Kaggle competition: Gendered Pronoun Resolution
<SOS> metric: MulticlassLoss Kaggle data source: gendered-pronoun-resolution<predict_on_test>
import os
import gc
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
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)
 print("Correct labels: ", cm_correct_labels.shape, cm_correct_labels)
 print("Predicted labels: ", cm_predictions.shape, cm_predictions )<compute_train_metric>
!wget -q https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-development.tsv
!wget -q https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-validation.tsv
!wget -q https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-test.tsv
!wget -q https://raw.githubusercontent.com/google-research/bert/master/modeling.py
!wget -q https://raw.githubusercontent.com/google-research/bert/master/extract_features.py
!wget -q https://raw.githubusercontent.com/google-research/bert/master/tokenization.py
cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))
 score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)) , average='macro')
 cmat =(cmat.T / cmat.sum(axis=1)).T
display_confusion_matrix(cmat, score, precision, recall)
 print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))<save_to_csv>
import modeling
import extract_features
import tokenization
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)
 np.save('ensemble_1.npy',probabilities)
 predictions = np.argmax(probabilities, axis=-1)
 print(predictions)
 print('Generating submission.csv file...')
 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')
 np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')
 !head submission.csv<set_options>
val_df = pd.read_table('gap-validation.tsv', index_col='ID' ).reset_index(drop=True)
 test_df = pd.read_table('gap-validation.tsv', index_col='ID' ).reset_index(drop=True)
 dev_df = pd.read_table('gap-development.tsv', index_col='ID' ).reset_index(drop=True )
print("Tensorflow version " + tf.__version__)
 AUTO = tf.data.experimental.AUTOTUNE<set_options>
!wget -q https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip
!unzip uncased_L-12_H-768_A-12.zip
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 )<define_variables>
def count_char(text, offset):
count = 0
for pos in range(offset):
if text[pos] != " ": count +=1
return count
def candidate_length(candidate):
count = 0
for i in range(len(candidate)) :
if candidate[i] != " ": count += 1
return count
def count_token_length_special(token):
count = 0
special_token = ["
for i in range(len(token)) :
if token[i] not in special_token: count+=1
return count
def embed_by_bert(df, path_to_bert='uncased_L-12_H-768_A-12', embed_size=768, batch_size=8,
layers='-1', max_seq_length=256):
text = df['Text']
text.to_csv('input.txt', index=False, header=False)
 os.system(f"python3 extract_features.py \
--input_file=input.txt \
--output_file=output.jsonl \
--vocab_file={path_to_bert}/vocab.txt \
--bert_config_file={path_to_bert}/bert_config.json \
--init_checkpoint={path_to_bert}/bert_model.ckpt \
--layers={layers} \
--max_seq_length={max_seq_length} \
--batch_size={batch_size}")
 bert_output = pd.read_json("output.jsonl", lines=True)
 bert_output.head()
os.system("rm input.txt")
 os.system("rm output.jsonl")
 index = df.index
columns = ["emb_A", "emb_B", "emb_P", "label"]
emb = pd.DataFrame(index = index, columns = columns)
 emb.index.name = "ID"
for i in tqdm(range(len(text))):
features = bert_output.loc[i, "features"]
P_char_start = count_char(df.loc[i, 'Text'], df.loc[i, 'Pronoun-offset'])
 A_char_start = count_char(df.loc[i, 'Text'], df.loc[i, 'A-offset'])
 B_char_start = count_char(df.loc[i, 'Text'], df.loc[i, 'B-offset'])
 A_length = candidate_length(df.loc[i, 'A'])
 B_length = candidate_length(df.loc[i, 'B'])
 emb_A, emb_B, emb_P = np.zeros(embed_size), np.zeros(embed_size), np.zeros(embed_size)
 char_count, cnt_A, cnt_B = 0, 0, 0
for j in range(2, len(features)) :
token = features[j]["token"]
token_length = count_token_length_special(token)
 if char_count == P_char_start:
emb_P += np.asarray(features[j]["layers"][0]['values'])
 if char_count in range(A_char_start, A_char_start + A_length):
emb_A += np.asarray(features[j]["layers"][0]['values'])
 cnt_A += 1
if char_count in range(B_char_start, B_char_start + B_length):
emb_B += np.asarray(features[j]["layers"][0]['values'])
 cnt_B += 1
char_count += token_length
if cnt_A > 0:
emb_A /= cnt_A
if cnt_B > 0:
emb_B /= cnt_B
label = "Neither"
if(df.loc[i,"A-coref"] == True):
label = "A"
if(df.loc[i,"B-coref"] == True):
label = "B"
emb.iloc[i] = [emb_A, emb_B, emb_P, label]
return emb
GCS_DS_PATH = KaggleDatasets().get_gcs_path() 
<define_variables>
%%time
val_bert_emb = embed_by_bert(val_df)
 test_bert_emb = embed_by_bert(test_df)
 dev_bert_emb = embed_by_bert(dev_df )
IMAGE_SIZE = [512, 512]
LR = 0.001
EPOCHS = 20
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
GCS_PATH_SELECT = {
192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]
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',
'snapdragon', "colt's foot", 'king protea', 'spear thistle', 'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower', 'giant white arum lily',
'fire lily', 'pincushion flower', 'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william',
'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip', 'lenten rose',
'barberton daisy', 'daffodil', 'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy', 'common dandelion',
'petunia', 'wild pansy', 'primula', 'sunflower', 'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia', 'pink-yellow dahlia',
'cautleya spicata', 'japanese anemone', 'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris', 'windflower', 'tree poppy',
'gazania', 'azalea', 'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily', 'anthurium',
'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily',
'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea', 'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower',
'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose']<install_modules>
val_bert_emb["emb_A"].head().map(np.asarray ).values[0].astype('float' ).shape
!pip install -q efficientnet<import_modules>
def featurize(embedding_df):
pronoun_embs, a_embs, b_embs, labels = [], [], [], []
for i in tqdm(range(len(embedding_df))):
pronoun_embs.append(embedding_df.loc[i, "emb_P"])
 a_embs.append(embedding_df.loc[i, "emb_A"])
 b_embs.append(embedding_df.loc[i, "emb_B"])
 label_map = {'A': 0, 'B': 1, 'Neither': 2}
labels.append(label_map[embedding_df.loc[i, "label"]])
 a_embs = np.asarray(a_embs ).astype('float')
 b_embs = np.asarray(b_embs ).astype('float')
 pronoun_embs = np.asarray(pronoun_embs ).astype('float')
 return np.concatenate([a_embs, b_embs, pronoun_embs], axis=1), np.asarray(labels )
import efficientnet.tfkeras as efn<define_variables>
X_train, y_train = featurize(pd.concat([val_bert_emb, dev_bert_emb] ).sort_index().reset_index() )
SKIP_VALIDATION = True
if SKIP_VALIDATION:
TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES<categorify>
logit = LogisticRegression(C=1e-2, random_state=17, solver='lbfgs',
multi_class='multinomial', max_iter=100,
n_jobs=4 )
np.set_printoptions(threshold=15, linewidth=80)
 def batch_to_numpy_images_and_labels(data):
images, labels = data
numpy_images = images.numpy()
numpy_labels = labels.numpy()
if numpy_labels.dtype == object:
numpy_labels = [None for _ in enumerate(numpy_images)]
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):

 images, labels = batch_to_numpy_images_and_labels(databatch)
 if labels is None:
labels = [None for _ in enumerate(images)]
rows = int(math.sqrt(len(images)))
 cols = len(images)//rows
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))
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
subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)
 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_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 += '
precision = {:.3f} '.format(precision)
 if recall is not None:
titlestring += '
recall = {:.3f} '.format(recall)
 if len(titlestring)> 0:
ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'
plt.show()
def display_training_curves(training, validation, title, subplot):
if subplot%10==1:
plt.subplots(figsize=(10,10), facecolor='
plt.tight_layout()
ax = plt.subplot(subplot)
 ax.set_facecolor('
ax.plot(training)
 ax.plot(validation)
 ax.set_title('model '+ title)
 ax.set_ylabel(title)
 ax.set_xlabel('epoch')
 ax.legend(['train', 'valid.'] )<normalization>
%%time
logit.fit(X_train, y_train )
def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = math.pi * rotation / 180.
 shear = math.pi * shear / 180.
 c1 = tf.math.cos(rotation)
 s1 = tf.math.sin(rotation)
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 rotation_matrix = tf.reshape(tf.concat([c1,s1,zero, -s1,c1,zero, zero,zero,one],axis=0),[3,3])
 c2 = tf.math.cos(shear)
 s2 = tf.math.sin(shear)
 shear_matrix = tf.reshape(tf.concat([one,s2,zero, zero,c2,zero, zero,zero,one],axis=0),[3,3])
 zoom_matrix = tf.reshape(tf.concat([one/height_zoom,zero,zero, zero,one/width_zoom,zero, zero,zero,one],axis=0),[3,3])
 shift_matrix = tf.reshape(tf.concat([one,zero,height_shift, zero,one,width_shift, zero,zero,one],axis=0),[3,3])
 return K.dot(K.dot(rotation_matrix, shear_matrix), K.dot(zoom_matrix, shift_matrix))
def random_rotate(image):
DIM = IMAGE_SIZE[0]
XDIM = DIM%2
rot = 15.* tf.random.normal([1],dtype='float32')
 shr = 5.* tf.random.normal([1],dtype='float32')
 h_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.
 w_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.
 h_shift = 16.* tf.random.normal([1],dtype='float32')
 w_shift = 16.* tf.random.normal([1],dtype='float32')
 m = get_mat(rot,shr,h_zoom,w_zoom,h_shift,w_shift)
 x = tf.repeat(tf.range(DIM//2,-DIM//2,-1), DIM)
 y = tf.tile(tf.range(-DIM//2,DIM//2),[DIM])
 z = tf.ones([DIM*DIM],dtype='int32')
 idx = tf.stack([x,y,z])
 idx2 = K.dot(m,tf.cast(idx,dtype='float32'))
idx2 = K.cast(idx2,dtype='int32')
 idx2 = K.clip(idx2,-DIM//2+XDIM+1,DIM//2)
 idx3 = tf.stack([DIM//2-idx2[0,], DIM//2-1+idx2[1,]])
 d = tf.gather_nd(image,tf.transpose(idx3))
return tf.reshape(d,[DIM,DIM,3] )<categorify>
!cp gap-development.tsv stage1_test.tsv
def decode_image(image_data):
image = tf.image.decode_jpeg(image_data, channels=3)
 image = tf.cast(image, tf.float32)/ 255.0
image = tf.reshape(image, [*IMAGE_SIZE, 3])
 return image
def read_labeled_tfrecord(example):
LABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"class": tf.io.FixedLenFeature([], tf.int64),
}
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
def read_unlabeled_tfrecord(example):
UNLABELED_TFREC_FORMAT = {
"image": tf.io.FixedLenFeature([], tf.string),
"id": tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)
 image = decode_image(example['image'])
 idnum = example['id']
return image, idnum
def load_dataset(filenames, labeled=True, ordered=False):
ignore_order = tf.data.Options()
if not ordered:
ignore_order.experimental_deterministic = False
dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
 dataset = dataset.with_options(ignore_order)
 dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)
 return dataset
def data_augment(image, label):
image = tf.image.random_flip_left_right(image)
 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()
dataset = dataset.shuffle(2048)
 dataset = dataset.batch(BATCH_SIZE)
 dataset = dataset.prefetch(AUTO)
 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):
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 = 0 if SKIP_VALIDATION else count_data_items(VALIDATION_FILENAMES)
 NUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)
 STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
print('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))<choose_model_class>
stage1_test_df = pd.read_table('stage1_test.tsv', index_col='ID' ).reset_index(drop=True )
with strategy.scope() :
pretrained_model = efn.EfficientNetB7(weights='imagenet', include_top=False, input_shape=[*IMAGE_SIZE, 3])
 pretrained_model.trainable = True
model = tf.keras.Sequential([
pretrained_model,
tf.keras.layers.GlobalAveragePooling2D() ,
tf.keras.layers.Dense(len(CLASSES), activation='softmax')
 ])



model.compile(
 optimizer='adam',
loss = 'sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'] 
)
 model.summary()<choose_model_class>
%%time
stage1_test_bert_emb = embed_by_bert(stage1_test_df )
scheduler = tf.keras.callbacks.ReduceLROnPlateau(patience=3, verbose=1)
 LR_START = 0.00001
LR_MAX = 0.00005 * strategy.num_replicas_in_sync
LR_MIN = 0.00001
LR_RAMPUP_EPOCHS = 5
LR_SUSTAIN_EPOCHS = 0
LR_EXP_DECAY =.8
def lrfn(epoch):
if epoch < LR_RAMPUP_EPOCHS:
lr =(LR_MAX - LR_START)/ LR_RAMPUP_EPOCHS * epoch + LR_START
elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:
lr = LR_MAX
else:
lr =(LR_MAX - LR_MIN)* LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS)+ LR_MIN
return lr
lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)
 rng = [i for i in range(EPOCHS)]
y = [lrfn(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]))<train_model>
X_test, y_test = featurize(stage1_test_bert_emb )
history = model.fit(get_training_dataset() , steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS,
callbacks=[lr_callback],
validation_data=None if SKIP_VALIDATION else get_validation_dataset() )<predict_on_test>
logit_test_pred = logit.predict_proba(X_test)
 log_loss(y_test, logit_test_pred )
<compute_train_metric><EOS> 
submission = pd.read_csv(".. /input/sample_submission_stage_1.csv", index_col = "ID")
 submission["A"] = logit_test_pred[:, 0]
submission["B"] = logit_test_pred[:, 1]
submission["NEITHER"] = logit_test_pred[:, 2]
submission.to_csv("submission.csv" )