Kaggle competition: Mechanisms of Action (MoA) Prediction
<SOS> metric: MeanColumnwiseLogLoss Kaggle data source: mechanisms-of-action-(moa)-prediction<save_to_csv>
import numpy as np
import pandas as pd
all_pred_test = all_pred_test.astype('int')
<merge>
df1 = pd.read_csv('.. /input/fork-of-2heads-looper-super-puper/submission.csv')
 df2 = pd.read_csv('.. /input/moa-pytorch-feature-engineering-0-01846/submission.csv')
 df3 = pd.read_csv('.. /input/explore-the-magic-of-mean-top-2-of-the-publiclb/submission.csv')
 df4 = pd.read_csv('.. /input/moasub/submission.csv')
 df5 = pd.read_csv('.. /input/moarankgauss/submission.csv')
 df5.head()
answer = pd.merge(df_test,all_pred_test, left_on = 'ForecastId',right_on = 'ForecastId');
train_max_date = train['Date2'].max()
test_min_date = test['Date2'].min()
for idx, region in enumerate(regions):
sel1 =(( train['Date2'] >= test_min_date)&(train['Region'] == region)) ;
to_paste = train.loc[sel1,['ConfirmedCases','Fatalities']].copy() ;
sel2 =(( answer['Date2'] <= train_max_date)&(answer['Region'] == region))
answer.loc[sel2,['ConfirmedCases','Fatalities']] = to_paste.loc[:,['ConfirmedCases','Fatalities']].values;<save_to_csv>
def order_sub(sub):
return sub.sort_values('sig_id' ).reset_index(drop=True )
answer = answer.loc[:,['ForecastId','ConfirmedCases','Fatalities']]
answer = answer.astype('int');
answer.to_csv("submission.csv", index = False);
answer.dtypes<feature_engineering>
df1 = order_sub(df1)
 df2 = order_sub(df2)
 df3 = order_sub(df3)
 df4 = order_sub(df4)
 df5 = order_sub(df5 )
<set_options>
BLEND=df1.copy()
BLEND.iloc[:,1:] = df1.iloc[:,1:]*0.5 +df2.iloc[:,1:]*0.3 +df4.iloc[:,1:]*0.15 +df5.iloc[:,1:]*0.15
warnings.filterwarnings("ignore")
 %matplotlib inline
%config InlineBackend.figure_format = 'retina'
train = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-4/train.csv")
 test = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-4/test.csv")
 region_metadata = pd.read_csv("/kaggle/input/covid19-forecasting-metadata/region_metadata.csv")
 region_date_metadata = pd.read_csv("/kaggle/input/covid19-forecasting-metadata/region_date_metadata.csv")
 train = train.merge(test[["ForecastId", "Province_State", "Country_Region", "Date"]], on = ["Province_State", "Country_Region", "Date"], how = "left")
 display(train.head())
 test = test[~test.Date.isin(train.Date.unique())]
display(test.head())
 df = pd.concat([train, test], sort = False)
 df.head()<categorify>
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["geo"] = df.Country_Region.astype(str)+ ": " + df.Province_State.astype(str)
 df.loc[df.Province_State.isna() , "geo"] = df[df.Province_State.isna() ].Country_Region
df.ConfirmedCases = df.groupby("geo")["ConfirmedCases"].cummax()
df.Fatalities = df.groupby("geo")["Fatalities"].cummax()
df = df.merge(region_metadata, on = ["Country_Region", "Province_State"])
 df = df.merge(region_date_metadata, on = ["Country_Region", "Province_State", "Date"], how = "left")
 df.continent = LabelEncoder().fit_transform(df.continent)
 df.Date = pd.to_datetime(df.Date, format = "%Y-%m-%d")
 df.sort_values(["geo", "Date"], inplace = True)
 df.head()<categorify>
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 )