Kaggle competition: Two Sigma Connect: Rental Listing Inquiries
<SOS> metric: MulticlassLoss Kaggle data source: two-sigma-connect:-rental-listing-inquiries<groupby>
train_df=pd.read_json('.. /input/train.json')
 test_df=pd.read_json('.. /input/test.json' )
train_gp=train_df.groupby(['Sex','Pclass'])['Age'].mean()
print(train_gp)
 test_gp=test_df.groupby(['Sex','Pclass'])['Age'].mean()
print(test_gp)
<feature_engineering>
train_df["price_t"] =train_df["price"]/train_df["bedrooms"]
test_df["price_t"] = test_df["price"]/test_df["bedrooms"]
train_df["room_sum"] = train_df["bedrooms"]+train_df["bathrooms"]
test_df["room_sum"] = test_df["bedrooms"]+test_df["bathrooms"]
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(" ")))
 features_to_use=["bathrooms", "bedrooms", "latitude", "longitude", "price","price_t","num_photos", "num_features", "num_description_words","listing_id"]
def fillAgeNa(df):
for i in range(len(df)) :
if pd.isnull(df.loc[i, "Age"]):
if(df.loc[i,'Sex']=='female')and(df.loc[i,'Pclass']==1):
df.loc[i,'Age']=37
elif(df.loc[i,'Sex']=='female')and(df.loc[i,'Pclass']==2):
df.loc[i,'Age']=26
elif(df.loc[i,'Sex']=='female')and(df.loc[i,'Pclass']==3):
df.loc[i,'Age']=22
elif(df.loc[i,'Sex']=='male')and(df.loc[i,'Pclass']==1):
df.loc[i,'Age']=40
elif(df.loc[i,'Sex']=='male')and(df.loc[i,'Pclass']==2):
df.loc[i,'Age']=30
elif(df.loc[i,'Sex']=='male')and(df.loc[i,'Pclass']==3):
df.loc[i,'Age']=25
return df 
<categorify>
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'] = 0.03
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
ndf=train_df.copy()
train_df=fillAgeNa(ndf)
 train_df.isnull().sum()
ndf=test_df.copy()
test_df=fillAgeNa(ndf)
 train_df['Embarked'].fillna('S',inplace=True )<count_missing_values>
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 )
train_df.isnull().sum()<count_missing_values>
train_df['features'] = train_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
test_df['features'] = test_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
print(train_df["features"].head())
 tfidf = CountVectorizer(stop_words='english', max_features=200)
 tr_sparse = tfidf.fit_transform(train_df["features"])
 te_sparse = tfidf.transform(test_df["features"] )
test_df['Fare'].fillna(test_df['Fare'].mean() ,inplace=True)
 test_df.isnull().sum()
test_df.shape<drop_column>
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=train_df.drop('Cabin',axis=1)
 test_data=test_df.drop('Cabin',axis=1)
 test_data.shape<drop_column>
cv_scores = []
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2016)
 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
<categorify><EOS> 
preds, model = runXGB(train_X, train_y, test_X, num_rounds=940)
 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_starter4.csv", index=False )