diff --git a/ml_mastery_tutorial/main_script.py b/ml_mastery_tutorial/main_script.py index f7caee7..e1bc422 100644 --- a/ml_mastery_tutorial/main_script.py +++ b/ml_mastery_tutorial/main_script.py @@ -12,6 +12,9 @@ from sklearn.metrics import accuracy_score from sklearn.model_selection import RepeatedKFold +# # Global Variable in use!! +# # global_feature_columns + ## original dataset helper functions -------------------------------- @@ -148,6 +151,8 @@ def create_toy_dataset(): # summarize first few examples print( '\nfirst few examples of toy dataset...') print(df.head()) + # Save the dataframe to a csv file + df.to_csv('toy_dataset.csv', index=False) return df # end def create_toy_dataset() @@ -173,6 +178,34 @@ def one_hot_encode(df, column_name): df = df.join(one_hot) return df +# def one_hot_encode_test_row(test_row: dict) -> pandas.DataFrame: +# temp_df = pandas.DataFrame(test_row, index=[0]) +# print(f'{temp_df = }') +# # one-hot encode each column +# for column_name in temp_df.columns: +# temp_df = one_hot_encode(temp_df, column_name) + +# print(f'one-hot encoded temp_df:\n') +# print(temp_df.head()) + +# return temp_df + +def one_hot_encode_test_row(test_row: dict) -> pandas.DataFrame: + # Create a dataframe from the test_row dictionary using the global_feature_columns as the columns + temp_df = pandas.DataFrame(columns=global_feature_columns) + # Create a row with all False values + temp_df.loc[0] = False + # print(f'------\n{temp_df = }') + # Iterate through the values in test_row and look for the corresponding column in temp_df + for key, value in test_row.items(): + # print(f'{key = }, {value = }') + if value in temp_df.columns: + # print(f'{value = } is in temp_df.columns') + # If the column exists, set the value in the first row to True + temp_df.at[0, value] = True + # print(f'After: \n{temp_df}') + return temp_df + def get_dataset(): """ Creates a toy-dataset in a dataframe. @@ -192,6 +225,10 @@ def get_dataset(): updated_feature_columns = df.columns[3:] # all columns except the first 3 (first 3 are labels) print(f'updated_feature_columns: {updated_feature_columns}') + # Assign updated_feature_columns to global variable + global global_feature_columns + global_feature_columns = updated_feature_columns + # print info about the dataframe print('-'*40) print('df.info()') @@ -262,6 +299,8 @@ def get_model(n_inputs, n_outputs): model = Sequential() # add a dense layer with 20 units, using 'relu' activation function model.add(Dense(20, input_dim=n_inputs, kernel_initializer='he_uniform', activation='relu')) + # add a dense layer with 10 units, using 'relu' activation function #TEST + model.add(Dense(10, kernel_initializer='he_uniform', activation='relu')) # add a dense layer with n_outputs units, using 'sigmoid' activation function model.add(Dense(n_outputs, activation='sigmoid')) # compile the model with binary cross-entropy loss and adam optimizer @@ -348,9 +387,10 @@ def manage_toy_dataset_processing(): print( 'finished manager model.fit()' ) - # # evaluate the model + # # # evaluate the model # results = evaluate_model(X, y) - + # print( f'Standard Deviation: {std(results):.3f} Accuracy Scores: ({results})' ) + # print( f'Averaged accuracy: {sum(results)/len(results):.3f}') # make a prediction for new data # row = [3, 3, 6, 7, 8, 2, 11, 11, 1, 3] @@ -360,16 +400,35 @@ def manage_toy_dataset_processing(): 'Jazzy Jeff', 'Popsicle', 'Rocky', '60s', '70s', '80s', '90s'] ''' - # FOR NEXT TIME: Create process to allow testing row in a more sensible way (i.e. using the same one-hot encoding process as the training data) - # BJD Has an idea involving a dictionary - row = [True, False, False, False, False, True, False, False, False, False, False, False, False, False, True, False, False] - print(f'Test row: blues, Blue Note Rock, 70s') - newX = asarray([row]) - yhat = model.predict(newX) - print('has_guitar, has_saxophone, has_vocals') - print('Predicted: %s' % yhat[0]) + print(f'{global_feature_columns = }') + # sys.exit("Stopping for testing") + #FOR NEXT TIME: Revise one_hot_encode_test_row() to take into account all the columns (we're using a global variable to store the column names) + + # row = [True, False, False, False, False, True, False, False, False, False, False, False, False, False, True, False, False] + test_rows = [ + {'genre': 'blues', 'artist': 'Blue Note Rock', 'decade': '70s'}, + {'genre': 'country', 'artist': 'Country Joe', 'decade': '80s'}, + {'genre': 'jazz', 'artist': 'Jazz on the Rocks', 'decade': '90s'}, + {'genre': 'pop', 'artist': 'Country of Pop', 'decade': '60s'}, + {'genre': 'rock', 'artist': 'Popsicle', 'decade': '70s'} + ] + + for test_row in test_rows: + print(f'Test row: {test_row}') + + encoded_test_row = one_hot_encode_test_row(test_row=test_row) + + # newX = asarray([row]) + newX = encoded_test_row.values + yhat = model.predict(newX) + print(' has_guitar, has_saxophone, has_vocals') + print(f'Predicted: ',end='') + for i in range(yhat.shape[1]): + print(f'{yhat[0][i]:.2f}', end=' ') + print() + ## end of manage_toy_dataset_processing() diff --git a/ml_mastery_tutorial/toy_dataset.csv b/ml_mastery_tutorial/toy_dataset.csv new file mode 100644 index 0000000..b205d1b --- /dev/null +++ b/ml_mastery_tutorial/toy_dataset.csv @@ -0,0 +1,1001 @@ +genre,artist,decade,has_guitar,has_saxophone,has_vocals +rock,Rocky,60s,1,0,1 +pop,Popsicle,70s,0,0,1 +jazz,Jazzy Jeff,80s,0,1,1 +blues,Blueman Group,90s,1,1,0 +country,Country Joe,60s,1,0,1 +rock,Jazz on the Rocks,70s,1,0,0 +pop,Blue Note Rock,80s,0,0,0 +jazz,Country of Pop,90s,0,1,0 +blues,Rocky,60s,1,1,1 +country,Popsicle,70s,1,0,1 +rock,Jazzy Jeff,80s,1,0,1 +pop,Blueman Group,90s,0,0,0 +jazz,Country Joe,60s,0,1,1 +blues,Jazz on the Rocks,70s,1,1,0 +country,Blue Note Rock,80s,1,0,0 +rock,Country of Pop,90s,1,0,0 +pop,Rocky,60s,0,0,1 +jazz,Popsicle,70s,0,1,1 +blues,Jazzy Jeff,80s,1,1,1 +country,Blueman Group,90s,1,0,0 +rock,Country Joe,60s,1,0,1 +pop,Jazz on the Rocks,70s,0,0,0 +jazz,Blue 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