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sklearn_training_snippets.py
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sklearn_training_snippets.py
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# encoding: utf-8
"""Sklearn snippets for machine learning trainings.
Methods:
" >>> train_report(data, split_test, k_fold, ml_list, file_path, header, classes)
" >>> _training_results(data_dict, split_test, k_fold, list)
" >>> _process_training(data_dict, result_dict, model, split_test, k_fold)
" >>> _log_results(file_path, header, final_results, split_test, training_len, testing_len)
"""
import numpy as np
import codecs
import datetime
import time
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.svm import LinearSVR
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
# Default parameters for: SVM and KNN
C = 1.0 # SVM regularization parameter
num_neighbors = 5
def train_report(data, split_test, k_fold, ml_list, file_path, header, classes):
"""Execute supervised training for list of algorithms and report the output.
Options:
- split_test: decimal number percentages (recommended 0.1 to 0.3)
- k_fold: integer number (recommended 5 or 10)
- ml_list: algorithms options
['logistic_regression', 'decision_tree', 'svm_svc_linear',
'svm_svc_rbf', 'svm_linear_svr ,'multinomial_nb',
'random-forest', 'kneighbors', 'stochastic-gradient-descent-log',
'stochastic-gradient-descent-svm']
- collection_label_list: collections labels for each theme from db_collection_list
[0, 1] # 0: database, 1: computer network (same quantity from db_collection_list)
:param data:
:param split_test:
:param k_fold:
:param ml_list:
:param file_path:
:param header:
:param classes:
:return:
"""
# Shuffle data and create train/test splits
np.random.shuffle(data)
# X as data matrix except true label column; Y as true label column
X = data[:, :-1]
Y = data[:, -1]
# Get length for testing and training
if split_test is None:
testing_len = 0
training_len = 0
else:
testing_len = int(round(len(data) * split_test))
training_len = int(round(len(data) - testing_len))
# Input data
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=split_test, random_state=0)
data_dict = {
'X': X,
'Y': Y,
'Xtrain': Xtrain,
'Xtest': Xtest,
'Ytrain': Ytrain,
'Ytest': Ytest,
'data': data,
'classes': classes,
'training_len': training_len,
'testing_len': testing_len
}
# Execute training and get results
final_results = _training_results(data_dict, split_test, k_fold, ml_list)
# Output the results in log JSON files
_log_results(file_path, header, final_results, split_test, training_len, testing_len)
def _training_results(data_dict, split_test, k_fold, training_type_list):
"""Execute supervised training for each training algorithm type.
Options:
- split_test: decimal number percentages (recommended 0.1 to 0.3)
- k_fold: integer number (recommended 5 or 10)
- training_type_list: algorithms options
['logistic_regression', 'decision_tree', 'svm_svc_linear',
'svm_svc_rbf', 'svm_linear_svr ,'multinomial_nb',
'random-forest', 'kneighbors', 'stochastic-gradient-descent-log',
'stochastic-gradient-descent-svm']
- [OUTPUT] final_results: e.g.
- {'training_test': [{'name': 'Logistic Regression', 'accuracy': 0.9531331',
'classification_report': '
precision recall f1-score support
0.0 0.95 0.95 0.95 4449
1.0 0.95 0.95 0.95 4449
avg / total 0.95 0.95 0.95 8898',
'confusion_matrix': '
[[4233 216]
[ 229 4220]]'
}],
{'cross_validation': [{'name': 'Logistic Regression', 'accuracy': 0.9531331', ...}]}
:param data_dict:
:param split_test:
:param k_fold:
:param training_type_list:
:return [object] final results for each methodology:
"""
final_results = {
'training_test': [],
'cross_validation': []
}
for training_type in training_type_list:
result_dict = {
'name': '',
'accuracy': None,
'classification_report': None,
'confusion_matrix': None
}
training_results = []
if training_type is not None:
if training_type == 'logistic_regression':
result_dict['name'] = 'Logistic Regression'
model = LogisticRegression()
elif training_type == 'decision_tree':
result_dict['name'] = 'Decision Tree'
model = DecisionTreeClassifier()
elif training_type == 'svm_svc_linear':
result_dict['name'] = 'SVM SVC Linear'
model = SVC(kernel='linear', C=C, verbose=True)
elif training_type == 'svm_svc_rbf':
result_dict['name'] = 'SVM SVC RBF'
model = SVC(kernel='rbf', C=C, verbose=True)
elif training_type == 'svm_linear_svr':
result_dict['name'] = 'SVM Linear SVR'
model = LinearSVR(C=C, verbose=True)
elif training_type == 'multinomial_nb':
result_dict['name'] = 'Multinomial Naive Bayes'
model = MultinomialNB()
elif training_type == 'random-forest':
result_dict['name'] = 'Random Forest'
model = RandomForestClassifier()
elif training_type == 'kneighbors':
result_dict['name'] = 'KNN'
model = KNeighborsClassifier(n_neighbors=num_neighbors)
elif training_type == 'stochastic-gradient-descent-log':
result_dict['name'] = 'Stochastic Gradient Descent - Logistic Regression'
model = SGDClassifier(loss='log')
elif training_type == 'stochastic-gradient-descent-svm':
result_dict['name'] = 'Stochastic Gradient Descent - Linear SVM'
model = SGDClassifier(loss='hinge')
training_results = _process_training(data_dict, result_dict, model, split_test, k_fold)
else:
print 'ML not implemented for ' + training_type
if training_results['training_test'] is not None:
final_results['training_test'].append(training_results['training_test'])
elif training_results['cross_validation'] is not None:
final_results['cross_validation'].append(training_results['cross_validation'])
return final_results
def _process_training(data_dict, result_dict, model, split_test, k_fold):
"""Execute training with Sklearn and return the results.
Options:
- result_dict: object to store the output results
{'name': 'Logistic Regression', 'accuracy': None, 'classification_report': None, 'confusion_matrix': None}
- model: sklearn object with specific supervised training bootstraped model
- split_test: decimal number percentages (recommended 0.1 to 0.3)
- k_fold: integer number (recommended 5 or 10)
:param data_dict:
:param result_dict:
:param model:
:param split_test:
:param k_fold:
:return [object] final_results
e.g. {'training_test': result_dict, 'cross_validation': result_dict, 'time': 130103}:
"""
training_results = {
'training_test': None,
'cross_validation': None,
'time': None
}
# Random forest algorithm has a different type of data argument
if result_dict['name'] == 'random-forest':
X = data_dict['data']
Y = data_dict['classes']
Xtrain = X[:-data_dict['testing_len'], ]
Ytrain = Y[:-data_dict['testing_len'], ]
Xtest = X[-data_dict['testing_len']:, ]
Ytest = Y[-data_dict['testing_len']:, ]
else:
X = data_dict['X']
Y = data_dict['Y']
Xtrain = data_dict['Xtrain']
Ytrain = data_dict['Ytrain']
Xtest = data_dict['Xtest']
Ytest = data_dict['Ytest']
if split_test is not None:
print "--- Split test training for " + result_dict['name'] + " starting... ---"
start = time.time()
model.fit(Xtrain, Ytrain)
result_dict['accuracy'] = model.score(Xtest, Ytest)
Ypred = model.predict(Xtest)
result_dict['classification_report'] = classification_report(Ytest, Ypred)
result_dict['confusion_matrix'] = confusion_matrix(Ytest, Ypred)
end = time.time()
result_dict['time'] = (end - start)
training_results['training_test'] = result_dict
print "--- Split test training ended for " + result_dict['name'] + " in " + str(result_dict['time']) + " ---"
if k_fold is not None:
print "--- k-fold test training for " + result_dict['name'] + " starting... ---"
start = time.time()
result_dict['accuracy'] = cross_val_score(model, X, Y, cv=k_fold).mean()
Ypred = cross_val_predict(model, X=X, y=Y, verbose=1, cv=k_fold)
result_dict['classification_report'] = classification_report(Y, Ypred)
result_dict['confusion_matrix'] = confusion_matrix(Y, Ypred)
end = time.time()
result_dict['time'] = (end - start)
training_results['cross_validation'] = result_dict
print "--- k-fold test training ended for " + result_dict['name'] + " in " + str(result_dict['time']) + " ---"
return training_results
def _log_results(file_path, header, final_results, split_test, training_len, testing_len):
"""Output the training results in a file path log JSON file.
@ref reports/*.json
:param file_path:
:param header:
:param final_results:
:param split_test:
:param training_len:
:param testing_len:
:return:
"""
# Report output
sout = header
if len(final_results['training_test']) > 0:
sout += "\n--------------------------------------\n"
sout += "Normal training/test mode\n"
sout += "--------------------------------------\n"
sout = sout + "Training mode: " + str(100 - split_test * 100) + "/" + str(split_test * 100) + "\n"
sout = sout + "Training amount: " + str(training_len) + "\n"
sout = sout + "Testing amount: " + str(testing_len) + "\n\n"
sout += "--------------------------------------\n"
for item_dict in final_results['training_test']:
sout = sout + "Results for " + item_dict['name'] + " algorithm in normal training/test mode\n"
sout += "--------------------------------------\n"
sout = sout + "Main Classification Rate: " + str(item_dict['accuracy']) + "\n"
sout += "Metrics:\n\n"
sout += item_dict['classification_report']
sout += "\n\nConfusion Matrix:\n"
sout += np.array_str(item_dict['confusion_matrix'])
sout = sout + "\n\nTime: " + str(item_dict['time']) + "\n"
sout += "\n--------------------------------------\n"
if len(final_results['cross_validation']) > 0:
sout += "\n--------------------------------------\n"
sout += "Cross-validation mode with k-folds\n"
sout += "--------------------------------------\n"
for item_dict in final_results['cross_validation']:
sout = sout + "Results for " + item_dict['name'] + " algorithm in cross-validation mode\n"
sout += "--------------------------------------\n"
sout = sout + "Main Classification Rate: " + str(item_dict['accuracy']) + "\n"
sout += "Metrics:\n\n"
sout += item_dict['classification_report']
sout += "\n\nConfusion Matrix:\n"
sout += np.array_str(item_dict['confusion_matrix'])
sout = sout + "\n\nTime: " + str(item_dict['time']) + "\n"
sout += "\n--------------------------------------\n"
fname = file_path + datetime.datetime.now().strftime("%d-%m-%Y_%H:%M:%S") + ".txt"
fout = codecs.open(fname, 'w+', 'utf8')
fout.write(sout) # Stored on disk as UTF-8
fout.close()