ClassicComputerScienceProblemsInPython/Chapter7/wine_test.py at master · sta2k/ClassicComputerScienceProblemsInPython · GitHub

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# wine_test.py

# From Classic Computer Science Problems in Python Chapter 7

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import csv

from typing import List

from util import normalize_by_feature_scaling

from network import Network

from random import shuffle

if __name__ == "__main__":

wine_parameters: List[List[float]] = []

wine_classifications: List[List[float]] = []

wine_species: List[int] = []

with open('wine.csv', mode='r') as wine_file:

wines: List = list(csv.reader(wine_file, quoting=csv.QUOTE_NONNUMERIC))

shuffle(wines) # get our lines of data in random order

for wine in wines:

parameters: List[float] = [float(n) for n in wine[1:14]]

wine_parameters.append(parameters)

species: int = int(wine[0])

if species == 1:

wine_classifications.append([1.0, 0.0, 0.0])

elif species == 2:

wine_classifications.append([0.0, 1.0, 0.0])

else:

wine_classifications.append([0.0, 0.0, 1.0])

wine_species.append(species)

normalize_by_feature_scaling(wine_parameters)

wine_network: Network = Network([13, 7, 3], 0.9)

def wine_interpret_output(output: List[float]) -> int:

if max(output) == output[0]:

return 1

elif max(output) == output[1]:

return 2

else:

return 3

# train over the first 150 wines 10 times

wine_trainers: List[List[float]] = wine_parameters[0:150]

wine_trainers_corrects: List[List[float]] = wine_classifications[0:150]

for _ in range(10):

wine_network.train(wine_trainers, wine_trainers_corrects)

# test over the last 28 of the wines in the data set

wine_testers: List[List[float]] = wine_parameters[150:178]

wine_testers_corrects: List[int] = wine_species[150:178]

wine_results = wine_network.validate(wine_testers, wine_testers_corrects, wine_interpret_output)

print(f"{wine_results[0]} correct of {wine_results[1]} = {wine_results[2] * 100}%")

ClassicComputerScienceProblemsInPython/Chapter7/wine_test.py at master · sta2k/ClassicComputerScienceProblemsInPython · GitHub

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