How to resolve error while using truncatedSVD in python

@pagal_guy wrote:

hello,

I am trying to solve the https://www.kaggle.com/c/home-depot-product-search-relevance problem and in one place I am using:

rfr = RandomForestRegressor(n_estimators = 500, n_jobs = -1, random_state = 2016, verbose = 1)
tfidf = TfidfVectorizer(ngram_range=(1, 1), stop_words='english')
tsvd = TruncatedSVD(n_components=10, random_state = 2016)
clf = pipeline.Pipeline([
        ('union', FeatureUnion(
                    transformer_list = [
                        ('cst',  cust_regression_vals()),  
                        ('txt1', pipeline.Pipeline([('s1', cust_txt_col(key='search_term')), ('tfidf1', tfidf), ('tsvd1', tsvd)])),
                        ('txt2', pipeline.Pipeline([('s2', cust_txt_col(key='product_title')), ('tfidf2', tfidf), ('tsvd2', tsvd)])),
                        ('txt3', pipeline.Pipeline([('s3', cust_txt_col(key='product_description')), ('tfidf3', tfidf), ('tsvd3', tsvd)])),
                        ('txt4', pipeline.Pipeline([('s4', cust_txt_col(key='brand')), ('tfidf4', tfidf), ('tsvd4', tsvd)]))
                        ],
                    transformer_weights = {
                        'cst': 1.0,
                        'txt1': 0.5,
                        'txt2': 0.25,
                        'txt3': 0.0,
                        'txt4': 0.5
                        },
                n_jobs = -1
                )), 
        ('rfr', rfr)])
param_grid = {'rfr__max_features': [10], 'rfr__max_depth': [20]}
model = grid_search.GridSearchCV(estimator = clf, param_grid = param_grid, n_jobs = -1, cv = 5, verbose = 20, scoring=RMSE)
model.fit(X_train, y_train)

However I am getting an error:

Can someone please help me resolve this??

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