def test_knn():
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
y = np.random.randint(2, size=100)
x = df.iloc[0:100, [0, 2]].values
print("Testing 2-D Iris data set with only one neighbor...")
neighbor = KNN(k=1)
neighbor.fit(x, y)
neighbor.plot(x, y)
print("Testing Iris data set with 15 neighbor...")
iris = datasets.load_iris()
x = iris.data[:, :2]
y = iris.target
neighbor = KNN(15)
neighbor.fit(x, y)
y_pred = neighbor.predict(x)
neighbor.accuracy(y_pred, y)
neighbor.plot(x, y)
print(
"Adding new point to dataset and testing with full Iris 1-k data set..."
)
neighbor = KNN(1)
neighbor.fit(x, y)
y2 = np.array([1])
y2 = np.append(y, y2)
x2 = np.vstack([x, [5.0, 3.2]])
neighbor.plot(x2, y2)
print("Testing SKLearn's model...")
clf = neighbors.KNeighborsClassifier(1)
clf.fit(x, y)
plot_decision_regions(x2, y2, clf)
def test_perceptron():
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
print("Creating a two-feature data set")
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
print("Creating Perceptron")
pn = Perceptron(0.1, 10)
print("Perceptron created")
pn.fit(X, y)
print("Perceptron fitted")
print("Error List")
print(pn.errors)
print("Weight vector")
print(pn.weight)
print("Using plot_decision_regions function")
plot_decision_regions(X, y, pn, 0.02)
pn.plot(X, y)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('Petal Length vs Sepal Length')
print("Creating a three-feature data set")
y = df.iloc[0:150, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:150, [0, 1, 2]].values
print("Creating Perceptron")
pn1 = Perceptron(0.1, 10)
print("Perceptron created")
pn1.fit(X, y)
print("Perceptron fitted")
print("Error List")
print(pn1.errors)
print("Weight vector")
print(pn1.weight)
print(
"Creating a perceptron that does not have enough iterations to learn the three-feature data set"
)
pn2 = Perceptron(0.1, 4)
print("Perceptron created")
pn2.fit(X, y)
print("Perceptron fitted")
print("Error List")
print(pn2.errors)
print("Weight vector")
print(pn2.weight)
def plot(self, X, y):
plot_decision_regions(X, y, self)
def main():
# IRIS DATASET #############################################################
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
# Extract the first 100 labels
y = df.iloc[0:100, 4].values
# Convert the labels to either 1 or 0
y = np.where(y == 'Iris-setosa', 0, 1)
# Extract features from dataset [sepal_length, petal_length]
X = df.iloc[0:100, [0, 2]].values
# plot variables
title = 'Iris Dataset'
xlabel = 'Sepal Length [cm]'
ylabel = 'Petal Length [cm]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the setosa data
plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')
# Plot the versicolor data
plt.scatter(X[50:100, 0],
X[50:100, 1],
color='blue',
marker='x',
label='versicolor')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn logistic regression
skLR = skLogisticRegression()
skLR.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
skLR,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title +
"\nscikit-learn Logistic Regression Model")
print(title + "\nscikit-learn Logistic Regression Model")
print(classification_report(y, skLR.predict(X)))
# ML.py logistic regression
mlLR = LogisticRegression(learning_rate=0.05, iterations=25)
mlLR.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
mlLR,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title + "\nML.py Logistic Regression Model")
print(title + "\nML.py Logistic Regression Model")
print(classification_report(y, mlLR.predict(X)))
# FISH DATASET #############################################################
df = pd.read_csv('./Fish.csv')
df = df.drop(df.index[0:61]) # Parkki rows
df = df.drop(df.index[11:84]) # Smelt rows
# Extract the data for Parkki and Smelt fish
y = df.iloc[:, 0].values
# Convert the labels to either 0 or 1
y = np.where(y == 'Parkki', 0, 1)
# Extract features from dataset [weight, length]
X = df.iloc[:, [2, 1]].values
# plot variables
title = 'Fish Dataset'
xlabel = 'Length [cm]'
ylabel = 'Weight [g]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the Parkki data
plt.scatter(X[:11, 0], X[:11, 1], color='red', marker='o', label='Parkki')
# Plot the Smelt data
plt.scatter(X[11:, 0], X[11:, 1], color='blue', marker='x', label='Smelt')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn logistic regression
skLR = skLogisticRegression()
skLR.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
skLR,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title +
"\nscikit-learn Logistic Regression Model")
print(title + "\nscikit-learn Logistic Regression Model")
print(classification_report(y, skLR.predict(X)))
# ML.py logistic regression
mlLR = LogisticRegression(learning_rate=0.01, iterations=10)
mlLR.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
mlLR,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title + "\nML.py Logistic Regression Model")
print(title + "\nML.py Logistic Regression Model")
print(classification_report(y, mlLR.predict(X)))
def main():
# IRIS DATASET #####################################################################################################
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
# Extract the first 100 labels
Y = df.iloc[0:100, 4].values
# Convert the labels to either 1 or -1
Y = np.where(Y == 'Iris-setosa', -1, 1)
# Extract features from dataset [sepal_length, petal_length]
X = df.iloc[0:100, [0, 2]].values
# plot variables
title = 'Iris Dataset'
xlabel = 'Sepal Length [cm]'
ylabel = 'Petal Length [cm]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the setosa data
plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')
# Plot the versicolor data
plt.scatter(X[50:100, 0], X[50:100, 1], color='blue', marker='x', label='versicolor')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn AdaBoost With scikit-learn Pre-built Decision Stump
boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1, max_leaf_nodes=2),
algorithm='SAMME', n_estimators=10, learning_rate=1.0)
boost.fit(X, Y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X, Y, boost, 0.01, x_label=xlabel, y_label=ylabel,
title=title + "\nscikit-learn Decision Stump Classifier")
print("scikit-learn AdaBoost With scikit-learn Pre-Built Decision Stump")
print("Score:", boost.score(X, Y))
# scikit-learn AdaBoost With Custom Decision Stump
boost = AdaBoostClassifier(base_estimator=AxisAlignedRectangles(),
algorithm='SAMME', n_estimators=10, learning_rate=1.0)
boost.fit(X, Y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X, Y, boost, 0.01, x_label=xlabel, y_label=ylabel,
title=title + "\nML.py Axis Aligned Rectangle Classifier")
print("scikit-learn AdaBoost With ML.py Axis Aligned Rectangle Classifier")
print("Score:", boost.score(X, Y))
####################################################################################################################
# FISH DATASET 1 ###################################################################################################
df = pd.read_csv('./Fish.csv')
df = df.drop(df.index[0:61]) # Parkki rows
df = df.drop(df.index[11:84]) # Smelt rows
# Extract the data for Parkki and Smelt fish
Y = df.iloc[:, 0].values
# Convert the labels to either 1 or -1
Y = np.where(Y == 'Parkki', -1, 1)
# Extract features from dataset [weight, length]
X = df.iloc[:, [2, 1]].values
# plot variables
title = 'Fish Dataset'
xlabel = 'Length [cm]'
ylabel = 'Weight [g]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the Parkki data
plt.scatter(X[:11, 0], X[:11, 1], color='red', marker='o', label='Parkki')
# Plot the Smelt data
plt.scatter(X[11:, 0], X[11:, 1], color='blue', marker='x', label='Smelt')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn AdaBoost With scikit-learn Pre-built Decision Stump
boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1, max_leaf_nodes=2),
algorithm='SAMME', n_estimators=10, learning_rate=1.0)
boost.fit(X, Y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X, Y, boost, x_label=xlabel, y_label=ylabel,
title=title + "\nscikit-learn Decision Stump Classifier")
print("\nscikit-learn AdaBoost With scikit-learn Pre-Built Decision Stump")
print("Score:", boost.score(X, Y))
# scikit-learn AdaBoost With Custom Decision Stump
boost = AdaBoostClassifier(base_estimator=AxisAlignedRectangles(),
algorithm='SAMME', n_estimators=10, learning_rate=1.0)
boost.fit(X, Y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X, Y, boost, 0.1, x_label=xlabel, y_label=ylabel,
title=title + "\nML.py Axis Aligned Rectangle Classifier")
print("scikit-learn AdaBoost With ML.py Axis Aligned Rectangle Classifier")
print("Score:", boost.score(X, Y))
###################################################################################################################
# FISH DATASET 2 ###################################################################################################
df = pd.read_csv('./Fish.csv')
# Extract the data for Bream and Roach fish
Y = df.iloc[:55, 0].values
# Convert the labels to either 1 or -1
Y = np.where(Y == 'Bream', -1, 1)
# Extract features from dataset [weight, length]
X = df.iloc[:55, [2, 1]].values
# plot variables
title = 'Fish Dataset'
xlabel = 'Length [cm]'
ylabel = 'Weight [g]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the Bream data
plt.scatter(X[:35, 0], X[:35, 1], color='red', marker='o', label='Bream')
# Plot the Roach data
plt.scatter(X[35:, 0], X[35:, 1], color='blue', marker='x', label='Roach')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn AdaBoost With scikit-learn Pre-built Decision Stump
boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1, max_leaf_nodes=2),
algorithm='SAMME', n_estimators=10, learning_rate=1.0)
boost.fit(X, Y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X, Y, boost, resolution=0.1, x_label=xlabel, y_label=ylabel,
title=title + "\nscikit-learn Decision Stump Classifier")
print("\nscikit-learn AdaBoost With scikit-learn Pre-Built Decision Stump")
print("Score:", boost.score(X, Y))
# scikit-learn AdaBoost With Custom Decision Stump
boost = AdaBoostClassifier(base_estimator=AxisAlignedRectangles(),
algorithm='SAMME', n_estimators=10, learning_rate=1.0)
boost.fit(X, Y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X, Y, boost, 0.1, x_label=xlabel, y_label=ylabel,
title=title + "\nML.py Axis Aligned Rectangle Classifier")
print("scikit-learn AdaBoost With ML.py Axis Aligned Rectangle Classifier")
print("Score:", boost.score(X, Y))
def main():
# IRIS DATASET #############################################################
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
# Extract the first 100 labels
y = df.iloc[0:100, 4].values
# Convert the labels to either 1 or 0
y = np.where(y == 'Iris-setosa', 0, 1)
# Extract features from dataset [sepal_length, petal_length]
X = df.iloc[0:100, [0, 2]].values
# plot variables
title = 'Iris Dataset'
xlabel = 'Sepal Length [cm]'
ylabel = 'Petal Length [cm]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the setosa data
plt.scatter(X[:50, 0], X[:50, 1], color='red', marker='o', label='setosa')
# Plot the versicolor data
plt.scatter(X[50:100, 0],
X[50:100, 1],
color='blue',
marker='x',
label='versicolor')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn k-Nearest Neighbors
k = 1
neigh = KNeighborsClassifier(n_neighbors=k)
neigh.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
neigh,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title +
"\nscikit-learn k-Nearest Neighbors\nk = " + str(k))
print(title + "\nscikit-learn k-Nearest Neighbors\nk = " + str(k))
print(classification_report(y, neigh.predict(X)))
# ML.py k-Nearest Neighbors
k = 1 # NOTE: the ML.py k-Nearest Neighbors class currently only supports k=1
knn = NearestNeighbors(k)
knn.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
knn,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title + "\nML.py k-Nearest Neighbors\nk = " +
str(k))
print(title + "\nML.py k-Nearest Neighbors\nk = " + str(k))
print(classification_report(y, knn.predict(X)))
############################################################################
# IRIS DATASET 2 ###########################################################
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header=None)
# Extract 100 labels
y = df.iloc[50:150, 4].values
# Convert the labels to either 1 or 0
y = np.where(y == 'Iris-versicolor', 0, 1)
# Extract features from dataset [sepal_length, petal_length]
X = df.iloc[50:150, [0, 2]].values
# plot variables
title = 'Iris Dataset'
xlabel = 'Sepal Length [cm]'
ylabel = 'Petal Length [cm]'
# Plot what we have so far
# Plot labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
# Plot the versicolor data
plt.scatter(X[:50, 0],
X[:50, 1],
color='red',
marker='o',
label='versicolor')
# Plot the virginica data
plt.scatter(X[50:100, 0],
X[50:100, 1],
color='blue',
marker='x',
label='virginica')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# scikit-learn k-Nearest Neighbors
k = 1
neigh = KNeighborsClassifier(n_neighbors=k)
neigh.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
neigh,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title +
"\nscikit-learn k-Nearest Neighbors\nk = " + str(k))
print(title + "\nscikit-learn k-Nearest Neighbors\nk = " + str(k))
print(classification_report(y, neigh.predict(X)))
# ML.py k-Nearest Neighbors
k = 1 # NOTE: the ML.py k-Nearest Neighbors class currently only supports k=1
knn = NearestNeighbors(k)
knn.fit(X, y)
# plot the decision regions and display metrics to the console
plot_decision_regions(X,
y,
knn,
resolution=0.1,
x_label=xlabel,
y_label=ylabel,
title=title + "\nML.py k-Nearest Neighbors\nk = " +
str(k))
print(title + "\nML.py k-Nearest Neighbors\nk = " + str(k))
print(classification_report(y, knn.predict(X)))
# Plot the versicolor data
plt.scatter(X[50:100, 0], X[50:100, 1], color='blue', marker='x', label='versicolor')
# Setup the plot legend
plt.legend(loc='upper left')
# Display the plot
plt.show()
# Setup the Perceptron
pn = Perceptron(0.1, 10)
# Fit X to y (i.e. find the weights)
pn.fit(X, y)
# Print the error array
print("Errors:", pn.errors)
# Plot the results of the first fit
plt.plot(range(1, len(pn.errors) + 1), pn.errors, marker='o')
plt.title('Iris Dataset')
plt.xlabel('Iteration')
plt.ylabel('# of Misclassifications')
plt.show()
print("Net Input X:", pn.net_input(X))
print("Predict X:", pn.predict(X))
print("Weights:", pn.weight)
plot_decision_regions(X, y, pn, x_label='Sepal Length [cm]', y_label='Petal Length [cm]', title='Iris Dataset')
