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#@title Install the What-If Tool widget if running in colab {display-mode: "form"}

try:
  import google.colab
  !pip install --upgrade witwidget
except:
  pass


    

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#@title Define helper functions {display-mode: "form"}

import pandas as pd
import numpy as np
import tensorflow as tf
import functools

# Creates a tf feature spec from the dataframe and columns specified.
def create_feature_spec(df, columns=None):
    feature_spec = {}
    if columns == None:
        columns = df.columns.values.tolist()
    for f in columns:
        if df[f].dtype is np.dtype(np.int64):
            feature_spec[f] = tf.io.FixedLenFeature(shape=(), dtype=tf.int64)
        elif df[f].dtype is np.dtype(np.float64):
            feature_spec[f] = tf.io.FixedLenFeature(shape=(), dtype=tf.float32)
        else:
            feature_spec[f] = tf.io.FixedLenFeature(shape=(), dtype=tf.string)
    return feature_spec

# Creates simple numeric and categorical feature columns from a feature spec and a
# list of columns from that spec to use.
#
# NOTE: Models might perform better with some feature engineering such as bucketed
# numeric columns and hash-bucket/embedding columns for categorical features.
def create_feature_columns(columns, feature_spec):
    ret = []
    for col in columns:
        if feature_spec[col].dtype is tf.int64 or feature_spec[col].dtype is tf.float32:
            ret.append(tf.feature_column.numeric_column(col))
        else:
            ret.append(tf.feature_column.indicator_column(
                tf.feature_column.categorical_column_with_vocabulary_list(col, list(df[col].unique()))))
    return ret

# An input function for providing input to a model from tf.Examples
def tfexamples_input_fn(examples, feature_spec, label, mode=tf.estimator.ModeKeys.EVAL,
                       num_epochs=None, 
                       batch_size=64):
    def ex_generator():
        for i in range(len(examples)):
            yield examples[i].SerializeToString()
    dataset = tf.data.Dataset.from_generator(
      ex_generator, tf.dtypes.string, tf.TensorShape([]))
    if mode == tf.estimator.ModeKeys.TRAIN:
        dataset = dataset.shuffle(buffer_size=2 * batch_size + 1)
    dataset = dataset.batch(batch_size)
    dataset = dataset.map(lambda tf_example: parse_tf_example(tf_example, label, feature_spec))
    dataset = dataset.repeat(num_epochs)
    return dataset

# Parses Tf.Example protos into features for the input function.
def parse_tf_example(example_proto, label, feature_spec):
    parsed_features = tf.io.parse_example(serialized=example_proto, features=feature_spec)
    target = parsed_features.pop(label)
    return parsed_features, target

# Converts a dataframe into a list of tf.Example protos.
def df_to_examples(df, columns=None):
    examples = []
    if columns == None:
        columns = df.columns.values.tolist()
    for index, row in df.iterrows():
        example = tf.train.Example()
        for col in columns:
            if df[col].dtype is np.dtype(np.int64):
                example.features.feature[col].int64_list.value.append(int(row[col]))
            elif df[col].dtype is np.dtype(np.float64):
                example.features.feature[col].float_list.value.append(row[col])
            elif row[col] == row[col]:
                example.features.feature[col].bytes_list.value.append(row[col].encode('utf-8'))
        examples.append(example)
    return examples

# Converts a dataframe column into a column of 0's and 1's based on the provided test.
# Used to force label columns to be numeric for binary classification using a TF estimator.
def make_label_column_numeric(df, label_column, test):
  df[label_column] = np.where(test(df[label_column]), 1, 0)


    

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#@title Read training dataset from CSV {display-mode: "form"}

import pandas as pd

# Set the path to the CSV containing the dataset to train on.
csv_path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'

# Set the column names for the columns in the CSV. If the CSV's first line is a header line containing
# the column names, then set this to None.
csv_columns = [
  "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Marital-Status",
  "Occupation", "Relationship", "Race", "Sex", "Capital-Gain", "Capital-Loss",
  "Hours-per-week", "Country", "Over-50K"]

# Read the dataset from the provided CSV and print out information about it.
df = pd.read_csv(csv_path, names=csv_columns, skipinitialspace=True)

df


    

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#@title Specify input columns and column to predict {display-mode: "form"}
import numpy as np

# Set the column in the dataset you wish for the model to predict
label_column = 'Over-50K'

# Make the label column numeric (0 and 1), for use in our model.
# In this case, examples with a target value of '>50K' are considered to be in
# the '1' (positive) class and all other examples are considered to be in the
# '0' (negative) class.
make_label_column_numeric(df, label_column, lambda val: val == '>50K')

# Set list of all columns from the dataset we will use for model input.
input_features = [
  'Age', 'Workclass', 'Education', 'Marital-Status', 'Occupation',
  'Relationship', 'Race', 'Sex', 'Capital-Gain', 'Capital-Loss',
  'Hours-per-week', 'Country']

# Create a list containing all input features and the label column
features_and_labels = input_features + [label_column]


    

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#@title Convert dataset to tf.Example protos {display-mode: "form"}

examples = df_to_examples(df)


    

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#@title Create and train the linear classifier {display-mode: "form"}

num_steps = 2000  #@param {type: "number"}

# Create a feature spec for the classifier
feature_spec = create_feature_spec(df, features_and_labels)

# Define and train the classifier
train_inpf = functools.partial(tfexamples_input_fn, examples, feature_spec, label_column)
classifier = tf.estimator.LinearClassifier(
    feature_columns=create_feature_columns(input_features, feature_spec))
classifier.train(train_inpf, steps=num_steps)


    

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#@title Create and train the DNN classifier {display-mode: "form"}
num_steps_2 = 2000  #@param {type: "number"}

classifier2 = tf.estimator.DNNClassifier(
    feature_columns=create_feature_columns(input_features, feature_spec),
    hidden_units=[128, 64, 32])
classifier2.train(train_inpf, steps=num_steps_2)


    

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#@title Invoke What-If Tool for test data and the trained models {display-mode: "form"}

num_datapoints = 2000  #@param {type: "number"}
tool_height_in_px = 1000  #@param {type: "number"}

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

# Load up the test dataset
test_csv_path = 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test'
test_df = pd.read_csv(test_csv_path, names=csv_columns, skipinitialspace=True,
  skiprows=1)
make_label_column_numeric(test_df, label_column, lambda val: val == '>50K.')
test_examples = df_to_examples(test_df[0:num_datapoints])

# Setup the tool with the test examples and the trained classifier
config_builder = WitConfigBuilder(test_examples[0:num_datapoints]).set_estimator_and_feature_spec(
    classifier, feature_spec).set_compare_estimator_and_feature_spec(
    classifier2, feature_spec).set_label_vocab(['Under 50K', 'Over 50K'])
a = WitWidget(config_builder, height=tool_height_in_px)