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"""
Explanation: Copyright 2019 The TensorFlow Authors.
End of explanation
"""


import tensorflow as tf

import pandas as pd

"""
Explanation: Premade Estimators
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/tutorials/estimator/premade"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/estimator/premade.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/tutorials/estimator/premade.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/estimator/premade.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>


Warning: Estimators are not recommended for new code.  Estimators run v1.Session-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under compatibility guarantees, but will receive no fixes other than security vulnerabilities. See the migration guide for details.

This tutorial shows you
how to solve the Iris classification problem in TensorFlow using Estimators. An Estimator is a legacy TensorFlow high-level representation of a complete model. For more details see
Estimators.
Note:  In TensorFlow 2.0, the Keras API can accomplish these same tasks, and is believed to be an easier API to learn. If you are starting fresh, it is recommended you start with Keras.
First things first
In order to get started, you will first import TensorFlow and a number of libraries you will need.
End of explanation
"""


CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']

"""
Explanation: The data set
The sample program in this document builds and tests a model that
classifies Iris flowers into three different species based on the size of their
sepals and
petals.
You will train a model using the Iris data set. The Iris data set contains four features and one
label.
The four features identify the following botanical characteristics of
individual Iris flowers:

sepal length
sepal width
petal length
petal width

Based on this information, you can define a few helpful constants for parsing the data:
End of explanation
"""


train_path = tf.keras.utils.get_file(
    "iris_training.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv")
test_path = tf.keras.utils.get_file(
    "iris_test.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv")

train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)

"""
Explanation: Next, download and parse the Iris data set using Keras and Pandas. Note that you keep distinct datasets for training and testing.
End of explanation
"""


train.head()

"""
Explanation: You can inspect your data to see that you have four float feature columns and one int32 label.
End of explanation
"""


train_y = train.pop('Species')
test_y = test.pop('Species')

# The label column has now been removed from the features.
train.head()

"""
Explanation: For each of the datasets, split out the labels, which the model will be trained to predict.
End of explanation
"""


def input_evaluation_set():
    features = {'SepalLength': np.array([6.4, 5.0]),
                'SepalWidth':  np.array([2.8, 2.3]),
                'PetalLength': np.array([5.6, 3.3]),
                'PetalWidth':  np.array([2.2, 1.0])}
    labels = np.array([2, 1])
    return features, labels

"""
Explanation: Overview of programming with Estimators
Now that you have the data set up, you can define a model using a TensorFlow Estimator. An Estimator is any class derived from tf.estimator.Estimator. TensorFlow
provides a collection of
tf.estimator
(for example, LinearRegressor) to implement common ML algorithms. Beyond
those, you may write your own
custom Estimators.
It is recommended using pre-made Estimators when just getting started.
To write a TensorFlow program based on pre-made Estimators, you must perform the
following tasks:

Create one or more input functions.
Define the model's feature columns.
Instantiate an Estimator, specifying the feature columns and various
  hyperparameters.
Call one or more methods on the Estimator object, passing the appropriate
  input function as the source of the data.

Let's see how those tasks are implemented for Iris classification.
Create input functions
You must create input functions to supply data for training,
evaluating, and prediction.
An input function is a function that returns a tf.data.Dataset object
which outputs the following two-element tuple:

features - A Python dictionary in which:
Each key is the name of a feature.
Each value is an array containing all of that feature's values.


label - An array containing the values of the
  label for
  every example.

Just to demonstrate the format of the input function, here's a simple
implementation:
End of explanation
"""


def input_fn(features, labels, training=True, batch_size=256):
    """An input function for training or evaluating"""
    # Convert the inputs to a Dataset.
    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))

    # Shuffle and repeat if you are in training mode.
    if training:
        dataset = dataset.shuffle(1000).repeat()
    
    return dataset.batch(batch_size)


"""
Explanation: Your input function may generate the features dictionary and label list any
way you like. However, It is recommended using TensorFlow's Dataset API, which can
parse all sorts of data.
The Dataset API can handle a lot of common cases for you. For example,
using the Dataset API, you can easily read in records from a large collection
of files in parallel and join them into a single stream.
To keep things simple in this example you are going to load the data with
pandas, and build an input pipeline from this
in-memory data:
End of explanation
"""


# Feature columns describe how to use the input.
my_feature_columns = []
for key in train.keys():
    my_feature_columns.append(tf.feature_column.numeric_column(key=key))

"""
Explanation: Define the feature columns
A feature column
is an object describing how the model should use raw input data from the
features dictionary. When you build an Estimator model, you pass it a list of
feature columns that describes each of the features you want the model to use.
The tf.feature_column module provides many options for representing data
to the model.
For Iris, the 4 raw features are numeric values, so you'll build a list of
feature columns to tell the Estimator model to represent each of the four
features as 32-bit floating-point values. Therefore, the code to create the
feature column is:
End of explanation
"""


# Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each.
classifier = tf.estimator.DNNClassifier(
    feature_columns=my_feature_columns,
    # Two hidden layers of 30 and 10 nodes respectively.
    hidden_units=[30, 10],
    # The model must choose between 3 classes.
    n_classes=3)

"""
Explanation: Feature columns can be far more sophisticated than those shown here.  You can read more about Feature Columns in this guide.
Now that you have the description of how you want the model to represent the raw
features, you can build the estimator.
Instantiate an estimator
The Iris problem is a classic classification problem. Fortunately, TensorFlow
provides several pre-made classifier Estimators, including:

tf.estimator.DNNClassifier for deep models that perform multi-class
  classification.
tf.estimator.DNNLinearCombinedClassifier for wide & deep models.
tf.estimator.LinearClassifier for classifiers based on linear models.

For the Iris problem, tf.estimator.DNNClassifier seems like the best choice.
Here's how you instantiated this Estimator:
End of explanation
"""


# Train the Model.
classifier.train(
    input_fn=lambda: input_fn(train, train_y, training=True),
    steps=5000)

"""
Explanation: Train, Evaluate, and Predict
Now that you have an Estimator object, you can call methods to do the following:

Train the model.
Evaluate the trained model.
Use the trained model to make predictions.

Train the model
Train the model by calling the Estimator's train method as follows:
End of explanation
"""


eval_result = classifier.evaluate(
    input_fn=lambda: input_fn(test, test_y, training=False))

print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))

"""
Explanation: Note that you wrap up your input_fn call in a
lambda
to capture the arguments while providing an input function that takes no
arguments, as expected by the Estimator. The steps argument tells the method
to stop training after a number of training steps.
Evaluate the trained model
Now that the model has been trained, you can get some statistics on its
performance. The following code block evaluates the accuracy of the trained
model on the test data:
End of explanation
"""


# Generate predictions from the model
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
    'SepalLength': [5.1, 5.9, 6.9],
    'SepalWidth': [3.3, 3.0, 3.1],
    'PetalLength': [1.7, 4.2, 5.4],
    'PetalWidth': [0.5, 1.5, 2.1],
}

def input_fn(features, batch_size=256):
    """An input function for prediction."""
    # Convert the inputs to a Dataset without labels.
    return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size)

predictions = classifier.predict(
    input_fn=lambda: input_fn(predict_x))

"""
Explanation: Unlike the call to the train method, you did not pass the steps
argument to evaluate. The input_fn for eval only yields a single
epoch of data.
The eval_result dictionary also contains the average_loss (mean loss per sample), the loss (mean loss per mini-batch) and the value of the estimator's global_step (the number of training iterations it underwent).
Making predictions (inferring) from the trained model
You now have a trained model that produces good evaluation results.
You can now use the trained model to predict the species of an Iris flower
based on some unlabeled measurements. As with training and evaluation, you make
predictions using a single function call:
End of explanation
"""


for pred_dict, expec in zip(predictions, expected):
    class_id = pred_dict['class_ids'][0]
    probability = pred_dict['probabilities'][class_id]

    print('Prediction is "{}" ({:.1f}%), expected "{}"'.format(
        SPECIES[class_id], 100 * probability, expec))

"""
Explanation: The predict method returns a Python iterable, yielding a dictionary of
prediction results for each example. The following code prints a few
predictions and their probabilities:
End of explanation
"""


