Kaggle competition: Two Sigma Connect: Rental Listing Inquiries
<SOS> metric: MulticlassLoss Kaggle data source: two-sigma-connect:-rental-listing-inquiries<concatenate>
import os
import sys
import operator
import numpy as np
import pandas as pd
from scipy import sparse
import xgboost as xgb
from sklearn import model_selection, preprocessing, ensemble
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer
train_data=pd.concat([train_data,encd1],axis=1)
 test_data=pd.concat([test_data,encd2],axis=1)
<feature_engineering>
def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_rounds=1000):
param = {}
param['objective'] = 'multi:softprob'
param['eta'] = 0.1
param['max_depth'] = 6
param['silent'] = 1
param['num_class'] = 3
param['eval_metric'] = "mlogloss"
param['min_child_weight'] = 1
param['subsample'] = 0.7
param['colsample_bytree'] = 0.7
param['seed'] = seed_val
num_rounds = num_rounds
plst = list(param.items())
 xgtrain = xgb.DMatrix(train_X, label=train_y)
 if test_y is not None:
xgtest = xgb.DMatrix(test_X, label=test_y)
 watchlist = [(xgtrain,'train'),(xgtest, 'test')]
model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=20)
 else:
xgtest = xgb.DMatrix(test_X)
 model = xgb.train(plst, xgtrain, num_rounds)
 pred_test_y = model.predict(xgtest)
 return pred_test_y, model
train_data['FamilySize']=train_data['SibSp']+train_data['Parch']+1
test_data['FamilySize']=test_data['SibSp']+test_data['Parch']+1 
<feature_engineering>
data_path = ".. /input/"
train_file = data_path + "train.json"
test_file = data_path + "test.json"
train_df = pd.read_json(train_file)
 test_df = pd.read_json(test_file)
 print(train_df.shape)
 print(test_df.shape )
train_data['Isalone']=train_data['FamilySize'].apply(lambda x : 1 if x>1 else 0)
 test_data['Isalone']=test_data['FamilySize'].apply(lambda x : 1 if x>1 else 0 )<import_modules>
features_to_use = ["bathrooms", "bedrooms", "latitude", "longitude", "price"]
from sklearn.preprocessing import LabelEncoder<drop_column>
train_df["num_photos"] = train_df["photos"].apply(len)
 test_df["num_photos"] = test_df["photos"].apply(len)
 train_df["num_features"] = train_df["features"].apply(len)
 test_df["num_features"] = test_df["features"].apply(len)
 train_df["num_description_words"] = train_df["description"].apply(lambda x: len(x.split(" ")))
 test_df["num_description_words"] = test_df["description"].apply(lambda x: len(x.split(" ")))
 train_df["created"] = pd.to_datetime(train_df["created"])
 test_df["created"] = pd.to_datetime(test_df["created"])
 train_df["created_year"] = train_df["created"].dt.year
test_df["created_year"] = test_df["created"].dt.year
train_df["created_month"] = train_df["created"].dt.month
test_df["created_month"] = test_df["created"].dt.month
train_df["created_day"] = train_df["created"].dt.day
test_df["created_day"] = test_df["created"].dt.day
train_df["created_hour"] = train_df["created"].dt.hour
test_df["created_hour"] = test_df["created"].dt.hour
features_to_use.extend(["num_photos", "num_features", "num_description_words","created_year", "created_month", "created_day", "listing_id", "created_hour"] )
train_data.drop(['Sex','Embarked','FamilySize'],axis=1,inplace=True)
 test_data.drop(['Sex','Embarked','FamilySize'],axis=1,inplace=True)
<normalization>
categorical = ["display_address", "manager_id", "building_id", "street_address"]
for f in categorical:
if train_df[f].dtype=='object':
lbl = preprocessing.LabelEncoder()
lbl.fit(list(train_df[f].values)+ list(test_df[f].values))
train_df[f] = lbl.transform(list(train_df[f].values))
test_df[f] = lbl.transform(list(test_df[f].values))
features_to_use.append(f )
scaler=MinMaxScaler()
scaler.fit(train_data[['Fare']])
 train_data['Fare']=scaler.transform(train_data[['Fare']])
 train_data['Age']=scaler.fit_transform(train_data[['Age']])
 test_data['Fare']=scaler.fit_transform(test_data[['Fare']])
 test_data['Age']=scaler.fit_transform(test_data[['Age']] )<feature_engineering>
train_df['features'] = train_df["features"].apply(lambda x: " ".join(x))
test_df['features'] = test_df["features"].apply(lambda x: " ".join(x))
print(train_df["features"].head())
 tfidf = TfidfVectorizer(stop_words='english', max_features=100)
 tr_sparse = tfidf.fit_transform(train_df["features"])
 te_sparse = tfidf.transform(test_df["features"] )
train_data['SibSp']=train_data['SibSp'].apply(lambda x: 1 if x>0 else 0)
 test_data['SibSp']=test_data['SibSp'].apply(lambda x: 1 if x>0 else 0 )<feature_engineering>
train_X = sparse.hstack([train_df[features_to_use], tr_sparse] ).tocsr()
test_X = sparse.hstack([test_df[features_to_use], te_sparse] ).tocsr()
target_num_map = {'high':0, 'medium':1, 'low':2}
train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))
print(train_X.shape, test_X.shape )
train_data['nw']=train_data['Age']*train_data['Pclass']
test_data['nw']=test_data['Age']*test_data['Pclass']<prepare_x_and_y>
cv_scores = []
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=42)
 for dev_index, val_index in kf.split(range(train_X.shape[0])) :
dev_X, val_X = train_X[dev_index,:], train_X[val_index,:]
dev_y, val_y = train_y[dev_index], train_y[val_index]
preds, model = runXGB(dev_X, dev_y, val_X, val_y)
 cv_scores.append(log_loss(val_y, preds))
print(cv_scores)
 break
<train_on_grid><EOS> 
preds, model = runXGB(train_X, train_y, test_X, num_rounds=400)
 out_df = pd.DataFrame(preds)
 out_df.columns = ["high", "medium", "low"]
out_df["listing_id"] = test_df.listing_id.values
out_df.to_csv("xgb_starter.csv", index=False )