Kaggle competition: Two Sigma Connect: Rental Listing Inquiries
model.load_weights(checkpoint_path)
 pred = model.predict(X_test)
 Y_pred =(pred > 0.5 ).astype(int )<prepare_output>
color = sns.color_palette()
%matplotlib inline
Y_pred = Y_pred[:, 0]
Y_pred<save_to_csv>
train_df = pd.read_json("/kaggle/input/two-sigma-connect-rental-listing-inquiries/train.json.zip")
 train_df.head()
submission = pd.DataFrame({"Survived": Y_pred}, index=test.index)
 submission.to_csv("submission.csv" )<load_from_csv>
test_df = pd.read_json("/kaggle/input/two-sigma-connect-rental-listing-inquiries/test.json.zip")
 print("Train Rows : ", train_df.shape[0])
 print("Test Rows : ", test_df.shape[0] )
pd.concat([submission, pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0)], axis=1 )<load_from_csv>
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
print(confusion_matrix(pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0 ).values, Y_pred))
accuracy_score(pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0 ).values, Y_pred )<load_from_csv>
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
precision_recall_fscore_support(pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0 ).values,
Y_pred, average='binary' )<define_search_space>
data_path = "/kaggle/input/two-sigma-connect-rental-listing-inquiries/"
train_file = data_path + "train.json.zip"
test_file = data_path + "test.json.zip"
train_df = pd.read_json(train_file)
 test_df = pd.read_json(test_file)
 print(train_df.shape)
 print(test_df.shape )
param_grid = [
{'penalty' : ['l1', 'l2', 'elasticnet', 'none'],
'C' : np.logspace(-4, 4, 500),
'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],
'max_iter' : np.arange(100, 150, 10)
 }
]
scoring = {'Accuracy': 'accuracy'}
gs = GridSearchCV(LogisticRegression() , return_train_score=True,
param_grid=param_grid, scoring=scoring, cv=10, refit='Accuracy', n_jobs=-1 )<train_model>
features_to_use = ["bathrooms", "bedrooms", "latitude", "longitude", "price"]
gs.fit(X_train, Y_train)
 print("best params: " + str(gs.best_estimator_))
print("best params: " + str(gs.best_params_))
print('best score:', gs.best_score_ )<predict_on_test>
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"] )
Y_pred2 = gs.predict(X_test )<save_to_csv>
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 )
submission2 = pd.DataFrame({"Survived": Y_pred2}, index=test.index)
 submission2.to_csv("submission2.csv" )<load_from_csv>
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"] )
pd.concat([submission2, pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0)], axis=1 )<load_from_csv>
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 )
print(confusion_matrix(pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0 ).values, Y_pred2))
accuracy_score(pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0 ).values, Y_pred2 )<load_from_csv>
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
precision_recall_fscore_support(pd.read_csv('.. /input/titanic/gender_submission.csv', index_col=0 ).values,
Y_pred2, average='binary' )<set_options>
preds, model = runXGB(train_X, train_y, test_X, num_rounds=400)
 out_df = pd.DataFrame(preds )
%matplotlib inline 
<load_from_csv>
out_df.columns = ["high", "medium", "low"]
train_df=pd.read_csv('/kaggle/input/titanic/train.csv')
 test_df=pd.read_csv('/kaggle/input/titanic/test.csv')
 train_df.head()<count_missing_values>
out_df["listing_id"] = test_df.listing_id.values
out_df
<count_missing_values><EOS> 
out_df.to_csv("xgb_starter2.csv", index=False )