Kaggle competition: Aerial Cactus Identification
<SOS> metric: AUC Kaggle data source: aerial-cactus-identification<categorify>
%matplotlib inline
%reload_ext autoreload
%autoreload 2
final = pd.get_dummies(final, columns = ["Title"] )<feature_engineering>
np.random.seed(42 )
final["Family"] = final["SibSp"] + final["Parch"] + 1<categorify>
data_folder = Path(".. /input")
 train_df = pd.read_csv(".. /input/train.csv")
 test_df = pd.read_csv(".. /input/sample_submission.csv" )
final['Single'] = final['Family'].map(lambda s: 1 if s == 1 else 0)
 final['SmallF'] = final['Family'].map(lambda s: 1 if s == 2 else 0)
 final['MedF'] = final['Family'].map(lambda s: 1 if 3 <= s <= 4 else 0)
 final['LargeF'] = final['Family'].map(lambda s: 1 if s >= 5 else 0 )<data_type_conversions>
learn50 = cnn_learner(train_img, models.densenet161, metrics=[error_rate, accuracy], model_dir="/tmp/model/" )
final['Embarked'].fillna("S",inplace = True )<categorify>
lr = 3e-02
learn50.fit_one_cycle(5 , slice(lr))
final = pd.get_dummies(final, columns = ["Embarked"], prefix="Embarked_from_" )<count_missing_values>
preds,_ = learn50.get_preds(ds_type=DatasetType.Test )
<count_values><EOS> 
test_df.to_csv('submission.csv', index=False )