Kaggle competition two Sigma connect: rental listing inquiries (xgboost)

Kaggle competition , Website links ：Two Sigma Connect: Rental Listing Inquiries

According to the data information on the rental website , Predict the popularity of the house .（ This is a question of classification , Contains the following data , Variable with category 、 Integer variable 、 Text variable ）.

XGBoost Model

Use sklearn Complete modeling and prediction . The data set can be downloaded from the official website of the competition .
XGBoost Website
About XGBoost Explanation , I won't introduce . follow-up , A series of machine learning algorithms will be explained .

import os
import sys
import operator
import numpy as np
import pandas as pd
import zipfile
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, CountVectorizer

TfidfVectorizer, CountVectorizer see sklearn Official website or TfidfVectorizer, CountVectorizer

# Data acquisition , See another article for details 
train_df = pd.read_json(zipfile.ZipFile(r'E:\Kaggle\Kaggle_dataset01\two_sigma\train.json.zip').open('train.json'))
test_df = pd.read_json(zipfile.ZipFile(r'E:\Kaggle\Kaggle_dataset01\two_sigma\test.json.zip').open('test.json'))

Another article - Random forest method

# Feature Engineering 
features_to_use = ['bathrooms','bedrooms','latitude','longitude','price']
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"])

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['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"])

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)

# Model structures, 
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())
    
    xgbtrain = xgb.DMatrix(train_X,label=train_y)
    
    if test_y is not None:
        xgbtest = xgb.DMatrix(test_X,label=test_y)
        watchlist = [(xgbtrain,'train'),(xgbtest,'test')]
        model =xgb.train(plst,xgbtrain,num_rounds,watchlist,early_stopping_rounds=20,evals_result=evals_result)
    else:
        xgbtest = xgb.DMatrix(test_X)
        model = xgb.train(plst,xgbtrain,num_rounds)
    pred_test_y = model.predict(xgbtest)
    return pred_test_y,model

# model training 
cv_scores = []
test_loss = []
train_loss = []
evals_result = {
    }
import matplotlib.pyplot as plt
kf = model_selection.KFold(n_splits=5,shuffle=True,random_state=12)
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  # Removable , take KFlold All the conditions of training once . Choose the best 

# Model training prediction 
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.csv", index=False)

# Analysis of model training process （loss Trends ）
import matplotlib.pyplot as plt
plt.figure(figsize=(8,4))
plt.plot(list(range(int(len(evals_result['train']['mlogloss'])))),evals_result['train']['mlogloss'],'r-',label='train_loss')
plt.plot(list(range(int(len(evals_result['test']['mlogloss'])))),evals_result['test']['mlogloss'],'b-',label='test_loss')
plt.legend(loc='upper right')
plt.show()
# Here's the picture 

Run the above code ,XGBoost The effect of is much better than random forest , But it can continue to improve . Further improve the feature engineering part , Get better features （ Or better ）！