tokenizer = None label_encoder = None encoded_labels = None inputdata = None test_df = None text_X = None test_Y = None shaper = None model = None from reader import Reader from dataloader import Utils from dataloader import DataSet from model import ModelBuilder from writer import Writer if __name__ == "__main__" : df = Reader.read_csv("data","sentiment_train.csv",config="default",streamType="csv",columns="0,1",filter="full",count=5) tokenizer = Utils.get_text_tokenizer(df,1) label_encoder = Utils.get_label_encoder(df,0) encoded_labels = DataSet.get_encodered_labels(df,0,label_encoder) inputdata = DataSet.text_to_matrix(df,1,tokenizer) test_df = Reader.read_csv("data","sentiment_test.csv",config="default",streamType="df",columns="0,1",filter="full",count=5) text_X = DataSet.text_to_matrix(test_df,1,tokenizer) test_Y = DataSet.get_encodered_labels(test_df,0,label_encoder) shaper = tuple([inputdata, encoded_labels, text_X, test_Y]) model = ModelBuilder.train_model((ModelBuilder.create_model('KNN Classifier',shape='2,2',config='{"n_neighbors":1,"algorithm":"ball_tree","weights":"distance"}')),shaper,'true') Writer.write_csv((DataSet.get_label((ModelBuilder.predict_model(model,text_X)),0,label_encoder)),"default")
header="1", transformers=None) tokenizer = Utils.get_text_tokenizer(df, 1) label_encoder = Utils.get_label_encoder(df, 0) encoded_labels = DataSet.get_encodered_labels(df, 0, label_encoder) inputdata = DataSet.text_to_matrix(df, 1, tokenizer) modeldef = ModelBuilder.create_model( 'Keras Sequential Model', shape='2,2', config= ('{"loss_function":"categorical_crossentropy","optimizer":"adam"}', [ '{ "layer_type":"Dense" ,"activation":"relu","optimizer":"Adam","threshold":"512","input_shape":"10000,"}', '{ "layer_type":"Dropout" ,"activation":"relu","optimizer":"Adam","threshold":".5","input_shape":""}', '{ "layer_type":"Dense" ,"activation":"softmax","optimizer":"Adam","threshold":"4","input_shape":""}' ])) test_df = Reader.read_csv("data", "sentiment_test.csv", config="default", streamType="df", columns="0,1", filter="full", count=5, header="1", transformers=None) text_X = DataSet.text_to_matrix(test_df, 1, tokenizer) test_Y = DataSet.get_encodered_labels(test_df, 0, label_encoder) shaper = tuple([inputdata, encoded_labels, text_X, test_Y]) model = ModelBuilder.train_model(modeldef, shaper, 'true') result = ModelBuilder.predict_model(model, text_X) result = DataSet.get_label(result, 0, label_encoder) print(result)