def score(xgb_params):
global model, oof_train, oof_test, feature_score, test_predictions, qwk, qwk_change
model, oof_train, oof_test, feature_score = run_xgb_tuning(xgb_params)
optR = OptimizedRounder()
optR.fit(oof_train, X_train['AdoptionSpeed'].values)
coefficients = optR.coefficients()
valid_pred = optR.predict(oof_train, coefficients)
qwk = quadratic_weighted_kappa(X_train['AdoptionSpeed'].values,
valid_pred)
print("QWK = ", qwk)
coefficients_ = coefficients.copy()
coefficients_[0] = 1.66
coefficients_[1] = 2.13
coefficients_[3] = 2.85
train_predictions = optR.predict(oof_train,
coefficients_).astype(np.int8)
test_predictions = optR.predict(oof_test.mean(axis=1),
coefficients_).astype(np.int8)
valid_pred = optR.predict(oof_train, coefficients_)
qwk_change = quadratic_weighted_kappa(X_train['AdoptionSpeed'].values,
valid_pred)
print("QWK_change = ", qwk_change)
score_result = {'loss': -qwk_change, 'status': STATUS_OK}
return score_result
def main():
train = read_train_data(path=os.path.join(input.__path__[0], 'petfinder-adoption-prediction/train/'))
test = read_test_data(path=os.path.join(input.__path__[0], 'petfinder-adoption-prediction/test/'))
if adoption_shuffle:
train['AdoptionSpeed'] = random.sample(train['AdoptionSpeed'].values.tolist(), len(train))
if densenet_predict:
dnet_model = densenet_model(weight_path=os.path.join(input.__path__[0], 'densenet-keras/DenseNet-BC-121-32-no-top.h5'))
train_feats = predict_using_img(dnet_model,
train,
img_path=os.path.join(input.__path__[0], 'petfinder-adoption-prediction/train_images/'))
test_feats = predict_using_img(dnet_model,
test,
img_path=os.path.join(input.__path__[0], 'petfinder-adoption-prediction/test_images/'))
train_feats.to_pickle('densenet_train_predict.pkl')
test_feats.to_pickle('densenet_test_predict.pkl')
else:
with open('./densenet_train_predict.pkl', 'rb') as f:
train_feats = pickle.load(f)
with open('./densenet_test_predict.pkl', 'rb') as f:
test_feats = pickle.load(f)
all_ids = pd.concat([train, test], axis=0, ignore_index=True, sort=False)[['PetID']]
svd_col = adopt_svd(train_feats, test_feats)
img_features = pd.concat([all_ids, svd_col], axis=1)
labels_breed = pd.read_csv(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/breed_labels.csv'))
labels_color = pd.read_csv(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/color_labels.csv'))
labels_state = pd.read_csv(os.path.join(input.__path__[0], 'my_state_labels/my_state_labels.csv'))
train_image_files = sorted(glob.glob(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/train_images/*.jpg')))
train_metadata_files = sorted(glob.glob(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/train_metadata/*.json')))
train_sentiment_files = sorted(glob.glob(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/train_sentiment/*.json')))
test_image_files = sorted(glob.glob(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/test_images/*.jpg')))
test_metadata_files = sorted(glob.glob(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/test_metadata/*.json')))
test_sentiment_files = sorted(glob.glob(os.path.join(input.__path__[0], 'petfinder-adoption-prediction/test_sentiment/*.json')))
# Metadata:
train_df_metadata = pd.DataFrame(train_metadata_files)
train_df_metadata.columns = ['metadata_filename']
train_df_sentiment = pd.DataFrame(train_sentiment_files)
train_df_sentiment.columns = ['sentiment_filename']
# Metadata:
test_df_metadata = pd.DataFrame(test_metadata_files)
test_df_metadata.columns = ['metadata_filename']
test_df_sentiment = pd.DataFrame(test_sentiment_files)
test_df_sentiment.columns = ['sentiment_filename']
train_pet_ids = train.PetID.unique()
test_pet_ids = test.PetID.unique()
if exe_extract_additional_feature:
dfs_train = Parallel(n_jobs=12, verbose=1)(
delayed(extract_additional_features)(i, mode='train') for i in train_pet_ids)
dfs_test = Parallel(n_jobs=12, verbose=1)(
delayed(extract_additional_features)(i, mode='test') for i in test_pet_ids)
train_dfs_sentiment = [x[0] for x in dfs_train if isinstance(x[0], pd.DataFrame)]
train_dfs_metadata = [x[1] for x in dfs_train if isinstance(x[1], pd.DataFrame)]
train_dfs_sentiment = pd.concat(train_dfs_sentiment, ignore_index=True, sort=False)
train_dfs_metadata = pd.concat(train_dfs_metadata, ignore_index=True, sort=False)
test_dfs_sentiment = [x[0] for x in dfs_test if isinstance(x[0], pd.DataFrame)]
test_dfs_metadata = [x[1] for x in dfs_test if isinstance(x[1], pd.DataFrame)]
test_dfs_sentiment = pd.concat(test_dfs_sentiment, ignore_index=True, sort=False)
test_dfs_metadata = pd.concat(test_dfs_metadata, ignore_index=True, sort=False)
train_dfs_metadata.to_pickle('train_dfs_metadata.pkl')
train_dfs_sentiment.to_pickle('train_dfs_sentiment.pkl')
test_dfs_metadata.to_pickle('test_dfs_metadata.pkl')
test_dfs_sentiment.to_pickle('test_dfs_sentiment.pkl')
else:
with open('./train_dfs_metadata.pkl', 'rb') as f:
train_dfs_metadata = pickle.load(f)
with open('./train_dfs_sentiment.pkl', 'rb') as f:
train_dfs_sentiment = pickle.load(f)
with open('./test_dfs_metadata.pkl', 'rb') as f:
test_dfs_metadata = pickle.load(f)
with open('./test_dfs_sentiment.pkl', 'rb') as f:
test_dfs_sentiment = pickle.load(f)
# ### group extracted features by PetID:
train_proc = agg_feature(train, train_dfs_metadata, train_dfs_sentiment)
test_proc = agg_feature(test, test_dfs_metadata, test_dfs_sentiment)
train_proc = merge_labels_breed(train_proc, labels_breed)
test_proc = merge_labels_breed(test_proc, labels_breed)
train_proc, test_proc = merge_labels_state(train_proc, test_proc, labels_state)
train_proc = fill_and_drop_feature(train_proc)
test_proc = fill_and_drop_feature(test_proc)
train_proc = add_feature(train_proc)
test_proc = add_feature(test_proc)
X = pd.concat([train_proc, test_proc], ignore_index=True, sort=False)
X_temp = X.copy()
text_columns = ['Description', 'metadata_annots_top_desc', 'sentiment_entities']
categorical_columns = ['main_breed_BreedName', 'second_breed_BreedName']
to_drop_columns = ['PetID', 'Name', 'RescuerID']
rescuer_count = X.groupby(['RescuerID'])['PetID'].count().reset_index()
rescuer_count.columns = ['RescuerID', 'RescuerID_COUNT']
X_temp = X_temp.merge(rescuer_count, how='left', on='RescuerID')
for i in categorical_columns:
try:
X_temp.loc[:, i] = pd.factorize(X_temp.loc[:, i])[0]
except:
pass
X_text = X_temp[text_columns]
for i in X_text.columns:
X_text.loc[:, i] = X_text.loc[:, i].fillna('none')
X_temp['Length_Description'] = X_text['Description'].map(len)
X_temp['Length_metadata_annots_top_desc'] = X_text['metadata_annots_top_desc'].map(len)
X_temp['Lengths_sentiment_entities'] = X_text['sentiment_entities'].map(len)
X_temp = parse_tfidf(X_temp, X_text)
X_temp = X_temp.merge(img_features, how='left', on='PetID')
agg_train_imgs = agg_img_feature(train_image_files)
agg_test_imgs = agg_img_feature(test_image_files)
agg_imgs = pd.concat([agg_train_imgs, agg_test_imgs], axis=0).reset_index(drop=True)
X_temp = X_temp.merge(agg_imgs, how='left', on='PetID')
# ### Drop ID, name and rescuerID
X_temp = X_temp.drop(to_drop_columns, axis=1)
X_train = X_temp.loc[np.isfinite(X_temp.AdoptionSpeed), :]
X_test = X_temp.loc[~np.isfinite(X_temp.AdoptionSpeed), :]
X_test = X_test.drop(['AdoptionSpeed'], axis=1)
assert X_train.shape[0] == train.shape[0]
assert X_test.shape[0] == test.shape[0]
train_cols = X_train.columns.tolist()
train_cols.remove('AdoptionSpeed')
test_cols = X_test.columns.tolist()
assert np.all(train_cols == test_cols)
X_train_non_null = X_train.fillna(-1)
X_test_non_null = X_test.fillna(-1)
X_train_non_null.isnull().any().any(), X_test_non_null.isnull().any().any()
xgb_params = {
'eval_metric': 'rmse',
'object':'reg:squarederror',
'seed': 1337,
'eta': 0.0123,
'subsample': 0.8,
'colsample_bytree': 0.85,
'tree_method': 'gpu_hist',
'device': 'gpu',
'silent': 1,
}
X_train_non_null = fill_and_drop_feature_end(X_train_non_null)
X_test_non_null = fill_and_drop_feature_end(X_test_non_null)
X_train_non_null.to_csv('./X_train.csv')
model, oof_train, oof_test, feature_score = run_xgb(xgb_params, X_train_non_null, X_test_non_null)
optR = OptimizedRounder()
optR.fit(oof_train, X_train['AdoptionSpeed'].values)
coefficients = optR.coefficients()
valid_pred = optR.predict(oof_train, coefficients)
qwk = quadratic_weighted_kappa(X_train['AdoptionSpeed'].values, valid_pred)
print("QWK = ", qwk)
coefficients_ = coefficients.copy()
coefficients_[0] = 1.66
coefficients_[1] = 2.13
coefficients_[3] = 2.85
train_predictions = optR.predict(oof_train, coefficients_).astype(np.int8)
test_predictions = optR.predict(oof_test.mean(axis=1), coefficients_).astype(np.int8)
valid_pred = optR.predict(oof_train, coefficients_)
qwk_change = quadratic_weighted_kappa(X_train['AdoptionSpeed'].values, valid_pred)
print("QWK_change = ", qwk_change)
submission = pd.DataFrame({'PetID': test['PetID'].values, 'AdoptionSpeed': test_predictions})
submission.to_csv('submission.csv', index=False)
str_metric_score = 'qwk' + '_0' + str(int(qwk * 100000))
storage_process(submission, str_metric_score, qwk, qwk_change, feature_score)
