Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<string_transform>
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nY5bIayd0ItKMsm1Zlpm/j8q1Wpq5eJnmCLdIR/H3221215ppCaG6tA8QEX3WqzM83TXrv7FoYoSf6/7Iw82IkVljnGN3uCPt6s5kWktHgEnbBxX70KR66Yp5R+gwXGbcfG8DlSnq3uOyKeS7vBcG5kSjOGJA5wEGloxFB2eoyfFEam9H5lr5w5xU2xtMaFU2640BIYAILj8DHwszQ7g0VK1XO9NBwaEuz7DSACesQC48V2M681CEvT0/vEnZrfcULod2rby7gEEna22pa1ujcyfjrIQ95jveBB8syJOITPUDqCxiLVLfA8o0iPhFRSjl4EWCFhTWuMl/AQF1pS9ZTQAA', 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', 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'src/utils/splitter.py': 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', 'src/utils/transformers.py': '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', 'src/utils/cache.py': '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', 'src/utils/environment.py': 'H4sIALjOw18C/41Pu27DMAzc9RWEF8eLP8BAhg4NkK1Dl06GItMxAUVyKCpN/j7yI7BQdAi3I+/BE340CtLQZfQscLG99b/zph10aBcMe/jmiArvBkeB48z9ZPbc/EM9aBtQKdVhD4zXSIyv665a03pwXnLdsp+GNQXMM3blj4/gEDsQD+SCaGvXT+GEvWeEGMidQQYK8PF1rMsq5cufbuLZDNu/M3yr2YuZFzujtB3eyGDWaWNndVAiuyW8XhWFiZ0uJsWynmBNodU3TVafbPIETFlQmDEW1ew14c21TIdSPQG0FUw+vgEAAA=='}
for path, encoded in file_data.items() :
print(path)
 path = Path(path)
 path.parent.mkdir(exist_ok=True)
 path.write_bytes(gzip.decompress(base64.b64decode(encoded)))
 def run(command):
os.system('echo "
os.system('export PYTHONPATH=${PYTHONPATH}:/kaggle/working && ' + command)
 run('python setup.py develop --install-dir /kaggle/working')
 print('b4e58086057ee83c7234b744eb37e39aee01efc3' )
print("Fitting tokenizer...")
 tokenizer = Tokenizer(num_words=num_words)
 tokenizer.fit_on_texts(train_df['Description'].values.tolist() + test_df['Description'].values.tolist() )<feature_engineering>
logger = LoggerFactory('notebook' ).getLogger('notebook')
 seed_everything()
train_df['Description'] = tokenizer.texts_to_sequences(train_df['Description'])
 test_df['Description'] = tokenizer.texts_to_sequences(test_df['Description'] )<feature_engineering>
(
train_features_df,
stage_1_1_target_cols,
stage_1_2_target_cols,
stage_1_train_features, 
)= preprocess.preprocess_train()
test_features_df, sample_submission_df = preprocess.preprocess_test(train_features_df=train_features_df )
avg_word_length = []
desc_length = []
number_words = []
for desc in train_df["Description1"]:
desc_length.append(len(desc))
words = desc.split()
number_words.append(len(words))
word_len = 0
for word in words:
word_len += len(word)
 avg_word_length.append(word_len / len(words))
train_df["average_word_length"] = avg_word_length
train_df["num_words"] = number_words
train_df["desc_char_length"] = desc_length<feature_engineering>
def get_feature(df):
features_g = list([x for x in df.columns if x.startswith("g-")])
 features_c = list([x for x in df.columns if x.startswith("c-")])
 df["g_sum"] = df[features_g].sum(axis=1)
 df["g_mean"] = df[features_g].mean(axis=1)
 df["g_median"] = df[features_g].median(axis=1)
 df["g_std"] = df[features_g].std(axis=1)
 df["g_kurt"] = df[features_g].kurtosis(axis=1)
 df["g_skew"] = df[features_g].skew(axis=1)
 df["c_sum"] = df[features_c].sum(axis=1)
 df["c_mean"] = df[features_c].mean(axis=1)
 df["c_std"] = df[features_c].std(axis=1)
 df["c_median"] = df[features_c].median(axis=1)
 df["c_kurt"] = df[features_c].kurtosis(axis=1)
 df["c_skew"] = df[features_c].skew(axis=1)
 df["gc_sum"] = df[features_g + features_c].sum(axis=1)
 df["gc_mean"] = df[features_g + features_c].mean(axis=1)
 df["gc_std"] = df[features_g + features_c].std(axis=1)
 df["gc_kurt"] = df[features_g + features_c].kurtosis(axis=1)
 df["gc_skew"] = df[features_g + features_c].skew(axis=1)
 df["gc_median"] = df[features_g + features_c].median(axis=1)
 return df
avg_word_length = []
desc_length = []
number_words = []
for desc in test_df["Description1"]:
desc_length.append(len(desc))
words = desc.split()
number_words.append(len(words))
word_len = 0
for word in words:
word_len += len(word)
 avg_word_length.append(word_len / len(words))
test_df["average_word_length"] = avg_word_length
test_df["num_words"] = number_words
test_df["desc_char_length"] = desc_length<feature_engineering>
n_comp_CELLS = 60
train_features_df = get_feature(train_features_df)
 features_c = list([x for x in train_features_df.columns if x.startswith("c-")])
 pca = PCA(n_components=n_comp_CELLS, random_state=42)
 pca_feat = [f'pca_C-{i}' for i in range(n_comp_CELLS)]
train_features_df[pca_feat] = pca.fit_transform(train_features_df[features_c])
 transformer = normalizer(transformer='quantile', params=dict(n_quantiles=100, random_state=42, output_distribution="normal"), df=train_features_df.loc[:, stage_1_train_features])
 train_features_df[stage_1_train_features] = transformer.transform(train_features_df.loc[:, stage_1_train_features] )
name_len = []
for name in train_df["Name"]:
try:
name_len.append(len(name))
except:
name_len.append(-1)
 train_df["name_len"] = name_len<feature_engineering>
test_features_df = get_feature(test_features_df)
 test_features_df[pca_feat] = pca.transform(test_features_df[features_c])
 test_features_df[stage_1_train_features] = transformer.transform(test_features_df.loc[:, stage_1_train_features] )
name_len = []
for name in test_df["Name"]:
try:
name_len.append(len(name))
except:
name_len.append(-1)
 test_df["name_len"] = name_len<define_variables>
stage_1_train_features += pca_feat
state_gdp = {
41336: 116.679,
41325: 40.596,
41367: 23.02,
41401: 190.075,
41415: 5.984,
41324: 37.274,
41332: 42.389,
41335: 52.452,
41330: 67.629,
41380: 5.642,
41327: 81.284,
41345: 80.167,
41342: 121.414,
41326: 280.698,
41361: 32.270
}
state_population = {
41336: 33.48283,
41325: 19.47651,
41367: 15.39601,
41401: 16.74621,
41415: 0.86908,
41324: 8.21110,
41332: 10.21064,
41335: 15.00817,
41330: 23.52743,
41380: 2.31541,
41327: 15.61383,
41345: 32.06742,
41342: 24.71140,
41326: 54.62141,
41361: 10.35977
}
train_df["state_gdp"] = train_df.State.map(state_gdp)
 train_df["state_population"] = train_df.State.map(state_population)
 test_df["state_gdp"] = test_df.State.map(state_gdp)
 test_df["state_population"] = test_df.State.map(state_population )<feature_engineering>
from typing import List
import pandas as pd
from src.utils.cache import Cache
from src.utils.splitter import splitter_for_moa
from src.metrics import logloss_for_multilabel
word_index = tokenizer.word_index
nb_words = min(num_words, len(word_index))
embedding_matrix = np.zeros(( nb_words, embed_size))
for word, i in word_index.items() :
if i >= nb_words: continue
try:
embedding_vector = embeddings_index[word]
except:
embedding_vector = None
if embedding_vector is not None: embedding_matrix[i] = embedding_vector<categorify>
@Cache('./cache')
 def build_trainer(trainer: MoaBase, df: pd.DataFrame, predictors: List[str], target_cols: List[str], num_seed_blends: int, n_splits: int, params: dict):
folds = splitter_for_moa(df, target_cols, n_splits=n_splits)
 model = trainer(
 target_cols = target_cols,
categorical_cols=[],
ignore_cols=[],
num_seed_blends=num_seed_blends,
metric=logloss_for_multilabel,
params=params,
exp=None 
)
 model.train(df[predictors], df[target_cols], folds)
 return model
stage1_1_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=train_features_df,
target_cols=stage_1_1_target_cols,
predictors=stage_1_train_features,
num_seed_blends=5,
n_splits=7,
params={}, 
)
 stage1_2_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=train_features_df,
target_cols=stage_1_2_target_cols,
predictors=stage_1_train_features,
num_seed_blends=5,
n_splits=7,
params={},
)
for col in cat_cols:
le = LabelEncoder()
le.fit(train_df[col].tolist() + test_df[col].tolist())
 train_df[col] = le.transform(train_df[col].tolist())
 test_df[col] = le.transform(test_df[col].tolist())
 embed_sizes = [len(set(list(train_df[col].unique())+ list(test_df[col].unique())))+ 1 for col in cat_cols]<drop_column>
stage1_2_xgb_trainer = build_trainer(
 trainer=XGBTrainer,
df=train_features_df,
target_cols=stage_1_2_target_cols,
predictors=stage_1_train_features,
num_seed_blends=2,
n_splits=5,
params={},
)
agg_list = ["Age", "Gender", "FurLength", "Vaccinated", "Dewormed", "Sterilized",
"Health", "Quantity", "Fee", "PhotoAmt", "VideoAmt", "Color1", "Color2", "Color3", "vertex_y", "label_score"]
train_df[agg_list]<categorify>
folds = splitter_for_moa(train_features_df, stage_1_2_target_cols, n_splits=4)
 stage1_2_svm_trainer = SVMTrainer(
 target_cols = stage_1_2_target_cols,
categorical_cols=[],
ignore_cols=[],
num_seed_blends=1,
metric=logloss_for_multilabel,
params={
},
exp=None 
)
 stage1_2_svm_trainer.train_predict(
 X_train=train_features_df[stage_1_train_features],
y_train=train_features_df[stage_1_2_target_cols],
X_test=test_features_df[stage_1_train_features],
folds=folds
)
for col in agg_list:
means = train_df.groupby("RescuerID")[col].mean()
train_df["agg_mean_" + col] = train_df["RescuerID"].map(means)
 means = test_df.groupby("RescuerID")[col].mean()
test_df["agg_mean_" + col] = test_df["RescuerID"].map(means)
 num_cols.append("agg_mean_" + col)
 for col in agg_list:
stds = train_df.groupby("RescuerID")[col].std()
stds = stds.fillna(stds.mean())
 train_df["agg_std_" + col] = train_df["RescuerID"].map(stds)
 stds = test_df.groupby("RescuerID")[col].std()
test_df["agg_std_" + col] = test_df["RescuerID"].map(stds)
 num_cols.append("agg_std_" + col )<create_dataframe>
stage1_1_nn_pred = stage1_1_nn_trainer.predict(test_features_df)
 stage1_2_nn_pred = stage1_2_nn_trainer.predict(test_features_df)
 stage1_1_tabnet_pred = stage1_1_tabnet_trainer.predict(test_features_df)
 stage1_2_tabnet_pred = stage1_2_tabnet_trainer.predict(test_features_df)
 stage1_2_svm_pred = stage1_2_svm_trainer.result.preds
stage1_2_xgb_pred = stage1_2_xgb_trainer.predict(test_features_df )
train_df_copy = deepcopy(train_df)
 test_df_copy = deepcopy(test_df )<train_model>
stack_train_df = pd.concat(
 [
pd.DataFrame(stage1_1_nn_trainer.result.oof_preds ).add_prefix("stage1_1_nn_"),
pd.DataFrame(stage1_2_nn_trainer.result.oof_preds ).add_prefix("stage1_2_nn_"),
train_features_df[stage_1_2_target_cols + ["drug_id", "sig_id"]],
],
axis=1, 
)
 stack_test_df = pd.concat(
 [
pd.DataFrame(stage1_1_nn_pred ).add_prefix("stage1_1_nn_"),
pd.DataFrame(stage1_2_nn_pred ).add_prefix("stage1_2_nn_"),
],
axis=1, 
)
 stack_train_predictors = [x for x in stack_train_df.columns if x not in stage_1_2_target_cols + ["drug_id", "sig_id"]]
stack_nn_trainer = train_nn_model(
 df=stack_train_df,
target_cols=stage_1_2_target_cols,
predictors=stack_train_predictors,
model_class=TabularMLP_2,
num_seed_blends=5,
n_splits=7 
)
 stacking_nn_pred = stack_nn_trainer.predict(stack_test_df )
img_size = 256
batch_size = 16
pet_ids = train_df['PetID'].values
n_batches = len(pet_ids)// batch_size +(len(pet_ids)% batch_size != 0)
 def resize_to_square(im):
old_size = im.shape[:2]
ratio = float(img_size)/max(old_size)
 new_size = tuple([int(x*ratio)for x in old_size])
 im = cv2.resize(im,(new_size[1], new_size[0]))
delta_w = img_size - new_size[1]
delta_h = img_size - new_size[0]
top, bottom = delta_h//2, delta_h-(delta_h//2)
 left, right = delta_w//2, delta_w-(delta_w//2)
 color = [0, 0, 0]
new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,value=color)
 return new_im
def load_image(path, pet_id):
image = cv2.imread(f'{path}{pet_id}-1.jpg')
 new_image = resize_to_square(image)
 new_image = preprocess_input(new_image)
 return new_image
inp = Input(( 256,256,3))
backbone = DenseNet121(input_tensor = inp, include_top = False, weights = None)
 backbone.load_weights(".. /input/densenet-keras/DenseNet-BC-121-32-no-top.h5")
 print("weights loaded succesfully")
 x = backbone.output
x = GlobalAveragePooling2D()(x)
 x = Lambda(lambda x: K.expand_dims(x,axis = -1))(x)
 out = Lambda(lambda x: x[:,:,0] )(x)
 m = Model(inp,out)
 features = {}
for b in tqdm(range(n_batches)) :
start = b*batch_size
end =(b+1)*batch_size
batch_pets = pet_ids[start:end]
batch_images = np.zeros(( len(batch_pets),img_size,img_size,3))
for i,pet_id in enumerate(batch_pets):
try:
batch_images[i] = load_image(".. /input/petfinder-adoption-prediction/train_images/", pet_id)
 except:
pass
batch_preds = m.predict(batch_images)
 for i,pet_id in enumerate(batch_pets):
features[pet_id] = batch_preds[i]
train_feats = pd.DataFrame.from_dict(features, orient='index' ).values
n_components = 32
svd = TruncatedSVD(n_components=n_components)
 svd.fit(train_feats)
 train_feats = svd.transform(train_feats)
 train_feats = pd.DataFrame(train_feats, columns=['img_svd1_{}'.format(i)for i in range(n_components)])
 train_feats.to_csv('train_img_features.csv')
 train_df = pd.concat([train_df, train_feats], axis = 1)
 pet_ids = test_df['PetID'].values
n_batches = len(pet_ids)// batch_size +(len(pet_ids)% batch_size != 0)
 features = {}
for b in tqdm(range(n_batches)) :
start = b*batch_size
end =(b+1)*batch_size
batch_pets = pet_ids[start:end]
batch_images = np.zeros(( len(batch_pets),img_size,img_size,3))
for i,pet_id in enumerate(batch_pets):
try:
batch_images[i] = load_image(".. /input/petfinder-adoption-prediction/test_images/", pet_id)
 except:
pass
batch_preds = m.predict(batch_images)
 for i,pet_id in enumerate(batch_pets):
features[pet_id] = batch_preds[i]
test_feats = pd.DataFrame.from_dict(features, orient='index' ).values
test_feats = svd.transform(test_feats)
 test_feats = pd.DataFrame(test_feats, columns=['img_svd1_{}'.format(i)for i in range(n_components)])
 test_feats.to_csv('test_img_features.csv')
 test_df = pd.concat(( test_df.reset_index(drop = True), test_feats), axis=1)
 print(train_df.shape, test_df.shape )<normalization>
stack_train_df = pd.concat(
 [
pd.DataFrame(stage1_1_tabnet_trainer.result.oof_preds ).add_prefix('stage1_1_tabnet_'),
pd.DataFrame(stage1_2_tabnet_trainer.result.oof_preds ).add_prefix('stage1_2_tabnet_'),
train_features_df[stage_1_2_target_cols + ["drug_id", "sig_id"]],
],
axis=1, 
)
 stack_test_df = pd.concat(
 [
pd.DataFrame(stage1_1_tabnet_pred ).add_prefix("stage1_1_tabnet_"),
pd.DataFrame(stage1_2_tabnet_pred ).add_prefix("stage1_2_tabnet_"),
],
axis=1, 
)
 stack_train_predictors = [x for x in stack_train_df.columns if x not in stage_1_2_target_cols + ["drug_id", "sig_id"]]
stack_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=stack_train_df,
target_cols=stage_1_2_target_cols,
predictors=stack_train_predictors,
num_seed_blends=5,
n_splits=7,
params={}, 
)
 stacking_tabnet_pred = stack_tabnet_trainer.predict(stack_test_df )
catvsdog = load_model(".. /input/kerascatvsdog/best.hd5")
 img_size = 299
batch_size = 16
pet_ids = train_df['PetID'].values
n_batches = len(pet_ids)// batch_size +(len(pet_ids)% batch_size != 0)
 def resize_to_square(im):
old_size = im.shape[:2]
ratio = float(img_size)/max(old_size)
 new_size = tuple([int(x*ratio)for x in old_size])
 im = cv2.resize(im,(new_size[1], new_size[0]))
delta_w = img_size - new_size[1]
delta_h = img_size - new_size[0]
top, bottom = delta_h//2, delta_h-(delta_h//2)
 left, right = delta_w//2, delta_w-(delta_w//2)
 color = [0, 0, 0]
new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,value=color)
 return new_im
def load_image(path, pet_id):
image = cv2.imread(f'{path}{pet_id}-1.jpg')
 new_image = resize_to_square(image)
 new_image = preprocess_input(new_image)
 return new_image 
<define_variables>
stage1_2_trainers = {
'nn': stage1_2_nn_trainer,
'tabnet': stage1_2_tabnet_trainer,
'svm': stage1_2_svm_trainer,
'stack_nn': stack_nn_trainer,
'stack_tabnet': stack_tabnet_trainer,
'xgb': stage1_2_xgb_trainer,
}
for name, trainer in stage1_2_trainers.items() :
print(name, logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, trainer.result.oof_preds))
backbone = catvsdog
out = backbone.layers[-2].output
m = Model(backbone.input, out)
 features = {}
for b in tqdm(range(n_batches)) :
start = b*batch_size
end =(b+1)*batch_size
batch_pets = pet_ids[start:end]
batch_images = np.zeros(( len(batch_pets),img_size,img_size,3))
for i,pet_id in enumerate(batch_pets):
try:
batch_images[i] = load_image(".. /input/petfinder-adoption-prediction/train_images/", pet_id)
 except:
pass
batch_preds = m.predict(batch_images)
 for i,pet_id in enumerate(batch_pets):
features[pet_id] = batch_preds[i]
train_feats = pd.DataFrame.from_dict(features, orient='index' ).values
n_components = 32
svd = TruncatedSVD(n_components=n_components)
 svd.fit(train_feats)
 train_feats = svd.transform(train_feats)
 train_feats = pd.DataFrame(train_feats, columns=['img_svd2_{}'.format(i)for i in range(n_components)])
 train_feats.to_csv('train_img_features.csv')
 train_df = pd.concat([train_df, train_feats], axis = 1)
 pet_ids = test_df['PetID'].values
n_batches = len(pet_ids)// batch_size + 1
features = {}
for b in tqdm(range(n_batches)) :
start = b*batch_size
end =(b+1)*batch_size
batch_pets = pet_ids[start:end]
batch_images = np.zeros(( len(batch_pets),img_size,img_size,3))
for i,pet_id in enumerate(batch_pets):
try:
batch_images[i] = load_image(".. /input/petfinder-adoption-prediction/test_images/", pet_id)
 except:
pass
batch_preds = m.predict(batch_images)
 for i,pet_id in enumerate(batch_pets):
features[pet_id] = batch_preds[i]
test_feats = pd.DataFrame.from_dict(features, orient='index' ).values
test_feats = svd.transform(test_feats)
 test_feats = pd.DataFrame(test_feats, columns=['img_svd2_{}'.format(i)for i in range(n_components)])
 test_feats.to_csv('test_img_features.csv')
 test_df = pd.concat(( test_df.reset_index(drop = True), test_feats), axis=1)
 print(train_df.shape, test_df.shape )<normalization>
stage3_oofs = [
stack_nn_trainer.result.oof_preds,
stage1_2_svm_trainer.result.oof_preds,
stage1_2_tabnet_trainer.result.oof_preds,
stage1_2_xgb_trainer.result.oof_preds,
]
stage3_preds = [
stacking_nn_pred,
stage1_2_svm_pred,
stage1_2_tabnet_pred,
stage1_2_xgb_pred,
]
n_components = 32
train_desc = train_df.Description1.fillna("none" ).values
test_desc = test_df.Description1.fillna("none" ).values
print(test_desc.shape)
 tfv = TfidfVectorizer(min_df=50, max_features=None,
strip_accents='unicode', analyzer='word', token_pattern=r'(?u)\b\w+\b',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1, 
)
 tfv.fit(list(train_desc))
X = tfv.transform(train_desc)
 X_test = tfv.transform(test_desc)
 svd = TruncatedSVD(n_components=n_components)
 svd.fit(X)
 X = svd.transform(X)
 X = pd.DataFrame(X, columns=['svd1_{}'.format(i)for i in range(n_components)])
 train_df = pd.concat(( train_df, X), axis=1)
 X_test = svd.transform(X_test)
 X_test = pd.DataFrame(X_test, columns=['svd1_{}'.format(i)for i in range(n_components)])
 test_df = pd.concat(( test_df.reset_index(drop = True), X_test), axis=1)
 print(test_df.shape )<drop_column>
DEVICE = get_device()
class CNNStacking(nn.Module):
def __init__(self, n_features, n_labels):
super(CNNStacking, self ).__init__()
self.sq = nn.Sequential(
 nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3, 1), bias=False),
nn.ReLU() ,
nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(2, 1), bias=False),
nn.ReLU() ,
nn.Flatten() ,
nn.Linear(in_features=16* n_labels, out_features=4 * n_labels),
nn.ReLU() ,
nn.Linear(in_features=4 * n_labels, out_features=n_labels), 
)
 def forward(self, x):
return self.sq(x)
 class CNNDataset(Dataset):
def __init__(self, X: np.ndarray, y: Optional[pd.DataFrame]):
self.X = X
if y is not None:
self.y = y.values
else:
self.y = y
def __len__(self):
return self.X.shape[0]
def __getitem__(self, idx):
if self.y is None:
return torch.tensor(self.X[idx], dtype=torch.float ).to(DEVICE)
 else:
return(
 torch.tensor(self.X[idx], dtype=torch.float ).to(DEVICE),
torch.tensor(self.y[idx], dtype=torch.float ).to(DEVICE), 
)
 class CNNTrainer(MoaBase):
def __init__(self, params: Optional[dict] = None, **kwargs):
if params is None:
self.params = {}
else:
self.params = params
super().__init__(**kwargs)
 def _get_default_params(self):
return {
'lr': 1e-4,
'batch_size': 256,
'epoch': 20,
}
def _train(self, X: pd.DataFrame, y: pd.DataFrame, predictors: List[str], train_idx: np.ndarray, valid_idx: np.ndarray, seed: int):
X_train, X_valid = X.iloc[train_idx], X.iloc[valid_idx]
y_train, y_valid = y.iloc[train_idx], y.iloc[valid_idx]
target_cols = y_valid.columns.tolist()
_params = self._get_default_params()
_params.update(self.params)
 self.n_predictors = len(predictors)
 self.n_targets = len(target_cols)
 self.n_models = self.n_predictors // self.n_targets
num_epoch = _params['epoch']
batch_size = _params['batch_size']
net = CNNStacking(n_features=self.n_predictors, n_labels=self.n_targets)
 net.to(DEVICE)
 optimizer = RAdam(net.parameters() , lr=_params['lr'])
 valid_criterion = nn.BCEWithLogitsLoss()
criterion = SmoothBCEwLogits(smoothing=0.001)
 scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10, eta_min=0.0001)
 X_train = X_train[predictors].values.reshape(-1, self.n_models, self.n_targets, 1 ).transpose(0, 3, 1, 2)
 X_valid = X_valid[predictors].values.reshape(-1, self.n_models, self.n_targets, 1 ).transpose(0, 3, 1, 2)
 train_dataset = CNNDataset(X_train, y_train)
 train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
 valid_dataset = CNNDataset(X_valid, y_valid)
 valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
 bar = trange(num_epoch, desc=f"seed: {seed} train : {X_train.shape[0]} valid:{X_valid.shape[0]}====")
 train_loss = []
valid_loss = []
best_loss = np.inf
best_preds = None
best_loss_epoch = 1
for epoch in bar:
running_loss = []
valid_loss = []
net.train()
for x, y in train_dataloader:
x = x.to(DEVICE)
 y = y.to(DEVICE)
 optimizer.zero_grad()
out = net(x)
 loss = criterion(out, y)
 loss.backward()
running_loss.append(loss.item())
 optimizer.step()
scheduler.step()
preds_valid = []
_valid_loss = []
net.eval()
with torch.no_grad() :
for x, y in valid_dataloader:
x = x.to(DEVICE)
 y = y.to(DEVICE)
 out = net(x)
 loss = valid_criterion(out, y)
 preds_valid.append(out.sigmoid().detach().cpu().numpy())
 _valid_loss.append(loss.item())
 bar.set_postfix(
 running_loss=f"{np.mean(running_loss):.5f}",
valid_loss=f"{np.mean(_valid_loss):.5f}",
best_loss=f"{best_loss:.5f}",
best_loss_epoch=f"{best_loss_epoch}", 
)
 train_loss.append(np.mean(running_loss))
valid_loss.append(np.mean(_valid_loss))
if best_loss > np.mean(_valid_loss):
best_loss = np.mean(_valid_loss)
 best_loss_epoch = epoch + 1
best_preds = np.concatenate(preds_valid)
 best_state = copy.deepcopy(net.state_dict())
 logger.info(f"best loss : {best_loss}")
 model = CNNStacking(n_features=self.n_predictors, n_labels=self.n_targets)
 model.load_state_dict(best_state)
 model.to(DEVICE)
 return best_preds, model
def _predict(self, model: Any, X_valid: pd.DataFrame, predictors: List[str]):
_params = self._get_default_params()
_params.update(self.params)
 X_valid = X_valid[predictors].values.reshape(-1, self.n_models, self.n_targets, 1 ).transpose(0, 3, 1, 2)
 batch_size = _params['batch_size']
valid_dataset = CNNDataset(X_valid, None)
 valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
 tmp_pred = []
model.eval()
with torch.no_grad() :
for x in valid_dataloader:
x = x.to(DEVICE)
 out = model(x)
 tmp_pred.append(out.sigmoid().detach().cpu().numpy())
 return np.concatenate(tmp_pred )
<define_variables>
cnn_preds = cnn_trainer.predict(make_cnn_inputs(stage3_preds, stage_1_2_target_cols))
split_char = '/'
train_df_ids = train_df[['PetID']]
test_df_ids = test_df[['PetID']]
train_df_imgs = pd.DataFrame(train_image_files)
 train_df_imgs.columns = ['image_filename']
train_imgs_pets = train_df_imgs['image_filename'].apply(lambda x: x.split(split_char)[-1].split('-')[0])
 test_df_imgs = pd.DataFrame(test_image_files)
 test_df_imgs.columns = ['image_filename']
test_imgs_pets = test_df_imgs['image_filename'].apply(lambda x: x.split(split_char)[-1].split('-')[0])
 train_df_imgs = train_df_imgs.assign(PetID=train_imgs_pets)
 test_df_imgs = test_df_imgs.assign(PetID=test_imgs_pets)
 def getSize(filename):
st = os.stat(filename)
 return st.st_size
def getDimensions(filename):
img_size = Image.open(filename ).size
return img_size
train_df_imgs['image_size'] = train_df_imgs['image_filename'].apply(getSize)
 train_df_imgs['temp_size'] = train_df_imgs['image_filename'].apply(getDimensions)
 train_df_imgs['width'] = train_df_imgs['temp_size'].apply(lambda x : x[0])
 train_df_imgs['height'] = train_df_imgs['temp_size'].apply(lambda x : x[1])
 train_df_imgs = train_df_imgs.drop(['temp_size'], axis=1)
 test_df_imgs['image_size'] = test_df_imgs['image_filename'].apply(getSize)
 test_df_imgs['temp_size'] = test_df_imgs['image_filename'].apply(getDimensions)
 test_df_imgs['width'] = test_df_imgs['temp_size'].apply(lambda x : x[0])
 test_df_imgs['height'] = test_df_imgs['temp_size'].apply(lambda x : x[1])
 test_df_imgs = test_df_imgs.drop(['temp_size'], axis=1)
 aggs = {
'image_size': ['sum', 'mean', 'var'],
'width': ['sum', 'mean', 'var'],
'height': ['sum', 'mean', 'var'],
}
agg_train_imgs = train_df_imgs.groupby('PetID' ).agg(aggs)
 new_columns = [
k + '_' + agg for k in aggs.keys() for agg in aggs[k]
]
agg_train_imgs.columns = new_columns
agg_train_imgs = agg_train_imgs.reset_index()
agg_test_imgs = test_df_imgs.groupby('PetID' ).agg(aggs)
 new_columns = [
k + '_' + agg for k in aggs.keys() for agg in aggs[k]
]
agg_test_imgs.columns = new_columns
agg_test_imgs = agg_test_imgs.reset_index()
train_df = train_df.merge(agg_train_imgs, how='left', on='PetID')
 test_df = test_df.merge(agg_test_imgs, how='left', on='PetID' )<normalization>
def get_score(weights, train_idx, oofs, labels):
blend = np.zeros_like(oofs[0][train_idx, :])
 for oof, weight in zip(oofs[:-1], weights):
blend += weight * oof[train_idx, :]
blend +=(1 - np.sum(weights)) * oofs[-1][train_idx, :]
return logloss_for_multilabel(labels[train_idx, :], blend)
 def get_best_weights(oofs, labels):
weight_list = []
weights = np.array([1/len(oofs)for x in range(len(oofs)- 1)])
 for n_splits in tqdm([5, 6]):
for i in range(2):
kf = KFold(n_splits=n_splits, random_state=i, shuffle=True)
 for fold,(train_idx, valid_idx)in enumerate(kf.split(X=oofs[0])) :
res = minimize(get_score, weights, args=(train_idx, oofs, labels), method="Nelder-Mead", tol=1e-6)
 logger.info(f"i: {i} fold: {fold} res.x: {res.x}")
 weight_list.append(res.x)
 mean_weight = np.mean(weight_list, axis=0)
 print(f"optimized weight: {mean_weight}")
 return mean_weight
best_weights = get_best_weights(stage3_oofs, train_features_df[stage_1_2_target_cols].values )
print('scaling num_cols')
 for col in num_cols:
print('scaling {}'.format(col))
try:
col_mean = train_df[col].mean()
train_df[col].fillna(col_mean, inplace=True)
 test_df[col].fillna(col_mean, inplace=True)
 scaler = StandardScaler()
train_df[col] = scaler.fit_transform(train_df[col].values.reshape(-1, 1))
test_df[col] = scaler.transform(test_df[col].values.reshape(-1, 1))
except:
print("*********" )<compute_test_metric>
best_weights = np.insert(best_weights, len(best_weights), 1 - np.sum(best_weights))
oof_preds = np.stack(stage3_oofs ).transpose(1, 2, 0 ).dot(best_weights)
 print(logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, oof_preds))
blend_preds = np.stack(stage3_preds ).transpose(1, 2, 0 ).dot(best_weights )
def rmse(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
 def get_input_features(df):
nn_nums= [x for x in list(num_cols)
 ]
X = {'description':pad_sequences(df['Description'], maxlen=maxlen)}
X['numerical'] = np.array(df[nn_nums])
 X['bow_inputs'] = np.array(df[list(["svd1_" + str(i)for i in range(n_components)] +
["SVD_metadata_annots_top_desc_" + str(i)for i in range(16)] +
["SVD_sentiment_entities_" + str(i)for i in range(16)] 
)])
 X['img_inputs'] = np.array(df[list(["img_svd1_" + str(i)for i in range(n_components)] + ["img_svd2_" + str(i)for i in range(n_components)] 
)])
 for cat in cat_cols:
X[cat] = np.array(df[cat])
 return X<compute_train_metric>
final_blend_oofs = oof_preds * 0.7 + cnn_trainer.result.oof_preds * 0.3
test_features_df[stage_1_2_target_cols] = blend_preds * 0.7 + cnn_preds * 0.3
def kappa_loss(y_pred, y_true, y_pow=2, eps=1e-10, N=5, bsize=256, name='kappa'):

 with tf.name_scope(name):
y_true = tf.to_float(y_true)
 repeat_op = tf.to_float(tf.tile(tf.reshape(tf.range(0, N), [N, 1]), [1, N]))
repeat_op_sq = tf.square(( repeat_op - tf.transpose(repeat_op)))
 weights = repeat_op_sq / tf.to_float(( N - 1)** 2)
 pred_ = y_pred ** y_pow
try:
pred_norm = pred_ /(eps + tf.reshape(tf.reduce_sum(pred_, 1), [-1, 1]))
except Exception:
pred_norm = pred_ /(eps + tf.reshape(tf.reduce_sum(pred_, 1), [bsize, 1]))
hist_rater_a = tf.reduce_sum(pred_norm, 0)
 hist_rater_b = tf.reduce_sum(y_true, 0)
 conf_mat = tf.matmul(tf.transpose(pred_norm), y_true)
 nom = tf.reduce_sum(weights * conf_mat)
 denom = tf.reduce_sum(weights * tf.matmul(
 tf.reshape(hist_rater_a, [N, 1]), tf.reshape(hist_rater_b, [1, N])) /
tf.to_float(bsize))
return nom /(denom + eps )<choose_model_class>
print(logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, final_blend_oofs))
def make_model(softmax = False):
k.clear_session()
categorical_inputs = []
for cat in cat_cols:
categorical_inputs.append(Input(shape=[1], name=cat))
categorical_embeddings = []
for i, cat in enumerate(cat_cols):
categorical_embeddings.append(
 Embedding(embed_sizes[i], 10, name = cat + "_embed" )(categorical_inputs[i]))
categorical_logits = Concatenate()([Flatten()(cat_emb)for cat_emb in categorical_embeddings])
 categorical_logits = Dropout (.5 )(categorical_logits)
 categorical_logits = Dense(50, activation = 'relu' )(categorical_logits)
 numerical_inputs = Input(shape=[X_train["numerical"].shape[1]], name = 'numerical')
 numerical_logits = Dropout (.2 )(numerical_inputs)
 numerical_logits = Dense(50, activation = 'relu' )(numerical_logits)
 numerical_logits = Dense(50, activation = 'relu' )(numerical_logits)
 img_inputs = Input(shape = [n_components * 2], name = "img_inputs")
 img_logits = Dropout (.2 )(img_inputs)
 bow_inputs = Input(shape = [n_components * 2], name = "bow_inputs")
 bow_logits = Dropout (.2 )(bow_inputs)
 text_inp = Input(shape=[maxlen], name='description')
 text_embed = Embedding(nb_words, embed_size, weights=[embedding_matrix], trainable=False )(text_inp)
 emb_desc = SpatialDropout1D (.6 )(text_embed)
 filter_sizes=[1,2,3,4]
convs = []
for filter_size in filter_sizes:
conv = Conv1D(8, kernel_size=(filter_size),
kernel_initializer="normal", activation="relu" )(emb_desc)
 convs.append(MaxPool1D(pool_size=(maxlen-filter_size+1))(conv))
text_logits = concatenate(convs)
 avg_pool = GlobalAveragePooling1D()(text_logits)
 max_pool = GlobalMaxPooling1D()(text_logits)
 text_logits = Concatenate()([avg_pool, max_pool])
 x = Concatenate()([
bow_logits,
categorical_logits,
text_logits,
numerical_logits,
img_logits
])
 x = Dense(300, activation = 'relu' )(x)
 x = Dropout (.2 )(x)
 x = Dense(200, activation = 'relu' )(x)
 x = Dropout (.2 )(x)
 x = Dense(100, activation = 'relu' )(x)
 if softmax == True:
out = Dense(5, activation = 'softmax' )(x)
 else:
out = Dense(1, activation = 'sigmoid' )(x)
 model = Model(inputs=[text_inp] + categorical_inputs + [numerical_inputs] + [bow_inputs] + [img_inputs],outputs=out)
 if softmax == True:
loss = kappa_loss
else:
loss = root_mean_squared_error
model.compile(optimizer=Adam(lr = 0.0005), loss = loss)
 return model 
<compute_test_metric>
class PostProcessor(object):
def __init__(self, original_oof, original_pred_df):
self.original_oof = original_oof
self.original_pred_df = original_pred_df
if "_dh" in globals().keys() :
if globals() ["_dh"][0] == "/kaggle/working":
self.working_place = "kaggle_kernel"
self.base_dir = '.. /input'
self.csv_dir = ".. /input/lish-moa/"
else:
self.working_place = "local_lab"
self.base_dir = '.. /input'
self.csv_dir = ".. /input/lish-moa/"
else:
self.working_place = "local_py"
self.base_dir = '.. /input'
self.csv_dir = ".. /input/lish-moa/"
self.int2drug_id = {1: '87d714366', 2: '9f80f3f77', 3: '8b87a7a83', 4: '5628cb3ee',
5: 'd08af5d4b', 6: '292ab2c28', 7: 'd50f18348', 8: 'd1b47f29d'}
test, targets_with_drug_ids, target_cols = self.load_csv()
self.test = test
self.targets_with_drug_ids = targets_with_drug_ids
self.target_cols = target_cols
def load_csv(self):
test = pd.read_csv(os.path.join(self.csv_dir + "test_features.csv"))
train = pd.read_csv(os.path.join(self.csv_dir + "train_features.csv"))
train = train[train['cp_type'] == 'trt_cp'].reset_index(drop=True)
 targets = pd.read_csv(os.path.join(self.csv_dir + "train_targets_scored.csv"))
target_cols = [x for x in targets.columns if x not in ['sig_id', 'drug_id']]
drugs = pd.read_csv(os.path.join(self.csv_dir + "train_drug.csv"))
targets_with_drug_ids = train.merge(drugs, on='sig_id', how='left')
 targets_with_drug_ids = targets_with_drug_ids.merge(targets, on='sig_id', how='left')
 return test, targets_with_drug_ids, target_cols
def loss(self, target_arr, oof_arr):
y = torch.tensor(target_arr, dtype=float)
 p = torch.tensor(oof_arr, dtype=float)
 p = torch.clamp(p, 1e-15, 1-(1e-15))
competition_metric = nn.BCELoss()(p, y ).item()
return np.mean(competition_metric)
 def get_feature(self, df):
df["cp_dose"] =(df["cp_dose"] == "D1" ).astype(int)
 df["cp_time"] = df["cp_time"] / 72
features_g = ["g-" + str(i)for i in range(772)]
features_c = ["c-" + str(i)for i in range(100)]
df["g_sum"] = df[features_g].sum(axis=1)
 df["g_mean"] = df[features_g].mean(axis=1)
 df["g_median"] = df[features_g].median(axis=1)
 df["g_std"] = df[features_g].std(axis=1)
 df["g_kurt"] = df[features_g].kurtosis(axis=1)
 df["g_skew"] = df[features_g].skew(axis=1)
 df["c_sum"] = df[features_c].sum(axis=1)
 df["c_mean"] = df[features_c].mean(axis=1)
 df["c_std"] = df[features_c].std(axis=1)
 df["c_median"] = df[features_c].median(axis=1)
 df["c_kurt"] = df[features_c].kurtosis(axis=1)
 df["c_skew"] = df[features_c].skew(axis=1)
 df["gc_sum"] = df[features_g + features_c].sum(axis=1)
 df["gc_mean"] = df[features_g + features_c].mean(axis=1)
 df["gc_std"] = df[features_g + features_c].std(axis=1)
 df["gc_kurt"] = df[features_g + features_c].kurtosis(axis=1)
 df["gc_skew"] = df[features_g + features_c].skew(axis=1)
 df["gc_median"] = df[features_g + features_c].median(axis=1)
 return df
def fit(self):
with open(f"{self.base_dir}/drug-prediction-models/drug_id_oof.pkl", "rb")as f:
drug_id_prediction = pickle.load(f)
 oof_df = pd.DataFrame(self.original_oof, columns=self.target_cols)
 best_score = 1000
best_th = 0
for threth in [0.5, 0.9, 0.95, 0.96, 0.97, 0.98, 0.99]:
drug_id_prediction_hard = self.prediction_to_int(drug_id_prediction, threthold=threth)
 oof_df_update = self.update_prediction(oof_df, drug_id_prediction_hard)
 oof_arr = self.targets_with_drug_ids[self.target_cols].values
pred_arr = oof_df_update[self.target_cols].values
tmp_score = self.loss(oof_arr, pred_arr)
 print(threth, tmp_score)
 if tmp_score < best_score:
best_score = tmp_score
best_th = threth
return best_th, best_score
def prediction_to_int(self, drug_id_prediction, threthold=None):
if threthold is None:
threthold = 0.95
drug_id_prediction[drug_id_prediction < threthold] = 0
drug_id_prediction_hard = np.argmax(drug_id_prediction, axis=1)
 return drug_id_prediction_hard
def update_prediction(self, pred_df, drug_id_prediction_hard):
_pred_df = pred_df.copy()
for pred_int, drug_id in self.int2drug_id.items() :
idx =(drug_id_prediction_hard == pred_int)
 targets_dict = self.targets_with_drug_ids[self.targets_with_drug_ids['drug_id'] == drug_id][self.target_cols].head(1 ).reset_index(drop=True ).T.to_dict() [0]
for k, v in targets_dict.items() :
_pred_df.loc[idx, k] = v
return _pred_df
def transform(self, threthold=None):
with open(f"{self.base_dir}/drug-prediction-models/drug_id_models.pkl", "rb")as f:
models = pickle.load(f)
 test = self.get_feature(self.test)
 predictors = [x for x in test.columns if x not in ["sig_id", "cp_type", "drug_id"]]
drug_id_prediction = []
for model in models:
drug_id_prediction.append(model.predict(test[predictors]))
drug_id_prediction = np.mean(drug_id_prediction, axis=0)
 drug_id_prediction_hard = self.prediction_to_int(drug_id_prediction, threthold=None)
 pred_df = self.original_pred_df.copy()
pred_df = self.update_prediction(pred_df, drug_id_prediction_hard)
 return pred_df
def confusion_matrix(rater_a, rater_b, min_rating=None, max_rating=None):

 assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(rater_a + rater_b)
 if max_rating is None:
max_rating = max(rater_a + rater_b)
 num_ratings = int(max_rating - min_rating + 1)
 conf_mat = [[0 for i in range(num_ratings)]
for j in range(num_ratings)]
for a, b in zip(rater_a, rater_b):
conf_mat[a - min_rating][b - min_rating] += 1
return conf_mat
def histogram(ratings, min_rating=None, max_rating=None):

 if min_rating is None:
min_rating = min(ratings)
 if max_rating is None:
max_rating = max(ratings)
 num_ratings = int(max_rating - min_rating + 1)
 hist_ratings = [0 for x in range(num_ratings)]
for r in ratings:
hist_ratings[r - min_rating] += 1
return hist_ratings
def quadratic_weighted_kappa(y, y_pred):

 rater_a = y
rater_b = y_pred
min_rating=None
max_rating=None
rater_a = np.array(rater_a, dtype=int)
 rater_b = np.array(rater_b, dtype=int)
 assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(min(rater_a), min(rater_b))
if max_rating is None:
max_rating = max(max(rater_a), max(rater_b))
conf_mat = confusion_matrix(rater_a, rater_b,
min_rating, max_rating)
 num_ratings = len(conf_mat)
 num_scored_items = float(len(rater_a))
hist_rater_a = histogram(rater_a, min_rating, max_rating)
 hist_rater_b = histogram(rater_b, min_rating, max_rating)
 numerator = 0.0
denominator = 0.0
for i in range(num_ratings):
for j in range(num_ratings):
expected_count =(hist_rater_a[i] * hist_rater_b[j]
/ num_scored_items)
 d = pow(i - j, 2.0)/ pow(num_ratings - 1, 2.0)
 numerator += d * conf_mat[i][j] / num_scored_items
denominator += d * expected_count / num_scored_items
return(1.0 - numerator / denominator )<train_on_grid>
pp = PostProcessor(original_oof=final_blend_oofs,
original_pred_df=test_features_df[stage_1_2_target_cols])
 best_th, best_score = pp.fit()
print('best is', best_th, best_score)
 pp_prediction = pp.transform(best_th)
 test_features_df[stage_1_2_target_cols] = pp_prediction.values
<load_from_csv><EOS> 
test_features_df.loc[test_features_df['cp_type'] != 'trt_cp', stage_1_2_target_cols] = 0
sub = sample_submission_df.drop(columns=stage_1_2_target_cols ).merge(test_features_df[['sig_id']+stage_1_2_target_cols], on='sig_id', how='left' ).fillna(0)
 sub.to_csv('submission.csv', index=False)
 sub.head()