#print(cross_validation(5, X_train.to_numpy(), Processor.ToNumpyCol(Y_train), model))
elif ds == "mam":
path = "./datasets/mam/mam.data"
header = ["BI-RADS", "age", "shape", "margin", "density", "result"]
All = Processor.read(path, header)
[X, Y] = Clean.mam(All)
[X_train, X_test, Y_train, Y_test] = Processor.split(X, Y, train=0.8)
model = NaiveBayes()
print(
cross_validation(5, X_train.to_numpy(), Processor.ToNumpyCol(Y_train),
model))
elif ds == "ttt":
path = "./datasets/tictactoe/tic-tac-toe.data"
header = ["tl", "tm", "tr", "ml", "mm", "mr", "bl", "bm", "br", "result"]
All = Processor.read(path, header)
[X, Y] = Clean.ttt(All)
print(X.shape)
[X_train, X_test, Y_train, Y_test] = Processor.split(X, Y, train=0.8)
model = NaiveBayes()
if type(model) == NaiveBayes:
X = X.to_numpy()
Y = Processor.ToNumpyCol(Y)
iters = np.arange(20, X.shape[0], 50)
#print(X.shape)
#print(Y.shape)
accuracies = []
for iter_ in iters:
#rowsX = X[0:X.shape[0], :]
#rowsY = Y[0:Y.shape[0], :]
rowsX = X[0:iter_, :]
rowsY = Y[0:iter_, :]
#acc, _, _ = cross_validation(5, rowsX, rowsY, LogisticRegression(), learning_rate=0.1, max_gradient=1e-3, max_iters=iter_)
acc = cross_validation(5, rowsX, rowsY, NaiveBayes())
accuracies.append(acc)
"""path = "../datasets/ionosphere/ionosphere.data"
header = ["{}{}".format("col", x) for x in range(33 + 1)]
header.append("signal")
All = Processor.read(path, header)
[X, Y] = Clean.Ionosphere(All)
path = "../datasets/adult/adult.data"
header = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation',
'relationship',
'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'salary']
All = Processor.read(path, header)
from Project1.src.HPTuning import df_to_table
import pandas as pd
# Find accuracies for ionosphere data set
print("Analyzing the ionosphere data set")
path = "../datasets/ionosphere/ionosphere.data"
header = ["{}{}".format("col", x) for x in range(33 + 1)]
header.append("signal")
All = Processor.read(path, header)
[X, Y] = Clean.Ionosphere(All)
ionosphere_results = ['ionosphere']
acc, _, _ = cross_validation(5,
X.to_numpy(),
Processor.ToNumpyCol(Y),
LogisticRegression(),
learning_rate=1.0,
max_gradient=1e-2,
max_iters=50000)
ionosphere_results.append(round(acc, 2))
acc = cross_validation(5, X.to_numpy(), Processor.ToNumpyCol(Y), NaiveBayes())
ionosphere_results.append(round(acc, 2))
print(ionosphere_results)
# Find accuracies for adult data set
print("Analyzing the adult data set")
path = "../datasets/adult/adult.data"
header = [
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
if dataset == 'ionosphere':
header = ["{}{}".format("col", x) for x in range(33 + 1)]
header.append("signal")
All = Processor.read(path, header)
[X, Y] = Clean.Ionosphere(All)
[X_train, X_test, Y_train, Y_test] = Processor.split(X, Y, train=0.8)
results = []
for rate in learning_rates:
r = cross_validation(5, X_train.to_numpy(), Processor.ToNumpyCol(Y_train), LogisticRegression(),
learning_rate=rate, max_gradient=1e-2, max_iters=50000, random=False)
r.insert(0, rate)
results.append(r)
df = pd.DataFrame(results, columns=['learning rate', 'accuracy', 'last gradient', 'iterations'])
df_to_table(df, 'ionosphere_table')
elif dataset == 'adult':
header = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation',
'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
'salary']
All = Processor.read(path, header)
[X, Y] = Clean.adult(All)
header = ["{}{}".format("col", x) for x in range(33 + 1)]
header.append("signal")
All = Processor.read(path, header)
[X, Y] = Clean.Ionosphere(All)
[X_train, X_test, Y_train, Y_test] = Processor.split(X, Y, train=0.8)
model = LogisticRegression()
print(
cross_validation(5,
X_train.to_numpy(),
Processor.ToNumpyCol(Y_train),
model,
learning_rate=0.2))
elif ds == "mam":
path = "./datasets/mam/mam.data"
header = ["BI-RADS", "age", "shape", "margin", "density", "result"]
All = Processor.read(path, header)
[X, Y] = Clean.mam(All)
[X_train, X_test, Y_train, Y_test] = Processor.split(X, Y, train=0.8)
model = LogisticRegression()
print(