Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<feature_engineering>
KAGGLE_PATH = '.. '
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = {}
for sentence in sentences:
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab<feature_engineering>
DataLoader,
Dataset,
random_split, 
)
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 for dirname, _, filenames in os.walk(f'{KAGGLE_PATH}/input/'):
for filename in filenames:
print(os.path.join(dirname, filename))
def add_features(df):
print('Adding some features to data')
 df["question_text"] = df["question_text"].apply(lambda x: str(x))
print('To Sting')
 df["num_words"] = df["question_text"].apply(lambda x: len(x.split()))
print('Number of words')
 df["num_unique_words"] = df["question_text"].apply(lambda x: len(set(x.split())))
 print('Number of unique words')
 df["num_chars"] = df["question_text"].apply(lambda x: len(x))
print('Number of chars')
 df["num_stopwords"] = df["question_text"].apply(lambda x: len([w for w in x.lower().split() if w in set(stopwords.words('english')) ]))
print('Number of Stopwords')
 df["num_punctuations"] =df['question_text'].apply(lambda x: len([c for c in x if c in string.punctuation]))
print('Number of punctuations')
 df["num_words_upper"] = df["question_text"].apply(lambda x: len([w for w in x.split() if w.isupper() ]))
print('Number of upper words')
 df["num_words_title"] = df["question_text"].apply(lambda x: len([w for w in x.split() if w.istitle() ]))
print('Number of title')
 df["num_num"] = df["question_text"].apply(lambda x: len([n for n in x.split() if n.isnumeric() ]))
print('Number of numbers')
 df["mean_word_len"] = df["question_text"].apply(lambda x: np.mean([len(w)for w in x.split() ]))
print('mean len word')
 return df<categorify>
my_pred_simple = pd.read_csv(f'{KAGGLE_PATH}/input/simple-model-lish-moa/submission.csv')
 my_pred_hard = pd.read_csv(f'{KAGGLE_PATH}/input/moa-prediction-torch/submission.csv')
 tabnet_pred = pd.read_csv(f'{KAGGLE_PATH}/input/moa-tabnet-with-pca-rank-gauss/submission.csv')
 xgb_pred = pd.read_csv(f'{KAGGLE_PATH}/input/xgboost-catboost-ensemble-baseline-solution/submission.csv' )
def load_embed(file):
def get_coefs(word,*arr):
return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.split(" ")) for o in open(file, encoding='latin'))
return embeddings_index<string_transform>
big_diffs =(abs(xgb_pred.iloc[:, 1:] - tabnet_pred.iloc[:, 1:])> 0.01)
 xgb_pred = xgb_pred.mask(big_diffs, xgb_pred[big_diffs] * 0.3 + tabnet_pred[big_diffs] * 0.7 )
def build_vocab(texts):
sentences = texts.apply(lambda x: x.split() ).values
vocab = set()
for sentence in sentences:
for word in sentence:
vocab.add(word)
 return vocab<feature_engineering>
TARGET_COL = ['5-alpha_reductase_inhibitor', '11-beta-hsd1_inhibitor', 'acat_inhibitor', 'acetylcholine_receptor_agonist', 'acetylcholine_receptor_antagonist', 'acetylcholinesterase_inhibitor', 'adenosine_receptor_agonist', 'adenosine_receptor_antagonist', 'adenylyl_cyclase_activator', 'adrenergic_receptor_agonist', 'adrenergic_receptor_antagonist', 'akt_inhibitor', 'aldehyde_dehydrogenase_inhibitor', 'alk_inhibitor', 'ampk_activator', 'analgesic', 'androgen_receptor_agonist', 'androgen_receptor_antagonist', 'anesthetic_-_local', 'angiogenesis_inhibitor', 'angiotensin_receptor_antagonist', 'anti-inflammatory', 'antiarrhythmic', 'antibiotic', 'anticonvulsant', 'antifungal', 'antihistamine', 'antimalarial', 'antioxidant', 'antiprotozoal', 'antiviral', 'apoptosis_stimulant', 'aromatase_inhibitor', 'atm_kinase_inhibitor', 'atp-sensitive_potassium_channel_antagonist', 'atp_synthase_inhibitor', 'atpase_inhibitor', 'atr_kinase_inhibitor', 'aurora_kinase_inhibitor', 'autotaxin_inhibitor', 'bacterial_30s_ribosomal_subunit_inhibitor', 'bacterial_50s_ribosomal_subunit_inhibitor', 'bacterial_antifolate', 'bacterial_cell_wall_synthesis_inhibitor', 'bacterial_dna_gyrase_inhibitor', 'bacterial_dna_inhibitor', 'bacterial_membrane_integrity_inhibitor', 'bcl_inhibitor', 'bcr-abl_inhibitor', 'benzodiazepine_receptor_agonist', 'beta_amyloid_inhibitor', 'bromodomain_inhibitor', 'btk_inhibitor', 'calcineurin_inhibitor', 'calcium_channel_blocker', 'cannabinoid_receptor_agonist', 'cannabinoid_receptor_antagonist', 'carbonic_anhydrase_inhibitor', 'casein_kinase_inhibitor', 'caspase_activator', 'catechol_o_methyltransferase_inhibitor', 'cc_chemokine_receptor_antagonist', 'cck_receptor_antagonist', 'cdk_inhibitor', 'chelating_agent', 'chk_inhibitor', 'chloride_channel_blocker', 'cholesterol_inhibitor', 'cholinergic_receptor_antagonist', 'coagulation_factor_inhibitor', 'corticosteroid_agonist', 'cyclooxygenase_inhibitor', 'cytochrome_p450_inhibitor', 'dihydrofolate_reductase_inhibitor', 'dipeptidyl_peptidase_inhibitor', 'diuretic', 'dna_alkylating_agent', 'dna_inhibitor', 'dopamine_receptor_agonist', 'dopamine_receptor_antagonist', 'egfr_inhibitor', 'elastase_inhibitor', 'erbb2_inhibitor', 'estrogen_receptor_agonist', 'estrogen_receptor_antagonist', 'faah_inhibitor', 'farnesyltransferase_inhibitor', 'fatty_acid_receptor_agonist', 'fgfr_inhibitor', 'flt3_inhibitor', 'focal_adhesion_kinase_inhibitor', 'free_radical_scavenger', 'fungal_squalene_epoxidase_inhibitor', 'gaba_receptor_agonist', 'gaba_receptor_antagonist', 'gamma_secretase_inhibitor', 'glucocorticoid_receptor_agonist', 'glutamate_inhibitor', 'glutamate_receptor_agonist', 'glutamate_receptor_antagonist', 'gonadotropin_receptor_agonist', 'gsk_inhibitor', 'hcv_inhibitor', 'hdac_inhibitor', 'histamine_receptor_agonist', 'histamine_receptor_antagonist', 'histone_lysine_demethylase_inhibitor', 'histone_lysine_methyltransferase_inhibitor', 'hiv_inhibitor', 'hmgcr_inhibitor', 'hsp_inhibitor', 'igf-1_inhibitor', 'ikk_inhibitor', 'imidazoline_receptor_agonist', 'immunosuppressant', 'insulin_secretagogue', 'insulin_sensitizer', 'integrin_inhibitor', 'jak_inhibitor', 'kit_inhibitor', 'laxative', 'leukotriene_inhibitor', 'leukotriene_receptor_antagonist', 'lipase_inhibitor', 'lipoxygenase_inhibitor', 'lxr_agonist', 'mdm_inhibitor', 'mek_inhibitor', 'membrane_integrity_inhibitor', 'mineralocorticoid_receptor_antagonist', 'monoacylglycerol_lipase_inhibitor', 'monoamine_oxidase_inhibitor', 'monopolar_spindle_1_kinase_inhibitor', 'mtor_inhibitor', 'mucolytic_agent', 'neuropeptide_receptor_antagonist', 'nfkb_inhibitor', 'nicotinic_receptor_agonist', 'nitric_oxide_donor', 'nitric_oxide_production_inhibitor', 'nitric_oxide_synthase_inhibitor', 'norepinephrine_reuptake_inhibitor', 'nrf2_activator', 'opioid_receptor_agonist', 'opioid_receptor_antagonist', 'orexin_receptor_antagonist', 'p38_mapk_inhibitor', 'p-glycoprotein_inhibitor', 'parp_inhibitor', 'pdgfr_inhibitor', 'pdk_inhibitor', 'phosphodiesterase_inhibitor', 'phospholipase_inhibitor', 'pi3k_inhibitor', 'pkc_inhibitor', 'potassium_channel_activator', 'potassium_channel_antagonist', 'ppar_receptor_agonist', 'ppar_receptor_antagonist', 'progesterone_receptor_agonist', 'progesterone_receptor_antagonist', 'prostaglandin_inhibitor', 'prostanoid_receptor_antagonist', 'proteasome_inhibitor', 'protein_kinase_inhibitor', 'protein_phosphatase_inhibitor', 'protein_synthesis_inhibitor', 'protein_tyrosine_kinase_inhibitor', 'radiopaque_medium', 'raf_inhibitor', 'ras_gtpase_inhibitor', 'retinoid_receptor_agonist', 'retinoid_receptor_antagonist', 'rho_associated_kinase_inhibitor', 'ribonucleoside_reductase_inhibitor', 'rna_polymerase_inhibitor', 'serotonin_receptor_agonist', 'serotonin_receptor_antagonist', 'serotonin_reuptake_inhibitor', 'sigma_receptor_agonist', 'sigma_receptor_antagonist', 'smoothened_receptor_antagonist', 'sodium_channel_inhibitor', 'sphingosine_receptor_agonist', 'src_inhibitor', 'steroid', 'syk_inhibitor', 'tachykinin_antagonist', 'tgf-beta_receptor_inhibitor', 'thrombin_inhibitor', 'thymidylate_synthase_inhibitor', 'tlr_agonist', 'tlr_antagonist', 'tnf_inhibitor', 'topoisomerase_inhibitor', 'transient_receptor_potential_channel_antagonist', 'tropomyosin_receptor_kinase_inhibitor', 'trpv_agonist', 'trpv_antagonist', 'tubulin_inhibitor', 'tyrosine_kinase_inhibitor', 'ubiquitin_specific_protease_inhibitor', 'vegfr_inhibitor', 'vitamin_b', 'vitamin_d_receptor_agonist', 'wnt_inhibitor']
NUM_TARGET = len(TARGET_COL )
def add_lower(embedding, df_column):
vocab = build_vocab(df_column)
 count = 0
for word in vocab:
if word in embedding and word.lower() not in embedding:
embedding[word.lower() ] = embedding[word]
count += 1
print(f"Added {count} words to embedding" )<feature_engineering>
BLEND = pd.concat([my_pred_simple.sig_id,
my_pred_hard.iloc[:, 1:] * 0.1 +
my_pred_simple.iloc[:, 1:] * 0.15 +
tabnet_pred.iloc[:, 1:] * 0.6 +
xgb_pred.iloc[:, 1:] * 0.15], axis=1)
<define_variables><EOS> 
submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
 df = pd.read_csv("/kaggle/input/lish-moa/sample_submission.csv")
 public_id = list(df['sig_id'].values)
 df_test = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
 test_id = list(df_test['sig_id'].values)
 private_id = list(set(test_id)-set(public_id))
df_submit = pd.DataFrame(index = public_id+private_id, columns=TARGET_COL)
 df_submit.index.name = 'sig_id'
df_submit[:] = 0
df_predict = BLEND.copy()
df_submit.loc[df_predict.sig_id,:] = df_predict[TARGET_COL].values
df_submit.loc[df_test[df_test.cp_type=='ctl_vehicle'].sig_id]= 0
df_submit.to_csv('submission.csv',index=True)