# Separate features and output + scale data
# pandas has some weird column counting
scaler = StandardScaler()
multiBinarizer = MultiLabelBinarizer()
trainData = scaler.fit_transform(processedData[:, 0:2])
# Convert output to binarized array
numbers = np.reshape((processedData[:, 2]), (len(processedData[:, 2]), 1))
predOutput = multiBinarizer.fit_transform(numbers)
X_train, X_test, y_train, y_test = train_test_split(trainData, predOutput, test_size=TEST_SIZE_SAMPLE,
random_state=RANDOM_STATE_SPLIT)
# Try to load classifier from file
clf = input_output.load_classifier("sanfCrimeCLF.pkl")
if not clf:
# If no file is present, train the classifier using the best known parameters and save the classifier
print("There is no saved classifier!")
print("Training Model...")
clf = KNeighborsClassifier(n_neighbors=26, weights='distance', algorithm='kd_tree')
clf.fit(X_train, y_train)
input_output.save_classifier("sanfCrimeCLF.pkl", clf)
print("Training Complete!")
# # Choose scoring metric
scores = ['f1']
# print(X_train)
# Find optimal model
# Define constants
TEST_SIZE_SAMPLE = 0.25
RANDOM_STATE_SPLIT = 40
rawData = input_output.load_data("train.csv")
processedData = preprocess.prepare_data(rawData)
# Separate features and output + scale data
trainData = (processedData[:, 1:])
predOutput = processedData[:, 0]
X_train, X_test, y_train, y_test = train_test_split(trainData, predOutput, test_size=TEST_SIZE_SAMPLE,
random_state=RANDOM_STATE_SPLIT)
# Try to load classifier from file
clf = input_output.load_classifier("titanicCLF.pkl")
if not clf:
# If no file is present, train the classifier using the best known parameters and save the classifier
print("There is no saved classifier!")
print("Training Model...")
clf = svm.SVC(C=1, kernel="rbf", gamma=0.1)
clf.fit(X_train, y_train)
input_output.save_classifier("titanicCLF.pkl", clf)
print("Training Complete!")
# Choose scoring metrics
scores = ['precision', 'recall']
# # SEARCH OPTIMAL PARAMETERS FOR SVM
# svmModel = models.SVMModel(scores, X_train, y_train, y_test, X_test)
# param_grid = svmModel.grid_search_setup()