# A Python implementation of the awesome script by Bohdan Pavlyshenko: http://tinyurl.com/jd6k2kr # and with inspiration from Rodolfo Lomascolo's http://tinyurl.com/z6qmxfk # # Author: willgraf import numpy as np import pandas as pd import xgboost as xgb import pdb # from xgboost.sklearn import XGBClassifier from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from subprocess import check_output print(check_output(["ls", "../input"]).decode("utf8")) ## --------------------------Constants----------------------------------------- # LAG_WEEK_VAL = 3 # set to 3 to use lagged features. BIG_LAG = True # set to True if to use more than 1 lagged_featc ## --------------------------Functions----------------------------------------- def get_dataframe(): ''' reads in a fixed column set, cleans the data, and returns a joined singular data set with the values to predict as null. ''' print('Loading training data') train = pd.read_csv('../input/train.csv', usecols=['Semana','Agencia_ID','Ruta_SAK','Cliente_ID','Producto_ID','Demanda_uni_equil']) print('Loading test data') test = pd.read_csv('../input/test.csv', usecols=['Semana','Agencia_ID','Ruta_SAK','Cliente_ID','Producto_ID','id']) print('Merging train & test data') # lagged week features train = train.loc[train['Semana'] > LAG_WEEK_VAL,] train['id'] = 0 test['Demanda_uni_equil'] = None train['target'] = train['Demanda_uni_equil'] test['target'] = 0 return pd.concat([train, test]) def create_lagged_feats(data, num_lag): ''' creates a lagged demand feature based on the keys passed in and a number of weeks lagged. feature name: target_lNUMBER ''' keys = ['Semana', 'Cliente_ID', 'Producto_ID'] lag_feat = 'target_l' + str(num_lag) print('Creating lagged feature: %s' % lag_feat) data1 = df.loc[: , ['Cliente_ID', 'Producto_ID']] data1['Semana'] = df.loc[: , 'Semana'].apply(lambda x: x + 1) data1[lag_feat] = df.loc[: , 'target'] data1 = pd.groupby(data1, keys).mean().reset_index() return pd.merge(data, data1, how='left', on=keys, left_index=False, right_index=False, suffixes=('', '_lag' + str(num_lag)), copy=False) def create_freq_feats(data, column_name): ''' returns new data with a new column: column_name_freq which is the average weekly frequency of that value. ''' freq_feat = column_name + '_freq' print('Creating frequency feature: %s' % freq_feat) freq_frame = pd.groupby(data, [column_name, 'Semana'])['target'].count().reset_index() freq_frame.rename(columns={'target': freq_feat}, inplace=True) freq_frame = pd.groupby(freq_frame, [column_name])[freq_feat].mean().reset_index() return pd.merge(data, freq_frame, how='left', on=[column_name], left_index=False, right_index=False, suffixes=('', '_freq'), copy=False) def build_model(df, features, model_params): ''' df: dataframe to be modled features: column names of df that should be used in model params: {xgb_param_key: 'xgb_param_value'} ''' mask = df['Demanda_uni_equil'].isnull() test = df[mask] train = df[~mask] train.loc[: , 'target'] = train.loc[: , 'target'].apply(lambda x: np.log(x + 1)) xlf = xgb.XGBRegressor(**model_params) x_train, x_test, y_train, y_test = train_test_split(train[features], train['target'], test_size=0.01, random_state=1) xlf.fit(x_train, y_train, eval_metric='rmse', verbose=1, eval_set=[(x_test, y_test)], early_stopping_rounds=100) preds = xlf.predict(x_test) print('RMSE of log(Demanda_uni_equil[x_test]) : %s" ' % str(mean_squared_error(y_test,preds) ** 0.5)) print('Predicting Demanda_uni_equil for Semana 10') data_test_10 = test.loc[test['Semana'] == 10, features] preds = xlf.predict(data_test_10) data_test_10['Demanda_uni_equil'] = np.exp(preds) - 1 data_test_10['id'] = test.loc[test['Semana'] == 10, 'id'].astype(int).tolist() print('Creating lagged demand feature for Semana 11') data_test_lag = data_test_10[['Cliente_ID', 'Producto_ID']] data_test_lag['target_l1'] = data_test_10['Demanda_uni_equil'] data_test_lag = pd.groupby(data_test_lag,['Cliente_ID','Producto_ID']).mean().reset_index() print('Predicting Demanda_uni_equil for Semana 11') data_test_11 = test.loc[test['Semana'] == 11, features.difference(['target_l1'])] data_test_11 = pd.merge(data_test_11, data_test_lag, how='left', on=['Cliente_ID', 'Producto_ID'], left_index=False, right_index=False, sort=True, copy=False) data_test_11 = data_test_11.loc[: , features]#.replace(np.nan, 0, inplace=True) preds = xlf.predict(data_test_11) data_test_11['Demanda_uni_equil'] = np.exp(preds) - 1 data_test_11['id'] = test.loc[test['Semana'] == 11, 'id'].astype(int).tolist() return pd.concat([data_test_10.loc[: , ['id', 'Demanda_uni_equil']], data_test_11.loc[: , ['id', 'Demanda_uni_equil']]], axis=0, copy=True) ## --------------------------Execution----------------------------------------- ## read in and lightly clean the data df = get_dataframe() ## create lagged demand features for i in range(1, 1 + 5): if not BIG_LAG and i > 1: break df = create_lagged_feats(df, num_lag=i) df = df[df['Semana'] > 8] ## create frequnecy features for col_name in ['Agencia_ID', 'Ruta_SAK', 'Cliente_ID', 'Producto_ID']: df = create_freq_feats(df, col_name) # df = df.replace(np.nan, 0, inplace=True) ## choose features and train model feature_names = df.columns.difference(['id', 'target', 'Demanda_uni_equil']) print('Data Cleaned. Using features: %s' % feature_names) ## model parameters xgb_params = { 'max_depth': 10, 'learning_rate': 0.01, 'n_estimators': 75, 'subsample': .85, 'colsample_bytree': 0.7, 'objective': 'reg:linear', 'silent': True, 'nthread': -1, 'gamma': 0, 'min_child_weight': 1, 'max_delta_step': 0, 'subsample': 0.85, 'colsample_bytree': 0.7, 'colsample_bylevel': 1, 'reg_alpha': 0, 'reg_lambda': 1, 'scale_pos_weight': 1, 'missing': None, 'seed': 1 } ## build model and write submission to file. submission = build_model(df, feature_names, xgb_params) submission.to_csv('../output/submission.csv', index=False)