Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<load_from_csv>
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import log_loss
import tensorflow.keras.backend as K
import tensorflow.keras.layers as L
import tensorflow.keras.models as M
import tensorflow_addons as tfa
from tqdm.notebook import tqdm
import tensorflow as tf
import pandas as pd
import numpy as np
import random
import time
test = pd.read_csv(input_path / 'test.csv', index_col='id' )<load_from_csv>
test_features = pd.read_csv('/kaggle/input/lish-moa/test_features.csv')
 train_targets = pd.read_csv('/kaggle/input/lish-moa/train_targets_scored.csv')
 train_features = pd.read_csv("/kaggle/input/lish-moa/train_features.csv")
 submission = pd.read_csv('/kaggle/input/lish-moa/sample_submission.csv')
 del test_features['sig_id'], train_features["sig_id"], train_targets['sig_id']
submission = pd.read_csv(input_path / 'sample_submission.csv', index_col='id' )<split>
df1 = pd.read_csv('.. /input/moa-blending/0.01832.csv')
 df2 = pd.read_csv('.. /input/mypredpublic/my_predictions_0000001.csv')
 df3 = pd.read_csv('.. /input/moa-blending/0.01833.csv' )
target = train.pop('target')
 X_train, X_test, y_train, y_test = train_test_split(train, target, train_size=0.60 )<import_modules>
for feature in ['cp_type', 'cp_dose']:
trans = LabelEncoder()
trans.fit(list(train_features[feature].astype(str ).values)+ list(test_features[feature].astype(str ).values))
train_features[feature] = trans.transform(list(train_features[feature].astype(str ).values))
test_features[feature] = trans.transform(list(test_features[feature].astype(str ).values))
train = train_features.copy()
from xgboost import XGBRegressor
import xgboost as xgb<choose_model_class>
def create_model(num_columns):
model = tf.keras.Sequential([
tf.keras.layers.Input(num_columns),
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(1000, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(700, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(400, activation="relu")) ,
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dropout(0.1),
tfa.layers.WeightNormalization(tf.keras.layers.Dense(206, activation="sigmoid"))
])
 model.compile(optimizer=tfa.optimizers.Lookahead(tf.optimizers.Adam() , sync_period=5),
loss='binary_crossentropy', metrics=["accuracy"] 
)
 return model
regressor = xgb.XGBRegressor(colsample_bytree=0.5,
alpha=0.01,
reg_lambda=0.003,
learning_rate=0.01,
max_depth=15,
min_child_weight=257,
n_estimators=1000,
subsample=0.7,
random_state=2020,
metric_period=100,
silent=1)
 regressor.fit(X_train, y_train, early_stopping_rounds=6, eval_set=[(X_test, y_test)], verbose=1)
 y_pred = regressor.predict(X_test)
 plot_results("XGBRegressor", y_test, y_pred)
<import_modules>
N_STARTS = 3
tf.random.set_seed(43)
 res = train_targets.copy()
submission.loc[:, train_targets.columns] = 0
res.loc[:, train_targets.columns] = 0
model = create_model(len(train.columns))
model.fit(train.values,train_targets.values,validation_data=(train.values, train_targets.values),epochs=1, batch_size=128)
 test_predict = model.predict(test_features.values)
 val_predict = model.predict(train.values)
 submission.loc[:, train_targets.columns] += test_predict
res.loc[:, train_targets.columns] += val_predict
print('')
 submission.loc[:, train_targets.columns] /= N_STARTS
res.loc[:, train_targets.columns] /= N_STARTS
import lightgbm as lgb<create_dataframe>
metrics = []
for _target in train_targets.columns:
metrics.append(log_loss(train_targets.loc[:, _target], res.loc[:, _target]))
print(f'OOF Metric: {np.mean(metrics)}' )
lgb_train = lgb.Dataset(X_train, y_train)
 lgb_valid = lgb.Dataset(X_test, y_test)
 param = {
'seed': 2021,
'objective': 'regression',
'metric': 'rmse',
'verbosity': -1,
'feature_pre_filter': False,
'lambda_l1': 6.540486456085813,
'lambda_l2': 0.01548480538099245,
'num_leaves': 256,
'feature_fraction': 0.52,
'bagging_fraction': 0.6161835249194311,
'bagging_freq': 7,
'min_child_samples': 20,
'learning_rate' : 0.001,
'early_stopping_round' : 1000,
'num_iterations' : 20000
}
lgb_model = lgb.train(param,
lgb_train,
valid_sets=lgb_valid,
num_boost_round=5000,
early_stopping_rounds=100)
 plot_results('LightGBM', y_test, y_pred )<import_modules>
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)
 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 = submission.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 )
import torch
from torch import nn<feature_engineering>
def order_sub(sub):
return sub.sort_values('sig_id' ).reset_index(drop=True)
 df1 = order_sub(df1)
 df2 = order_sub(df2)
 df3 = order_sub(df3)
 BLEND=df1.copy()
BLEND.iloc[:,1:] = df1.iloc[:,1:]*0.8 +df2.iloc[:,1:]*0.1 +df3.iloc[:,1:]*0.1
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 )
<choose_model_class><EOS> 
sub = pd.read_csv('.. /input/moa-submission/submission.csv')
 sub.to_csv('submission.csv', index=False )