C svm implementation in python

# importing scikit learn with make_blobs
from sklearn.datasets.samples_generator import make_blobs
  
# creating datasets X containing n_samples
# Y containing two classes
X, Y = make_blobs(n_samples=500, centers=2,
                  random_state=0, cluster_std=0.40)
import matplotlib.pyplot as plt
# plotting scatters 
plt.scatter(X[:, 0], X[:, 1], c=Y, s=50, cmap='spring');
plt.show()# Method 1
from sklearn.svm import LinearSVC
# OR from sklearn.svm import SVC 
# instatiate a scikit-learn SVM model
# to indicate the class imbalance at fit time, set class_weight='balanced'
# for reproducible output across multiple function calls, set random_state to a given integer value
svm = LinearSVC(class_weight='balanced', random_state=42, loss="hinge", fit_intercept=False) 
# svm = SVC(kernel='linear', gamma=.5, probability=True)  # Another way
# train a linear Support Vector Machine model using Scikit-Learn
t0 = time.time()
svm.fit(X_train, y_train)
sklearn_time = time.time() - t0

# Method 2 : Use snapml library
# in contrast to scikit-learn's LinearSVC, Snap ML offers multi-threaded CPU/GPU training of SVMs
from snapml import SupportVectorMachine
snapml_svm_gpu = SupportVectorMachine(class_weight='balanced', random_state=42, use_gpu=True, fit_intercept=False)
snapml_svm_cpu = SupportVectorMachine(class_weight='balanced', random_state=42, n_jobs=4, fit_intercept=False)
t0 = time.time()
model = snapml_svm_cpu.fit(X_train, y_train)
snapml_time = time.time() - t0

# Predict
y_pred = svm.predict(X_test)

# Evaluate model
roc_auc_score(y_test, y_pred)

# Get confidence score for probability
y_pred_conf = svm.decision_function(X_test)

# Evaluate hinge loss
hinge_loss(y_test, y_pred_conf)
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