"""
# Step 1: Imports #

We begin by importing several Python packages.
"""
import math, re, os
import numpy as np
import tensorflow as tf

print("Tensorflow version " + tf.__version__)
"""
# Step 2: Distribution Strategy #

A TPU has eight different *cores* and each of these cores acts as its own accelerator. (A TPU is sort of like having eight GPUs in one machine.) We tell TensorFlow how to make use of all these cores at once through a **distribution strategy**. Run the following cell to create the distribution strategy that we'll later apply to our model.
"""
# Detect TPU, return appropriate distribution strategy
try:
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver() 
    print('Running on TPU ', tpu.master())
except ValueError:
    tpu = None

if tpu:
    tf.config.experimental_connect_to_cluster(tpu)
    tf.tpu.experimental.initialize_tpu_system(tpu)
    strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
    strategy = tf.distribute.get_strategy() 

print("REPLICAS: ", strategy.num_replicas_in_sync)
"""
We'll use the distribution strategy when we create our neural network model. Then, TensorFlow will distribute the training among the eight TPU cores by creating eight different *replicas* of the model, one for each core.

# Step 3: Loading the Competition Data #

## Get GCS Path ##

When used with TPUs, datasets need to be stored in a [Google Cloud Storage bucket](https://cloud.google.com/storage/). You can use data from any public GCS bucket by giving its path just like you would data from `'/kaggle/input'`. The following will retrieve the GCS path for this competition's dataset.
"""
from kaggle_datasets import KaggleDatasets

GCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')
print(GCS_DS_PATH) # what do gcs paths look like?
"""
You can use data from any public dataset here on Kaggle in just the same way. If you'd like to use data from one of your private datasets, see [here](https://www.kaggle.com/docs/tpu#tpu3pt5).

## Load Data ##

When used with TPUs, datasets are often serialized into [TFRecords](https://www.kaggle.com/ryanholbrook/tfrecords-basics). This is a format convenient for distributing data to each of the TPUs cores. We've hidden the cell that reads the TFRecords for our dataset since the process is a bit long. You could come back to it later for some guidance on using your own datasets with TPUs.
"""

IMAGE_SIZE = [512, 512]
GCS_PATH = GCS_DS_PATH + '/tfrecords-jpeg-512x512'
AUTO = tf.data.experimental.AUTOTUNE

TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')
VALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
TEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') 

CLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09
           'snapdragon',       "colt's foot",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19
           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29
           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39
           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49
           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59
           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69
           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79
           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89
           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99
           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102


def decode_image(image_data):
    image = tf.image.decode_jpeg(image_data, channels=3)
    image = tf.cast(image, tf.float32) / 255.0  # convert image to floats in [0, 1] range
    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU
    return image

def read_labeled_tfrecord(example):
    LABELED_TFREC_FORMAT = {
        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
        "class": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element
    }
    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)
    image = decode_image(example['image'])
    label = tf.cast(example['class'], tf.int32)
    return image, label # returns a dataset of (image, label) pairs

def read_unlabeled_tfrecord(example):
    UNLABELED_TFREC_FORMAT = {
        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
        "id": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element
        # class is missing, this competitions's challenge is to predict flower classes for the test dataset
    }
    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
    image = decode_image(example['image'])
    idnum = example['id']
    return image, idnum # returns a dataset of image(s)

def load_dataset(filenames, labeled=True, ordered=False):
    # Read from TFRecords. For optimal performance, reading from multiple files at once and
    # disregarding data order. Order does not matter since we will be shuffling the data anyway.

    ignore_order = tf.data.Options()
    if not ordered:
        ignore_order.experimental_deterministic = False # disable order, increase speed

    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files
    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order
    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False
    return dataset
"""
## Create Data Pipelines ##

In this final step we'll use the `tf.data` API to define an efficient data pipeline for each of the training, validation, and test splits.
"""

def data_augment(image, label):
    # Thanks to the dataset.prefetch(AUTO)
    # statement in the next function (below), this happens essentially
    # for free on TPU. Data pipeline code is executed on the "CPU"
    # part of the TPU while the TPU itself is computing gradients.
    image = tf.image.random_flip_left_right(image)
    #image = tf.image.random_saturation(image, 0, 2)
    return image, label   

def get_training_dataset():
    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)
    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
    dataset = dataset.repeat() # the training dataset must repeat for several epochs
    dataset = dataset.shuffle(2048)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

def get_validation_dataset(ordered=False):
    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.cache()
    dataset = dataset.prefetch(AUTO)
    return dataset

def get_test_dataset(ordered=False):
    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.prefetch(AUTO)
    return dataset

def count_data_items(filenames):
    # the number of data items is written in the name of the .tfrec
    # files, i.e. flowers00-230.tfrec = 230 data items
    n = [int(re.compile(r"-([0-9]*)\.").search(filename).group(1)) for filename in filenames]
    return np.sum(n)

NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))

"""
This next cell will create the datasets that we'll use with Keras during training and inference. Notice how we scale the size of the batches to the number of TPU cores.
"""
# Define the batch size. This will be 16 with TPU off and 128 (=16*8) with TPU on
BATCH_SIZE = 16 * strategy.num_replicas_in_sync

ds_train = get_training_dataset()
ds_valid = get_validation_dataset()
ds_test = get_test_dataset()

print("Training:", ds_train)
print ("Validation:", ds_valid)
print("Test:", ds_test)
"""
These datasets are `tf.data.Dataset` objects. You can think about a dataset in TensorFlow as a *stream* of data records. The training and validation sets are streams of `(image, label)` pairs.
"""
np.set_printoptions(threshold=15, linewidth=80)

print("Training data shapes:")
for image, label in ds_train.take(3):
    print(image.numpy().shape, label.numpy().shape)
print("Training data label examples:", label.numpy())
"""
The test set is a stream of `(image, idnum)` pairs; `idnum` here is the unique identifier given to the image that we'll use later when we make our submission as a `csv` file.
"""
print("Test data shapes:")
for image, idnum in ds_test.take(3):
    print(image.numpy().shape, idnum.numpy().shape)
print("Test data IDs:", idnum.numpy().astype('U')) # U=unicode string
"""
# Step 4: Explore Data #

Let's take a moment to look at some of the images in the dataset.
"""

from matplotlib import pyplot as plt

def batch_to_numpy_images_and_labels(data):
    images, labels = data
    numpy_images = images.numpy()
    numpy_labels = labels.numpy()
    if numpy_labels.dtype == object: # binary string in this case,
                                     # these are image ID strings
        numpy_labels = [None for _ in enumerate(numpy_images)]
    # If no labels, only image IDs, return None for labels (this is
    # the case for test data)
    return numpy_images, numpy_labels

def title_from_label_and_target(label, correct_label):
    if correct_label is None:
        return CLASSES[label], True
    correct = (label == correct_label)
    return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '',
                                CLASSES[correct_label] if not correct else ''), correct

def display_one_flower(image, title, subplot, red=False, titlesize=16):
    plt.subplot(*subplot)
    plt.axis('off')
    plt.imshow(image)
    if len(title) > 0:
        plt.title(title, fontsize=int(titlesize) if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))
    return (subplot[0], subplot[1], subplot[2]+1)
    
def display_batch_of_images(databatch, predictions=None):
    """This will work with:
    display_batch_of_images(images)
    display_batch_of_images(images, predictions)
    display_batch_of_images((images, labels))
    display_batch_of_images((images, labels), predictions)
    """
    # data
    images, labels = batch_to_numpy_images_and_labels(databatch)
    if labels is None:
        labels = [None for _ in enumerate(images)]
        
    # auto-squaring: this will drop data that does not fit into square
    # or square-ish rectangle
    rows = int(math.sqrt(len(images)))
    cols = len(images)//rows
        
    # size and spacing
    FIGSIZE = 13.0
    SPACING = 0.1
    subplot=(rows,cols,1)
    if rows < cols:
        plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))
    else:
        plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))
    
    # display
    for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])):
        title = '' if label is None else CLASSES[label]
        correct = True
        if predictions is not None:
            title, correct = title_from_label_and_target(predictions[i], label)
        dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images
        subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
    
    #layout
    plt.tight_layout()
    if label is None and predictions is None:
        plt.subplots_adjust(wspace=0, hspace=0)
    else:
        plt.subplots_adjust(wspace=SPACING, hspace=SPACING)
    plt.show()


def display_training_curves(training, validation, title, subplot):
    if subplot%10==1: # set up the subplots on the first call
        plt.subplots(figsize=(10,10), facecolor='#F0F0F0')
        plt.tight_layout()
    ax = plt.subplot(subplot)
    ax.set_facecolor('#F8F8F8')
    ax.plot(training)
    ax.plot(validation)
    ax.set_title('model '+ title)
    ax.set_ylabel(title)
    #ax.set_ylim(0.28,1.05)
    ax.set_xlabel('epoch')
    ax.legend(['train', 'valid.'])
"""
You can display a single batch of images from a dataset with another of our helper functions. The next cell will turn the dataset into an iterator of batches of 20 images.
"""
ds_iter = iter(ds_train.unbatch().batch(20))
"""
Use the Python `next` function to pop out the next batch in the stream and display it with the helper function.
"""
one_batch = next(ds_iter)
display_batch_of_images(one_batch)
"""
By defining `ds_iter` and `one_batch` in separate cells, you only need to rerun the cell above to see a new batch of images.
"""
"""
# Step 5: Define Model #

Now we're ready to create a neural network for classifying images! We'll use what's known as **transfer learning**. With transfer learning, you reuse part of a pretrained model to get a head-start on a new dataset.

For this tutorial, we'll to use a model called **VGG16** pretrained on [ImageNet](http://image-net.org/)). Later, you might want to experiment with [other models](https://www.tensorflow.org/api_docs/python/tf/keras/applications) included with Keras. ([Xception](https://www.tensorflow.org/api_docs/python/tf/keras/applications/Xception) wouldn't be a bad choice.)

The distribution strategy we created earlier contains a [context manager](https://docs.python.org/3/reference/compound_stmts.html#with), `strategy.scope`. This context manager tells TensorFlow how to divide the work of training among the eight TPU cores. When using TensorFlow with a TPU, it's important to define your model in a `strategy.scope()` context.
"""
EPOCHS = 12

with strategy.scope():
    pretrained_model = tf.keras.applications.VGG16(
        weights='imagenet',
        include_top=False ,
        input_shape=[*IMAGE_SIZE, 3]
    )
    pretrained_model.trainable = False
    
    model = tf.keras.Sequential([
        # To a base pretrained on ImageNet to extract features from images...
        pretrained_model,
        # ... attach a new head to act as a classifier.
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(len(CLASSES), activation='softmax')
    ])
"""
The `'sparse_categorical'` versions of the loss and metrics are appropriate for a classification task with more than two labels, like this one.
"""
model.compile(
    optimizer='adam',
    loss = 'sparse_categorical_crossentropy',
    metrics=['sparse_categorical_accuracy'],
)

model.summary()
"""
# Step 6: Training #

## Learning Rate Schedule ##

We'll train this network with a special learning rate schedule.
"""

# Learning Rate Schedule for Fine Tuning #
def exponential_lr(epoch,
                   start_lr = 0.00001, min_lr = 0.00001, max_lr = 0.00005,
                   rampup_epochs = 5, sustain_epochs = 0,
                   exp_decay = 0.8):

    def lr(epoch, start_lr, min_lr, max_lr, rampup_epochs, sustain_epochs, exp_decay):
        # linear increase from start to rampup_epochs
        if epoch < rampup_epochs:
            lr = ((max_lr - start_lr) /
                  rampup_epochs * epoch + start_lr)
        # constant max_lr during sustain_epochs
        elif epoch < rampup_epochs + sustain_epochs:
            lr = max_lr
        # exponential decay towards min_lr
        else:
            lr = ((max_lr - min_lr) *
                  exp_decay**(epoch - rampup_epochs - sustain_epochs) +
                  min_lr)
        return lr
    return lr(epoch,
              start_lr,
              min_lr,
              max_lr,
              rampup_epochs,
              sustain_epochs,
              exp_decay)

lr_callback = tf.keras.callbacks.LearningRateScheduler(exponential_lr, verbose=True)

rng = [i for i in range(EPOCHS)]
y = [exponential_lr(x) for x in rng]
plt.plot(rng, y)
print("Learning rate schedule: {:.3g} to {:.3g} to {:.3g}".format(y[0], max(y), y[-1]))
"""
## Fit Model ##

And now we're ready to train the model. After defining a few parameters, we're good to go!
"""
# Define training epochs
EPOCHS = 12
STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE

history = model.fit(
    ds_train,
    validation_data=ds_valid,
    epochs=EPOCHS,
    steps_per_epoch=STEPS_PER_EPOCH,
    callbacks=[lr_callback],
)
"""
This next cell shows how the loss and metrics progressed during training. Thankfully, it converges!
"""
display_training_curves(
    history.history['loss'],
    history.history['val_loss'],
    'loss',
    211,
)
display_training_curves(
    history.history['sparse_categorical_accuracy'],
    history.history['val_sparse_categorical_accuracy'],
    'accuracy',
    212,
)
"""
# Step 7: Evaluate Predictions #

Before making your final predictions on the test set, it's a good idea to evaluate your model's predictions on the validation set. This can help you diagnose problems in training or suggest ways your model could be improved. We'll look at two common ways of validation: plotting the **confusion matrix** and **visual validation**.
"""

import matplotlib.pyplot as plt
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix

def display_confusion_matrix(cmat, score, precision, recall):
    plt.figure(figsize=(15,15))
    ax = plt.gca()
    ax.matshow(cmat, cmap='Reds')
    ax.set_xticks(range(len(CLASSES)))
    ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})
    plt.setp(ax.get_xticklabels(), rotation=45, ha="left", rotation_mode="anchor")
    ax.set_yticks(range(len(CLASSES)))
    ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})
    plt.setp(ax.get_yticklabels(), rotation=45, ha="right", rotation_mode="anchor")
    titlestring = ""
    if score is not None:
        titlestring += 'f1 = {:.3f} '.format(score)
    if precision is not None:
        titlestring += '\nprecision = {:.3f} '.format(precision)
    if recall is not None:
        titlestring += '\nrecall = {:.3f} '.format(recall)
    if len(titlestring) > 0:
        ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'#804040'})
    plt.show()
    
def display_training_curves(training, validation, title, subplot):
    if subplot%10==1: # set up the subplots on the first call
        plt.subplots(figsize=(10,10), facecolor='#F0F0F0')
        plt.tight_layout()
    ax = plt.subplot(subplot)
    ax.set_facecolor('#F8F8F8')
    ax.plot(training)
    ax.plot(validation)
    ax.set_title('model '+ title)
    ax.set_ylabel(title)
    #ax.set_ylim(0.28,1.05)
    ax.set_xlabel('epoch')
    ax.legend(['train', 'valid.'])
"""
## Confusion Matrix ##

A [confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix) shows the actual class of an image tabulated against its predicted class. It is one of the best tools you have for evaluating the performance of a classifier.

The following cell does some processing on the validation data and then creates the matrix with the `confusion_matrix` function included in [`scikit-learn`](https://scikit-learn.org/stable/index.html).
"""
cmdataset = get_validation_dataset(ordered=True)
images_ds = cmdataset.map(lambda image, label: image)
labels_ds = cmdataset.map(lambda image, label: label).unbatch()

cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy()
cm_probabilities = model.predict(images_ds)
cm_predictions = np.argmax(cm_probabilities, axis=-1)

labels = range(len(CLASSES))
cmat = confusion_matrix(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
)
cmat = (cmat.T / cmat.sum(axis=1)).T # normalize
"""
You might be familiar with metrics like [F1-score](https://en.wikipedia.org/wiki/F1_score) or [precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall). This cell will compute these metrics and display them with a plot of the confusion matrix. (These metrics are defined in the Scikit-learn module `sklearn.metrics`; we've imported them in the helper script for you.)
"""
score = f1_score(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
    average='macro',
)
precision = precision_score(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
    average='macro',
)
recall = recall_score(
    cm_correct_labels,
    cm_predictions,
    labels=labels,
    average='macro',
)
display_confusion_matrix(cmat, score, precision, recall)
"""
## Visual Validation ##

It can also be helpful to look at some examples from the validation set and see what class your model predicted. This can help reveal patterns in the kinds of images your model has trouble with.

This cell will set up the validation set to display 20 images at a time -- you can change this to display more or fewer, if you like.
"""
dataset = get_validation_dataset()
dataset = dataset.unbatch().batch(20)
batch = iter(dataset)
"""
And here is a set of flowers with their predicted species. Run the cell again to see another set.
"""
images, labels = next(batch)
probabilities = model.predict(images)
predictions = np.argmax(probabilities, axis=-1)
display_batch_of_images((images, labels), predictions)
"""
# Step 8: Make Test Predictions #

Once you're satisfied with everything, you're ready to make predictions on the test set.
"""
test_ds = get_test_dataset(ordered=True)

print('Computing predictions...')
test_images_ds = test_ds.map(lambda image, idnum: image)
probabilities = model.predict(test_images_ds)
predictions = np.argmax(probabilities, axis=-1)
print(predictions)
"""
We'll generate a file `submission.csv`. This file is what you'll submit to get your score on the leaderboard.
"""
print('Generating submission.csv file...')

# Get image ids from test set and convert to unicode
test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()
test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U')

# Write the submission file
np.savetxt(
    'submission.csv',
    np.rec.fromarrays([test_ids, predictions]),
    fmt=['%s', '%d'],
    delimiter=',',
    header='id,label',
    comments='',
)

# Look at the first few predictions
!head submission.csv
"""
# Step 9: Make a submission #

If you haven't already, create your own editable copy of this notebook by clicking on the **Copy and Edit** button in the top right corner. Then, submit to the competition by following these steps:

1. Begin by clicking on the blue **Save Version** button in the top right corner of the window.  This will generate a pop-up window.  
2. Ensure that the **Save and Run All** option is selected, and then click on the blue **Save** button.
3. This generates a window in the bottom left corner of the notebook.  After it has finished running, click on the number to the right of the **Save Version** button.  This pulls up a list of versions on the right of the screen.  Click on the ellipsis **(...)** to the right of the most recent version, and select **Open in Viewer**.  This brings you into view mode of the same page. You will need to scroll down to get back to these instructions.
4. Click on the **Output** tab on the right of the screen.  Then, click on the file you would like to submit, and click on the blue **Submit** button to submit your results to the leaderboard.

You have now successfully submitted to the competition!

If you want to keep working to improve your performance, select the blue **Edit** button in the top right of the screen. Then you can change your code and repeat the process. There's a lot of room to improve, and you will climb up the leaderboard as you work.

"""
"""
---




*Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161321) to chat with other Learners.*
"""