def run(self, train_returns, train_sizes, bin_number): cross_val = CrossValidation.CrossVal(train_sizes, bin_number) bins = cross_val.get_bins() scores = self.predict(train_returns, bins) self.interpret_scores(scores) print(scores) #decile scores
def run(self, train_corpus, freq_type, stopwords, train_returns, bin_number, filename): vectorizer = MatrixVectorizer.Vectorizer(train_corpus, freq_type, stopwords) train_count_matrix = vectorizer.get_count_matrix() negative_word_list = self.create_negative_stuff(filename) negative_word_matrix = vectorizer.transform_new_data( negative_word_list) document_scores = self.score_documents(negative_word_matrix, train_count_matrix) cross_val = CrossValidation.CrossVal(document_scores, bin_number) bins = cross_val.get_bins() scores = self.predict(train_returns, bins) self.interpret_scores(scores) print(scores)
def run(self, train_corpus, freq_type, stopwords, test_corpus, svals, reduce_type, test_returns, bin_number, train_returns, method): vectorizer = MatrixVectorizer.Vectorizer(train_corpus, freq_type, stopwords) train_count_matrix = vectorizer.get_count_matrix() test_count_matrix = vectorizer.transform_new_data(test_corpus) dim_reducer = DimensionReducer.Reducer(train_count_matrix) reduced_train_count_matrix = dim_reducer.reduce_dimension( svals, reduce_type) reduced_test_count_matrix = dim_reducer.reduce_more_data( test_count_matrix.todense()) cross_val = CrossValidation.CrossVal(test_returns, bin_number) bins = cross_val.get_bins() scores = self.fit_predict(reduced_train_count_matrix, train_returns, reduced_test_count_matrix, bins, method) self.interpret_scores(scores)