Python generate_cm示例

示例#1

文件： base-mlp.py 项目： pobudzey/ml-experiments

import pandas as pd

from utils import data
from utils import metrics
from sklearn.neural_network import MLPClassifier

# Dataset 1 (Latin letters)
# Training
trainX, trainY = data.load_data('train_1.csv')
clf = MLPClassifier(activation='logistic', solver='sgd')
clf.fit(trainX, trainY)
# Testing
testX, testY = data.load_data('test_with_label_1.csv')
predictions = pd.DataFrame(clf.predict(testX))
data.generate_csv(predictions, 'Base-MLP-DS1.csv')
metrics.compute(predictions, testY, 'Base-MLP-DS1.csv')
data.generate_cm(predictions, testY, 'Base-MLP-DS1.png')

# Dataset 2 (Greek letters)
# Training
trainX, trainY = data.load_data('train_2.csv')
clf = MLPClassifier(activation='logistic', solver='sgd')
clf.fit(trainX, trainY)
# Testing
testX, testY = data.load_data('test_with_label_2.csv')
predictions = pd.DataFrame(clf.predict(testX))
data.generate_csv(predictions, 'Base-MLP-DS2.csv')
metrics.compute(predictions, testY, 'Base-MLP-DS2.csv')
data.generate_cm(predictions, testY, 'Base-MLP-DS2.png')

示例#2

文件： gnb.py 项目： pobudzey/ml-experiments

import pandas as pd

from utils import data
from utils import metrics
from sklearn.naive_bayes import GaussianNB

# Dataset 1 (Latin letters)
# Training
trainX, trainY = data.load_data('train_1.csv')
clf = GaussianNB()
clf.fit(trainX, trainY)
# Testing
testX, testY = data.load_data('test_with_label_1.csv')
predictions = pd.DataFrame(clf.predict(testX))
data.generate_csv(predictions, 'GNB-DS1.csv')
metrics.compute(predictions, testY, 'GNB-DS1.csv')
data.generate_cm(predictions, testY, 'GNB-DS1.png')

# Dataset 2 (Greek letters)
# Training
trainX, trainY = data.load_data('train_2.csv')
clf = GaussianNB()
clf.fit(trainX, trainY)
# Testing
testX, testY = data.load_data('test_with_label_2.csv')
predictions = pd.DataFrame(clf.predict(testX))
data.generate_csv(predictions, 'GNB-DS2.csv')
metrics.compute(predictions, testY, 'GNB-DS2.csv')
data.generate_cm(predictions, testY, 'GNB-DS2.png')

示例#3

文件： best-dt.py 项目： pobudzey/ml-experiments

# Dataset 1 (Latin letters)
# Training
trainX, trainY = data.load_data('train_1.csv')
clf = GridSearchCV(DecisionTreeClassifier(), param_grid, verbose=1)
clf.fit(trainX, trainY)
# Validation
validX, validY = data.load_data('val_1.csv')
print(f'Score: {round(clf.score(validX, validY), 3)}')
print(f'Parameters chosen: {clf.best_params_}')
# Testing
testX, testY = data.load_data('test_with_label_1.csv')
predictions = pd.DataFrame(clf.predict(testX))
data.generate_csv(predictions, 'Best-DT-DS1.csv')
metrics.compute(predictions, testY, 'Best-DT-DS1.csv')
data.generate_cm(predictions, testY, 'Best-DT-DS1.png')

# Dataset 2 (Greek letters)
# Training
trainX, trainY = data.load_data('train_2.csv')
clf = GridSearchCV(DecisionTreeClassifier(), param_grid, verbose=1)
clf.fit(trainX, trainY)
# Validation
validX, validY = data.load_data('val_2.csv')
print(f'Score: {round(clf.score(validX, validY), 3)}')
print(f'Parameters chosen: {clf.best_params_}')
# Testing
testX, testY = data.load_data('test_with_label_2.csv')
predictions = pd.DataFrame(clf.predict(testX))
data.generate_csv(predictions, 'Best-DT-DS2.csv')
metrics.compute(predictions, testY, 'Best-DT-DS2.csv')