SR

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor, BaggingRegressor
from nltk.stem.snowball import SnowballStemmer

df_train = pd.read_csv('C:/Users/Liubotao/Desktop/Search/input/train.csv', encoding="ISO-8859-1")
df_test = pd.read_csv('C:/Users/Liubotao/Desktop/Search/input/test.csv', encoding="ISO-8859-1")

df_desc = pd.read_csv('C:/Users/Liubotao/Desktop/Search/input/product_descriptions.csv')

df_all = pd.concat((df_train, df_test), axis=0, ignore_index=True)

df_all = pd.merge(df_all, df_desc, how='left', on='product_uid')

stemmer = SnowballStemmer('english')

def str_stemmer(s):
    return " ".join([stemmer.stem(word) for word in s.lower().split()])


def str_common_word(str1, str2):
    return sum(int(str2.find(word)>=0) for word in str1.split())

df_all['search_term'] = df_all['search_term'].map(lambda x:str_stemmer(x))

df_all['product_title'] = df_all['product_title'].map(lambda x:str_stemmer(x))

df_all['product_description'] = df_all['product_description'].map(lambda x:str_stemmer(x))

df_all['len_of_query'] = df_all['search_term'].map(lambda x:len(x.split())).astype(np.int64)

df_all['commons_in_title'] = df_all.apply(lambda 
                  x:str_common_word(x['search_term'],x['product_title']), axis=1)

df_all['commons_in_desc'] = df_all.apply(lambda 
                  x:str_common_word(x['search_term'],x['product_description']), axis=1)


df_train = df_all.loc[df_train.index]
df_test = df_all.loc[df_test.index]

test_ids = df_test['id']
y_train = df_train['relevance'].values
X_train = df_train.drop(['id','relevance'],axis=1).values
X_test = df_test.drop(['id','relevance'],axis=1).values
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
params = [1,3,5,6,7,8,9,10]
test_scores = []
for param in params:
    clf = RandomForestRegressor(n_estimators=30, max_depth=param)
    test_score = np.sqrt(-cross_val_score(clf, X_train, y_train, cv=5, scoring='neg_mean_squared_error'))
    test_scores.append(np.mean(test_score))
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(params, test_scores)
plt.title("Param vs CV Error");
rf = RandomForestRegressor(n_estimators=30, max_depth=6)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)