def test_perceptron(self):
# train the perceptron model
perceptron = Perceptron(learning_rate=0.1, num_epochs=10)
perceptron.fit(self.x, self.y)
# plot learning
curve = {
'cost_length': len(perceptron.cost),
'cost': perceptron.cost,
'marker': 'o',
'x_label': 'Epochs',
'y_label': 'Number of updates',
'title': 'Perceptron - Learning rate 0.1'
}
Plotter.plot_learning_curve(
curve,
FilesystemUtils.get_test_resources_plot_file_name(
'perceptron/Perceptron-Learning-Curve.png'))
# plot decision boundary
diagram_options = {
'x_label': 'sepal length [cm]',
'y_label': 'petal length [cm]',
'legend': 'upper left'
}
Plotter.plot_decision_boundary(
self.x,
self.y,
classifier=perceptron,
diagram_options=diagram_options,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'perceptron/Perceptron-Decision-Boundary.png'))
def test_scikit_learn_decision_tree(self):
# Train the decision tree.
# Most algorithms in scikit-learn already support multiclass classification via the One-versus-Rest (OvR) method
tree = DecisionTreeClassifier(criterion='gini',
max_depth=4,
random_state=1)
tree.fit(self.x_train, self.y_train)
self.predict_and_evaluate(
tree,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'decision_tree/DecisionTree-ScikitLearn-Decision-Boundary.png')
)
# save the tree as a digram
dot_data = export_graphviz(
tree,
filled=True,
rounded=True,
class_names=['Setosa', 'Versicolor', 'Virginica'],
feature_names=['petal length', 'petal width'],
out_file=None)
graph = graph_from_dot_data(dot_data)
graph.write_png(
FilesystemUtils.get_test_resources_plot_file_name(
'decision_tree/DecisionTree-ScikitLearn-Tree-Diagram.png'))
def test_logistic_regresssion(self):
# train the logistic regression model
logistic_regression = LogisticRegressionBGD(learning_rate=0.05,
num_epochs=1000)
logistic_regression.fit(self.x, self.y)
# plot learning curve
curve = {
'cost_length': len(logistic_regression.cost),
'cost': np.log10(logistic_regression.cost),
'marker': 'o',
'x_label': 'Epochs',
'y_label': 'Number of updates',
'title': 'Logistic regression - Learning rate 0.05'
}
Plotter.plot_learning_curve(
curve,
FilesystemUtils.get_test_resources_plot_file_name(
'logistic_regression/LogisticRegressionBGD-Learning-Curve.png')
)
# plot decision boundary
diagram_options = {
'x_label': 'sepal length [cm]',
'y_label': 'petal length [cm]',
'legend': 'upper left'
}
Plotter.plot_decision_boundary(
self.x,
self.y,
classifier=logistic_regression,
diagram_options=diagram_options,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'logistic_regression/LogisticRegressionBGD-Decision-Boundary.png'
))
def test_adaline(self):
# train the first with bigger learning ratio
adaline1 = AdalineBGD(learning_rate=0.01, num_epochs=30)
adaline1.fit(self.x, self.y)
# train the second adaline model with smaller learning ration
adaline2 = AdalineBGD(learning_rate=0.0001, num_epochs=30)
adaline2.fit(self.x, self.y)
# plot multiple learning curves for both adaline trained models
curves = [{
'cost_length': len(adaline1.cost),
'cost': np.log10(adaline1.cost),
'marker': 'o',
'x_label': 'Epochs',
'y_label': 'log(Sum-squared-error)',
'title': 'Adaline - Learning rate 0.1'
}, {
'cost_length': len(adaline2.cost),
'cost': np.log10(adaline2.cost),
'marker': 'o',
'x_label': 'Epochs',
'y_label': 'log(Sum-squared-error)',
'title': 'Adaline - Learning rate 0.0001'
}]
Plotter.plot_multiple_learning_curves(
curves,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'adaline/AdalineBGD-Learning-Curves.png'))
# plot decision boundary for divergent model (adaline 1)
Plotter.plot_decision_boundary(
self.x,
self.y,
classifier=adaline1,
diagram_options={
'x_label': 'sepal length [cm]',
'y_label': 'petal length [cm]',
'legend': 'upper left'
},
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'adaline/AdalineBGD-Decision-Boundary-Divergent.png'))
# plot decision boundary for convergent model (adaline 2)
Plotter.plot_decision_boundary(
self.x,
self.y,
classifier=adaline2,
diagram_options={
'x_label': 'sepal length [cm]',
'y_label': 'petal length [cm]',
'legend': 'upper left'
},
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'adaline/AdalineBGD-Decision-Boundary-Convergent.png'))
def setUp(self):
# load subset of Iris data
iris_data_reader = IrisDataReader(
FilesystemUtils.get_resources_data_file_name('iris/iris.data'))
self.x, self.y = iris_data_reader.get_data()
# plotter data and save it to file
Plotter.plot_iris_data_set(
self.x,
FilesystemUtils.get_test_resources_plot_file_name(
'adaline/Adaline-Training-Set.png'))
def test_scikit_learn_svm_nonlinear_iris(self):
svm = SVC(kernel='rbf', random_state=1, gamma=0.2, C=1.0)
svm.fit(self.x_train, self.y_train)
self.predict_and_evaluate(
svm,
FilesystemUtils.get_test_resources_plot_file_name(
'svm/SVM-ScikitLearn-LowGamma-Decision-Boundary.png'))
svm = SVC(kernel='rbf', random_state=1, gamma=100.0, C=1.0)
svm.fit(self.x_train, self.y_train)
self.predict_and_evaluate(
svm,
FilesystemUtils.get_test_resources_plot_file_name(
'svm/SVM-ScikitLearn-HighGamma-Decision-Boundary.png'))
def test_draw_impurity_criteria_types(self):
# For a visual comparison of the three different impurity criteria (Entropy, Gini, Misclassification Error),
# let us plot the impurity indices for the probability range [0, 1] for class 1.
# Note that we will also add a scaled version of the entropy (entropy / 2) to observe that the Gini impurity is
# an intermediate measure between entropy and the classification error.
x_range = np.arange(start=0.0, stop=1.0, step=0.01, dtype=float)
# compute entropy and scaled entropy
ent = [self.entropy(p) if p != 0 else None for p in x_range]
sc_ent = [e * 0.5 if e else None for e in ent]
# compute gini
gn = [self.gini(p) for p in x_range]
# computer misclassification error
err = [self.misclassification_error(p) for p in x_range]
Plotter.plot_impurity_criteria(
x_range,
entropy=ent,
scaled_entropy=sc_ent,
gini=gn,
misclassification_error=err,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'decision_tree/DecisionTree-ScikitLearn-Impurity-Criteria.png')
)
def test_scikit_learn_validation_curves_on_wdbc_pipeline(self):
# create a pipeline to test
wdbc_pipeline = make_pipeline(
StandardScaler(), PCA(n_components=2),
LogisticRegression(solver='lbfgs', random_state=42))
# prepare validation curves
# Validation curves are a useful tool for improving the performance of a model by addressing issues such as
# overfitting or underfitting.
# Validation curves are related to learning curves, but instead of plotting the training and test accuracies as
# functions of the sample size, we vary the values of the model parameters, for example, the
# inverse regularization parameter C in logistic regression.
num_folds = 10
param_range = np.array([0.001, 0.01, 0.1, 1.0, 10.0, 100.0])
train_scores, test_scores = validation_curve(
estimator=wdbc_pipeline,
X=self.x_train,
y=self.y_train,
param_name='logisticregression__C',
param_range=param_range,
cv=num_folds,
n_jobs=-1)
image_file_path = FilesystemUtils.get_test_resources_plot_file_name(
'model_performance/ModelPerformance-ScikitLearn-ValidationCurves.png'
)
Plotter.plot_validation_curves(param_range,
train_scores,
test_scores,
image_file_path=image_file_path)
def test_scikit_learn_learning_curves_on_wdbc_pipeline(self):
# create a pipeline to test
wdbc_pipeline = make_pipeline(
StandardScaler(), PCA(n_components=2),
LogisticRegression(solver='lbfgs', random_state=42))
# prepare learning curves
# Via the train_sizes parameter in the learning_curve function, we can control the absolute or relative number
# of training samples that are used to generate the learning curves.
# Here, we set train_sizes=np.linspace(0.1, 1.0, 10) to use 10 evenly spaced, relative intervals for the
# training set sizes. By default, the learning_curve function uses stratified k-fold cross-validation to
# calculate the cross-validation accuracy of a classifier, and we set k=num_folds via the cv parameter for
# k-fold stratified cross-validation.
# Finally, we plot the diagram using a helper function.
num_folds = 10
train_sizes, train_scores, test_scores = learning_curve(
estimator=wdbc_pipeline,
X=self.x_train,
y=self.y_train,
train_sizes=np.linspace(0.1, 1.0, 10),
cv=num_folds,
n_jobs=-1)
image_file_path = FilesystemUtils.get_test_resources_plot_file_name(
'model_performance/ModelPerformance-ScikitLearn-LearningCurves.png'
)
Plotter.plot_performance_curves(train_sizes,
train_scores,
test_scores,
image_file_path=image_file_path)
def test_scikit_learn_confusion_matrix_by_svm(self):
# create a pipeline to test
wdbc_pipeline = make_pipeline(
StandardScaler(),
SVC(random_state=42)
)
wdbc_pipeline.fit(self.x_train, self.y_train)
y_pred = wdbc_pipeline.predict(self.x_test)
cm = confusion_matrix(y_true=self.y_test, y_pred=y_pred)
# display confusion matrix
print('Confusion matrix')
print(cm)
# display metrics
print('Precision: {:.3f}'.format(precision_score(y_true=self.y_test, y_pred=y_pred)))
print('Recall: {:.3f}'.format(recall_score(y_true=self.y_test, y_pred=y_pred)))
print('F1: {:.3f}'.format(f1_score(y_true=self.y_test, y_pred=y_pred)))
# The array that was returned after executing the code provides us with information about the different types of
# error the classifier made on the test dataset.
image_file_path = FilesystemUtils.get_test_resources_plot_file_name(
'model_performance/ModelPerformance-ScikitLearn-ConfusionMatrix.png'
)
Plotter.plot_confusion_matrix(cm, image_file_path=image_file_path)
def load_wine_data_set(self):
# Load a non linearly separable dataset
# There are 13 different features in the Wine dataset, describing the chemical properties of the 178 wine
# samples, and each sample belongs to one of three different classes, 1, 2, and 3, which refer to the three
# different types of grape grown in the same region in Italy but derived from different wine cultivars, as
# described in the dataset summary (https://archive. ics.uci.edu/ml/machine-learning-databases/wine/wine.names).
# load wine data set
df = pd.read_csv(
FilesystemUtils.get_resources_data_file_name('wine/wine.data'),
header=None)
# create headers
df.columns = [
'Class label', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash',
'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',
'Proanthocyanins', 'Color intensity', 'Hue',
'OD280/OD315 of diluted wines', 'Proline'
]
# print sample info:
# values of class labels
print('Class labels', np.unique(df['Class label']))
# 10% of the shuffled data set samples with respective class labels (shuffling is necessary as rows are sorted
# according to class label values in increasing order
print(df.sample(frac=0.1).to_string())
# split training and test set
# separate class labels from features
self.x, self.y = df.iloc[:, 1:].values, df.iloc[:, 0].values
# save column names for later usage
self.df_columns = df.columns
def test_variable_L1_regularization_strength(self):
# first, split the entire dataset into the training and testing subsets just for pretending this is a real
# training scenario
X_train, X_test, Y_train, Y_test = train_test_split(self.x,
self.y,
test_size=0.3,
random_state=0,
stratify=self.y)
# standardize data sets
X_train_standardized = StandardScaler().fit_transform(X_train)
weights, params = [], []
for c in np.arange(-4., 6.):
lr = LogisticRegression(penalty='l1', C=10.**c, random_state=0)
lr.fit(X_train_standardized, Y_train)
weights.append(lr.coef_[1])
params.append(10**c)
weights = np.array(weights)
Plotter.plot_variable_feature_weights(
weights,
params,
self.df_columns,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'regularization/Logistic-Regression-Variable-L1-Regularized-Strength.png'
))
def load_iris_dataset(self):
# Loading the Iris dataset from scikit-learn.
# The classes are already converted to integer labels where 0=Iris-Setosa, 1=Iris-Versicolor, 2=Iris-Virginica.
iris = datasets.load_iris()
x = iris.data[:, [2, 3]]
y = iris.target
print('Class labels:', np.unique(y))
# plotter data and save it to file
Plotter.plot_iris_data_set(x, FilesystemUtils.get_test_resources_plot_file_name(
'ScikitLearn-Iris-Training-Set.png'))
# Splitting data into 70% training and 30% test data
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1, stratify=y)
print('Labels counts in y:', np.bincount(y))
print('Labels counts in y_train:', np.bincount(y_train))
print('Labels counts in y_test:', np.bincount(y_test))
# Standardize features
sc = StandardScaler()
sc.fit(x_train)
x_train_std = sc.transform(x_train)
sc.fit(x_test)
x_test_std = sc.transform(x_test)
self.x_train = x_train_std
self.y_train = y_train
self.x_test = x_test_std
self.y_test = y_test
def test_feature_importance(self):
# split training and testing dataset
x_train, x_test, y_train, y_test = train_test_split(self.x,
self.y,
test_size=0.25,
random_state=42)
# standardize features
stdsc = StandardScaler()
x_train_std = stdsc.fit_transform(x_train)
# use random forest
forest = RandomForestClassifier(n_estimators=500, random_state=42)
forest.fit(x_train_std, y_train)
importance = forest.feature_importances_
indices = np.argsort(importance)[::-1]
feature_names = self.df_columns[1:]
for f in range(x_train_std.shape[1]):
print(
"%2d) %-*s %f" %
(f + 1, 30, feature_names[indices[f]], importance[indices[f]]))
Plotter.plot_feature_importance(
x_train_std.shape[1], importance[indices], feature_names[indices],
FilesystemUtils.get_test_resources_plot_file_name(
'feature_importance/FeatureImportance.png'))
def test_adaline_with_stochastic_update(self):
# standardize features
x_std: np.matrix = np.copy(self.x)
x_std[:, 0] = (self.x[:, 0] - self.x[:, 0].mean()) / self.x[:, 0].std()
x_std[:, 1] = (self.x[:, 1] - self.x[:, 1].mean()) / self.x[:, 1].std()
# plotter data and save it to file
Plotter.plot_iris_data_set(
x_std,
FilesystemUtils.get_test_resources_plot_file_name(
'adaline/AdalineSGD-Standardized-Training-Set.png'))
# train adaline on standardized features with a small number of epochs
adaline = AdalineSGD(learning_rate=0.01, num_epochs=15)
adaline.fit(x_std, self.y)
# plot learning curve
curve = {
'cost_length': len(adaline.cost),
'cost': adaline.cost,
'marker': 'o',
'x_label': 'Epochs',
'y_label': 'log(Sum-squared-error)',
'title': 'Adaline - Learning rate 0.01'
}
Plotter.plot_learning_curve(
curve,
FilesystemUtils.get_test_resources_plot_file_name(
'adaline/AdalineSGD-Learning-Curve-Standardized-Features.png'))
# plot decision boundary
Plotter.plot_decision_boundary(
x_std,
self.y,
classifier=adaline,
diagram_options={
'x_label': 'sepal length [cm]',
'y_label': 'petal length [cm]',
'legend': 'upper left'
},
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'adaline/AdalineSGD-Decision-Boundary-Standardized-Features.png'
))
adaline.partial_fit(x_std[0, :], self.y[0])
def test_scikit_learn_perceptron(self):
# Train the perceptron.
# Most algorithms in scikit-learn already support multiclass classification via the One-versus-Rest (OvR) method
perceptron = Perceptron(n_iter=40, eta0=0.1, random_state=1)
perceptron.fit(self.x_train, self.y_train)
self.predict_and_evaluate(perceptron,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'perceptron/Perceptron-ScikitLearn-Decision-Boundary.png'))
def test_scikit_learn_knn(self):
# Train the perceptron.
# Most algorithms in scikit-learn already support multiclass classification via the One-versus-Rest (OvR) method
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(self.x_train, self.y_train)
self.predict_and_evaluate(
knn,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'knn/KNN-ScikitLearn-Decision-Boundary.png'))
def test_scikit_learn_svm(self):
# Train the SVM.
# Most algorithms in scikit-learn already support multiclass classification via the One-versus-Rest (OvR) method
svm = SVC(kernel='linear', C=1.0, random_state=1)
svm.fit(self.x_train, self.y_train)
self.predict_and_evaluate(
svm,
FilesystemUtils.get_test_resources_plot_file_name(
'svm/SVM-ScikitLearn-Decision-Boundary.png'))
def test_scikit_learn_random_forest(self):
forest = RandomForestClassifier(criterion='gini',
n_estimators=25,
random_state=1,
n_jobs=2)
forest.fit(self.x_train, self.y_train)
self.predict_and_evaluate(
forest,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'decision_tree/RandomForest-ScikitLearn-Decision-Boundary.png')
)
def test_scikit_learn_perceptron_by_SGDClassifier(self):
# Train the SVM.
# Most algorithms in scikit-learn already support multiclass classification via the One-versus-Rest (OvR) method
# Sometimes our datasets are too large to fit into computer memory, thus, scikit-learn also offers alternative
# implementations viaThe SGDClassifier class, which also supports online learning via the partial_fit method.
# The concept behind the SGDClassifier class is similar to the stochastic gradient algorithm
perceptron = SGDClassifier(loss='perceptron')
perceptron.fit(self.x_train, self.y_train)
self.predict_and_evaluate(perceptron,
FilesystemUtils.get_test_resources_plot_file_name(
'perceptron/Perceptron-ScikitLearn-Classifier-Decision-Boundary.png'))
def load_svm_nonlinear_data_set(self):
# Load a non linearly separable dataset
np.random.seed(1)
x_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(x_xor[:, 0] > 0, x_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)
# plotter data and save it to file
Plotter.plot_svm_nonlinear_data_set(
x_xor, y_xor,
FilesystemUtils.get_test_resources_plot_file_name(
'svm/SVM-ScikitLearn-NonLinear-Training-Set.png'))
self.x_train = x_xor
self.y_train = y_xor
def test_scikit_learn_svm_nonlinear(self):
# The γ parameter, which we set to gamma=0.1, can be understood as a cut-off parameter for the Gaussian sphere.
# If we increase the value for γ , we increase the influence or reach of the training samples, which leads to a
# tighter and bumpier decision boundary.
svm = SVC(kernel='rbf', random_state=1, gamma=0.10, C=10.0)
svm.fit(self.x_train, self.y_train)
diagram_options = {
'x_label': 'feature 1',
'y_label': 'feature 2',
'legend': 'best'
}
Plotter.plot_decision_boundary(
self.x_train,
self.y_train,
svm,
diagram_options,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'svm/SVM-ScikitLearn-NonLinear-Decision-Boundary.png'))
def test_elbow_method(self):
# To quantify the quality of clustering, we need to use intrinsic metrics—such as the within-cluster
# Sum of Squared Errors (SSE), which is sometimes also called cluster inertia or distortion to compare the
# performance of different k-means clusterings.
# Conveniently, we don't need to compute the within-cluster SSE explicitly when we are using scikit-learn, as it
# is already accessible via the inertia_ attribute after fitting a KMeans model:
# >>> print('Distortion: %.2f' % km.inertia_)
# Distortion: 72.48
# Based on the within-cluster SSE, we can use a graphical tool, the so-called elbow method, to estimate the
# optimal number of clusters k for a given task. Intuitively, we can say that, if k increases, the distortion
# will decrease. This is because the samples will be closer to the centroids they are assigned to.
# The idea behind the elbow method is to identify the value of k where the distortion begins to increase most
# rapidly
distortions = []
k_range = range(1, 11)
for i in k_range:
km = KMeans(
# number of clusters
n_clusters=i,
# initialization method
init='k-means++',
# number of different experiments run
n_init=10,
# maximum number of iteration
max_iter=300,
# change within cluster minimum threshold: below it, the training process is stopped
tol=1e-04,
# initialization seed
random_state=42)
km.fit(self.x)
distortions.append(km.inertia_)
data = np.column_stack((k_range, distortions))
Plotter.plot_data(
data,
x_label='Number of clusters',
y_label='Distorsion',
title='Elbow method for optimal number of clusters',
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'clustering/ElbowMethodForKMeansClustering.png'))
def test_scikit_learn_roc_auc_on_wdbc_pipeline(self):
# create a pipeline to test
wdbc_pipeline = make_pipeline(
StandardScaler(),
PCA(n_components=2),
LogisticRegression(solver='lbfgs', penalty='l2', C=100.0, random_state=42)
)
# create a stratified n_splits-fold cross-validation
# In this example, we use 3 folds on the training test, actually
cv = StratifiedKFold(n_splits=3, random_state=42).split(self.x_train, self.y_train)
false_positive_rates = []
true_positive_rates = []
roc_auc_values = []
# let's use just 10 features to have a more interesting diagram
x_train_reduced = self.x_train[:, [4, 14]]
# iterate over the folds to draw the related ROC curve
for i, (train, test) in enumerate(cv):
# compute the probabilities predicted by the classifier using the current fold
probs = wdbc_pipeline.fit(x_train_reduced[train], self.y_train[train]).predict_proba(x_train_reduced[test])
# computer ROC curve arrays
fpr, tpr, thresholds = roc_curve(self.y_train[test], probs[:, 1], pos_label=1)
# compute AUC (Area Under Curve)
roc_auc = auc(fpr, tpr)
false_positive_rates.append(fpr)
true_positive_rates.append(tpr)
roc_auc_values.append(roc_auc)
# display results
image_file_path = FilesystemUtils.get_test_resources_plot_file_name(
'model_performance/ModelPerformance-ScikitLearn-ROC_AUC.png'
)
Plotter.plot_roc_auc(
np.asarray(false_positive_rates), np.asarray(true_positive_rates), np.asarray(roc_auc_values),
image_file_path=image_file_path
)
def test_sequential_feature_selection(self):
knn = KNeighborsClassifier(n_neighbors=5)
# standardize features
stdsc = StandardScaler()
x_std = stdsc.fit_transform(self.x)
# selecting features
sbs = SequentialFeatureSelection(knn, selected_features_number=1)
sbs.fit(x_std, self.y)
Plotter.plot_accuracy_by_feature_number(
sbs.subsets_, sbs.scores_,
FilesystemUtils.get_test_resources_plot_file_name(
'sequential_feature_selection/AccuracyByFeatureNumber.png'))
# for each subset of features, print column names, so to see how the elimination process worked
print('Progressive selection explained')
for feature_subset in sbs.subsets_:
indices = list(feature_subset)
print(self.df_columns[1:][indices])
def plot_predictions(self, predictions: np.matrix, centroids: np.matrix,
title: str, diagram_file_name: str):
data = [{
'x': self.x[predictions == 0, :],
'color': 'lightgreen',
'marker': 's',
'marker_size': 50,
'edge_color': 'black',
'label': 'cluster 1'
}, {
'x': self.x[predictions == 1, :],
'color': 'orange',
'marker': 's',
'marker_size': 50,
'edge_color': 'black',
'label': 'cluster 2'
}, {
'x': self.x[predictions == 2, :],
'color': 'lightblue',
'marker': 's',
'marker_size': 50,
'edge_color': 'black',
'label': 'cluster 3'
}]
centroids = {
'x': centroids,
'color': 'red',
'marker': '*',
'marker_size': 250,
'edge_color': 'black',
'label': 'centroids'
}
Plotter.plot_multiple_scattered_data(
data,
centroids,
title,
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'clustering/' + diagram_file_name))
def setUp(self):
# load subset of Iris data
iris = datasets.load_iris()
x_train = iris.data[:, [2, 3]]
y_train = iris.target
# consider only 0 and 1 labels
x_train_01_subset = x_train[(y_train == 0) | (y_train == 1)]
y_train_01_subset = y_train[(y_train == 0) | (y_train == 1)]
# Standardize features
sc = StandardScaler()
sc.fit(x_train_01_subset)
self.x = sc.transform(x_train_01_subset)
self.y = y_train_01_subset
print('Class labels:', np.unique(self.y))
# plotter data and save it to file
Plotter.plot_iris_data_set(
self.x,
FilesystemUtils.get_test_resources_plot_file_name(
'logistic_regression/LogisticRegressionBGD-Training-Set.png'))
def test_plot_sample_clustered_data(self):
Plotter.plot_scattered_data(
self.x,
title='Sample clustered data',
image_file_path=FilesystemUtils.get_test_resources_plot_file_name(
'clustering/SampleClusteredData.png'))
