def rf_model(train, target, test, text_train_tfidf, text_test_tfidf):
text_train = train["Title"].values + ". " + train["BodyMarkdown"].values
text_test = test["Title"].values + ". " + test["BodyMarkdown"].values
print("Creating word2vec model...")
w2v.make_word2vec_model(text_train, text_test)
wv_train, wv_test = w2v.word2vec_features(text_train, text_test, load=False)
X_train, X_test = fe.extract_features(train), fe.extract_features(test)
X_train, X_test = fe.categories_to_counters(X_train, X_test, target)
X_train, X_test = fe.transform_features(X_train, X_test)
print("Creating linear model metafeature...")
X_train["LinearModelText"], X_test["LinearModelText"] = mf.linear_model_as_feature(text_train_tfidf, target, text_test_tfidf, load=False)
print("Creating word2vec model metafeature...")
X_train["w2vModelRFText"], X_test["w2vModelRFText"] = mf.w2v_model_as_feature(wv_train, target, wv_test, load=False, model_to_train="rf")
scaler = sklearn.preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model = sklearn.ensemble.RandomForestClassifier(criterion="entropy", max_depth=14, n_estimators=2000,
min_samples_leaf=4, min_samples_split=16, n_jobs=4, random_state=1234)
result = make_predictions(model, X_train, target, X_test)
io.save_result(test["PostId"], result)
return result
def linear_model(train, target, test, text_train_tfidf, text_test_tfidf):
X_train, X_test = fe.extract_features(train), fe.extract_features(test)
X_train, X_test = fe.categories_to_counters(X_train, X_test, target)
X_train, X_test = fe.transform_features(X_train, X_test)
feature_train = np.load("w2v/word2vec_feature_train")
feature_test = np.load("w2v/word2vec_feature_test")
X_train = np.column_stack((X_train.values, feature_train))
X_test = np.column_stack((X_test.values, feature_test))
scaler = sklearn.preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
X_train = scipy.sparse.hstack((text_train_tfidf, X_train), format="csr")
X_test = scipy.sparse.hstack((text_test_tfidf, X_test), format="csr")
model = sklearn.linear_model.LogisticRegression(C=0.7, penalty="l2")
result = make_predictions(model, X_train, target, X_test)
io.save_result(test["PostId"], result)
return result
