 'examples', which
  # points to the input placeholder.
  receiver_tensors = {'examples': serialized_tf_example}

  features = tf.io.parse_example(serialized_tf_example, FEATURE_MAP)
  features['weight'] = tf.ones_like(features[LABEL])

  return tfma.export.EvalInputReceiver(
    features=features,
    receiver_tensors=receiver_tensors,
    labels=features[LABEL])

tfma_export_dir = tfma.export.export_eval_savedmodel(
  estimator=classifier,
  export_dir_base=os.path.join(BASE_DIR, 'tfma_eval_model'),
  eval_input_receiver_fn=eval_input_receiver_fn)

"""
Explanation: Run TensorFlow Model Analysis with Fairness Indicators
Export Saved Model
End of explanation
"""


#@title Fairness Indicators Computation Options
tfma_eval_result_path = os.path.join(BASE_DIR, 'tfma_eval_result')

#@markdown Modify the slice_selection for experiments on other identities.
slice_selection = 'sexual_orientation' #@param ["sexual_orientation", "gender", "religion", "race", "disability"]
#@markdown Confidence Intervals can help you make better decisions regarding your data, but as it requires computing multiple resamples, is slower particularly in the colab environment that cannot take advantage of parallelization.
compute_confidence_intervals = False #@param {type:"boolean"}

# Define slices that you want the evaluation to run on.
slice_spec = [
    tfma.slicer.SingleSliceSpec(), # Overall slice
    tfma.slicer.SingleSliceSpec(columns=[slice_selection]),
]

# Add the fairness metrics.
add_metrics_callbacks = [
  tfma.post_export_metrics.fairness_indicators(
      thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
      labels_key=LABEL
      )
]

eval_shared_model = tfma.default_eval_shared_model(
    eval_saved_model_path=tfma_export_dir,
    add_metrics_callbacks=add_metrics_callbacks)

# Run the fairness evaluation.
with beam.Pipeline() as pipeline:
  _ = (
      pipeline
      | 'ReadData' >> beam.io.ReadFromTFRecord(validate_tf_file)
      | 'ExtractEvaluateAndWriteResults' >>
       tfma.ExtractEvaluateAndWriteResults(
                 eval_shared_model=eval_shared_model,
                 slice_spec=slice_spec,
                 compute_confidence_intervals=compute_confidence_intervals,
                 output_path=tfma_eval_result_path)
  )

eval_result = tfma.load_eval_result(output_path=tfma_eval_result_path)

"""
Explanation: Compute Fairness Metrics
Select the identity to compute metrics for and whether to run with confidence intervals in the panel on the right-hand side. Depending on your configurations, this step will take 2-10 minutes to run.
End of explanation
"""


DEFAULT_MAX_EXAMPLES = 1000

# Load 10000 examples in memory. When first rendered, 
# What-If Tool should only display 1000 of these due to browser constraints.
def wit_dataset(file, num_examples=100000):
  dataset = tf.data.TFRecordDataset(
      filenames=[train_tf_file]).take(num_examples)
  return [tf.train.Example.FromString(d.numpy()) for d in dataset]

wit_data = wit_dataset(train_tf_file)
config_builder = WitConfigBuilder(wit_data[:DEFAULT_MAX_EXAMPLES]).set_estimator_and_feature_spec(
    classifier, FEATURE_MAP).set_label_vocab(['non-toxicity', LABEL]).set_target_feature(LABEL)
wit = WitWidget(config_builder)

"""
Explanation: Render What-if Tool
In this section, you'll use the What-If Tool's interactive visual interface to explore and manipulate data at a micro-level.
On the right-hand panel in the visualization, you will see a scatter plot where each point represents one of the examples in the subset loaded into the tool. Click on one of the points. In the left-hand panel, you should now see details about this particular example. The comment text, ground truth toxicity, and applicable identities are shown. At the bottom of this left-hand panel, you see the inference results from the model you just trained.
Modify the text of the example. You can then click the "Run inference" button to view how your changes caused the perceived toxicity prediction to change.
End of explanation
"""


event_handlers={'slice-selected':
                wit.create_selection_callback(wit_data, DEFAULT_MAX_EXAMPLES)}
widget_view.render_fairness_indicator(eval_result,
                                      slicing_column=slice_selection,
                                      event_handlers=event_handlers)

"""
Explanation: Render Fairness Indicators
Render the Fairness Indicators widget with the exported evaluation results.
Below you will see bar charts displaying performance of each slice of the data on selected metrics. You can adjust the baseline comparison slice as well as the displayed threshold(s) using the drop down menus at the top of the visualization. 
The Fairness Indicator widget is integrated with the What-If Tool rendered above. If you select one slice of the data in the bar chart, the What-If Tool will update to show you examples from the selected slice. When the data reloads in the What-If Tool above, try modifying Color By to toxicity. This can give you a visual understanding of the toxicity balance of examples by slice.
End of explanation
"""


