class Classifier(object):
"""This is a classifier for codon reassignment"""
def __init__(self, method, classifier_spec={}, scale=False, n_estimators=1000):
if method == 'rf':
self.clf = RandomForestClassifier(
n_estimators=n_estimators, n_jobs=-1, max_leaf_nodes=1000, **classifier_spec)
elif method == 'svc':
self.clf = svm.SVC(probability=True, **classifier_spec)
elif method == 'etc':
self.clf = ExtraTreesClassifier(
n_estimators=n_estimators, **classifier_spec)
elif method == 'gnb':
self.clf = GaussianNB()
else:
raise NotImplementedError(
"The method you chose (%s) is not implemented" % method)
self.method = method
self.trained = False
self.scale = scale
@classmethod
def load_from_file(clc, loadfile):
"""Load model from a file"""
try:
clf = joblib.load(loadfile)
return clf
except IOError:
print('Problem with file %s, can not open it' % loadfile)
except Exception as e:
raise e
return None
def save_model(self, outfile):
"""Save model to a file"""
joblib.dump(self, outfile)
def train(self, X=None, Y=None):
"""Train the model"""
if self.scale:
X = preprocessing.scale(X)
self.clf.fit(X, Y)
self.X = X
self.y = Y
self.trained = True
@classmethod
def from_classifier(clc, clfier):
newclf = clc(clfier.method, {}, clfier.scale)
newclf.__dict__.update(clfier.__dict__)
return newclf
def get_score(self, X, Y):
"""Return score for classification on X"""
if self.scale:
X = preprocessing.scale(X)
return self.clf.score(X, Y)
def predict(self, X):
"""Predict values for X"""
if not self.trained:
raise ValueError("Classifier is not trained")
if self.scale:
X = preprocessing.scale(X)
return self.clf.predict(X)
def predict_proba(self, X):
"""Return probability for each class prediction"""
return self.clf.predict_proba(X)
def feature_importance(self, outfile="importance.png", features_list=[]):
"""Show each feature importance"""
if (self.method in ['rf', 'etc']):
importances = self.clf.feature_importances_
if len(features_list) > 0 and len(features_list) != len(importances):
raise ValueError("Number of features does not fit!")
indices = np.argsort(importances)[::-1]
n_feats = len(features_list)
np.savetxt(outfile + ".txt", np.array([tree.feature_importances_
for tree in self.clf.estimators_]), delimiter=',', fmt='%1.3e')
std = np.std(
[tree.feature_importances_ for tree in self.clf.estimators_], axis=0)
plt.figure()
plt.title("Feature importances")
plt.bar(range(n_feats), importances[
indices], width=0.5, color="b", yerr=std[indices], align="center")
if len(features_list) > 0:
features_list = np.asarray(features_list)[indices]
plt.xticks(range(n_feats), features_list, rotation='vertical')
plt.xlim([-1, n_feats])
plt.margins(0.2)
plt.subplots_adjust(bottom=0.15)
plt.savefig(outfile, bbox_inches='tight')
else:
raise NotImplementedError(
"Not supported for classifier other than Ensembl Tree")
def cross_validation(self, X, Y, X_test=None, Y_test=None, tsize=0.3):
"""Cross validation on X and Y, using a sub sample"""
if X_test is None or Y_test is None:
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=tsize)
else:
X_train = X
Y_train = Y
self.train(X_train, Y_train)
Y_predicted = self.predict(X_test)
self.get_stat(X_test, Y_test)
return Y_predicted, self.get_score(X_test, Y_test)
def plot_precision_recall(self, X_test, y_test, infos="", outfile="precision_recall.png"):
"""plot precicion-recall curve"""
if self.trained:
try:
y_score = self.clf.decision_function(X_test)
except:
y_score = self.clf.predict_proba(X_test)[:, 1]
precision, recall, _ = precision_recall_curve(y_test, y_score)
average_precision = average_precision_score(
y_test, y_score, average="micro")
# Plot Precision-Recall curve for each class
plt.clf()
plt.plot(recall, precision,
label='Average Precision-recall curve (area = {0:0.2f})'
''.format(average_precision))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall curve for %s (%s)' %
(self.method, infos))
plt.legend(loc="lower right")
plt.savefig(outfile)
else:
raise ValueError("Classifier is not trained")
def get_stat(self, X_test, y_test):
"""Print list of score for the current classifier"""
y_pred = self.predict(X_test)
if hasattr(self.clf, "predict_proba"):
prob_pos = self.clf.predict_proba(X_test)[:, 1]
else: # use decision function
prob_pos = self.clf.decision_function(X_test)
prob_pos = (prob_pos - prob_pos.min()) / \
(prob_pos.max() - prob_pos.min())
clf_score = brier_score_loss(y_test, prob_pos)
print("%s:" % self.method)
print("\tBrier: %1.3f" % (clf_score))
print("\tPrecision: %1.3f" % precision_score(y_test, y_pred))
print("\tRecall: %1.3f" % recall_score(y_test, y_pred))
print("\tF1: %1.3f" % f1_score(y_test, y_pred))
print("\tROC AUC score: %1.3f\n" % roc_auc_score(y_test, prob_pos))
clf.fit(X, y)
SVC(C=1.0,
cache_size=200,
class_weight=None,
coef0=0.0,
degree=3,
gamma='auto',
kernel='linear',
max_iter=-1,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
print(clf.predict([[-0.8, -1]]))
print(clf.decision_function([[-0.8, -1]]))
print(clf.get_params())
print(clf.score([[-0.8, -1]], [1]))
print(clf.score([[-0.8, -1]], [2]))
########################################################################################################################
########################################################################################################################
from sklearn.svm import LinearSVC
from sklearn.datasets import make_blobs
import mglearn
X, y = make_blobs(random_state=42)
linear_svm = LinearSVC().fit(X, y)
mglearn.discrete_scatter(X[:, 0], X[:, 1], y)
plt.xlabel("Feature 0")
learning_rate_init=1e-3,
learning_rate='adaptive',
tol=1e-4,
max_iter=200)
elif i == 4:
clf = LinearSVC(penalty='l2', random_state=0, tol=1e-4)
skf_accuracy1 = []
skf_accuracy2 = []
for train, test in skf.split(X, y):
clf.fit(X[train], y[train])
if n_classes.size < 3:
skf_accuracy1.append(
roc_auc_score(y[test],
clf.predict_proba(X[test])[:,
1] if i != 4
else clf.decision_function(X[test]),
average='micro'))
clf.fit(X_new[train], y[train])
skf_accuracy2.append(
roc_auc_score(
y[test],
clf.predict_proba(X_new[test])[:, 1]
if i != 4 else clf.decision_function(X_new[test]),
average='micro'))
else:
ytest_one_hot = label_binarize(y[test], n_classes)
skf_accuracy1.append(
roc_auc_score(ytest_one_hot,
clf.predict_proba(X[test]) if i != 4 else
clf.decision_function(X[test]),
average='micro'))
svc_prediction = clf.predict(NMF1_test)
svc_score = clf.predict(NMF1_test)
evaluate(test_label, svc_score)
##min_df=5
clf = GaussianNB().fit(NMF2_train, train_label)
svc_prediction = clf.predict(NMF2_test)
svc_score = clf.predict(NMF2_test)
evaluate(test_label, svc_score)
## (h)
print('(h)------------------------------------------')
print('Logistic Regression with min_df=2: \n')
clf = linear_model.LogisticRegression().fit(LSI1_train, train_label)
svc_prediction = clf.predict(LSI1_test)
svc_score = clf.decision_function(LSI1_test)
evaluate(test_label, svc_score)
print('Logistic Regression with min_df=5: \n')
clf = linear_model.LogisticRegression().fit(LSI2_train, train_label)
svc_prediction = clf.predict(LSI2_test)
svc_score = clf.decision_function(LSI2_test)
evaluate(test_label, svc_score)
## (i)
cross_list = []
for c in [0.001, 0.01, 0.1, 1, 10, 100, 1000]:
linear_svc = linear_model.LogisticRegression(penalty='l1', C=c).fit(
LSI1_train, train_label)
linear_svc.fit(LSI1_train, train_label)
svc_prediction = linear_svc.predict(LSI1_test)
class Learning(object):
"""
usage:
>> from feelit.features import Learning
>> learner = Learning(verbose=args.verbose, debug=args.debug)
>> learner.set(X_train, y_train, feature_name)
>>
>> scores = {}
>> for C in Cs:
>> for gamma in gammas:
>> score = learner.kFold(kfolder, classifier='SVM',
>> kernel='rbf', prob=False,
>> C=c, scaling=True, gamma=gamma)
>> scores.update({(c, gamma): score})
>>
>> best_C, best_gamma = max(scores.iteritems(), key=operator.itemgetter(1))[0]
>> learner.train(classifier='SVM', kernel='rbf', prob=True, C=best_C, gamma=best_gamma,
>> scaling=True, random_state=np.random.RandomState(0))
>> results = learner.predict(X_test, yb_test, weighted_score=True, X_predict_prob=True, auc=True)
"""
def __init__(self, X=None, y=None, **kwargs):
loglevel = logging.ERROR if 'loglevel' not in kwargs else kwargs['loglevel']
logging.basicConfig(format='[%(levelname)s][%(name)s] %(message)s', level=loglevel)
self.logger = logging.getLogger(__name__+'.'+self.__class__.__name__)
self.X = X
self.y = y
self.kfold_results = []
self.Xs = {}
self.ys = {}
self.scaling = False if 'scaling' not in kwargs else kwargs['scaling']
def set(self, X, y, feature_name):
self.X = X
self.y = y
self.feature_name = feature_name
def train(self, **kwargs):
self._train(self.X, self.y, **kwargs)
def _train(self, X_train, y_train, **kwargs):
"""
required:
X_train, y_train
options:
classifier: 'SVM', 'SGD', 'GaussianNB'
with_mean: True/False
with_std: True/False
scaling: True/False
prob: True/False. Esimate probability during training
random_state: seed, RandomState instance or None; for probability estimation
kernel: 'rbf', ...
C: float; svm parameters
shuffle: True/False; for SGD
"""
## setup a classifier
classifier = "SVM" if "classifier" not in kwargs else kwargs["classifier"]
# ## slice
# delete = None if "delete" not in kwargs else kwargs["delete"]
# if delete:
# X_train = np.delete(utils.toDense(self.X), delete, axis=0)
# y_train = np.delete(self.y, delete, axis=0)
# else:
self.logger.debug("%d samples x %d features in X_train" % ( X_train.shape[0], X_train.shape[1] ))
self.logger.debug("%d samples in y_train" % ( y_train.shape[0] ))
with_mean = True if 'with_mean' not in kwargs else kwargs['with_mean']
with_std = True if 'with_std' not in kwargs else kwargs['with_std']
# Cannot center sparse matrices, `with_mean` should be set as `False`
# Douglas: this doesn't make sense
#if utils.isSparse(self.X):
# with_mean = False
self.scaling = False if 'scaling' not in kwargs else kwargs['scaling']
if self.scaling:
self.scaler = StandardScaler(with_mean=with_mean, with_std=with_std)
## apply scaling on X
self.logger.debug("applying a standard scaling with_mean=%d, with_std=%d" % (with_mean, with_std))
X_train = self.scaler.fit_transform(X_train)
## determine whether using predict or predict_proba
self.prob = False if 'prob' not in kwargs else kwargs["prob"]
random_state = None if 'random_state' not in kwargs else kwargs["random_state"]
if classifier == "SVM":
## setup a svm classifier
kernel = "rbf" if 'kernel' not in kwargs else kwargs["kernel"]
## cost: default 1
C = 1.0 if "C" not in kwargs else kwargs["C"]
## gamma: default (1/num_features)
num_features = X_train.shape[1]
gamma = (1.0/num_features) if "gamma" not in kwargs else kwargs["gamma"]
#self.clf = svm.SVC(C=C, gamma=gamma, kernel=kernel, probability=self.prob, random_state=random_state, class_weight='auto')
self.clf = svm.SVC(C=C, gamma=gamma, kernel=kernel, probability=self.prob, random_state=random_state)
self.params = "%s_%s C=%f gamma=%f probability=%d" % (classifier, kernel, C, gamma, self.prob)
elif classifier == "SGD":
shuffle = True if 'shuffle' not in kwargs else kwargs['shuffle']
if self.prob:
self.clf = SGDClassifier(loss="log", shuffle=shuffle)
else:
self.clf = SGDClassifier(shuffle=shuffle)
self.params = "%s_%s" % (classifier, 'linear')
elif classifier == "GaussianNB":
self.clf = GaussianNB()
self.params = "%s_%s" % (classifier, 'NB')
else:
raise Exception("currently only support SVM, SGD and GaussianNB classifiers")
self.logger.debug(self.params)
self.clf.fit(X_train, y_train)
def dump_model(self, file_name):
try:
pickle.dump(self.clf, open(file_name, "w"))
except ValueError:
self.logger.error("failed to dump %s" % (file_name))
def dump_scaler(self, file_name):
try:
if self.scaling:
pickle.dump(self.scaler, open(file_name, "w"))
else:
self.logger.warning("scaler doesn't exist")
except ValueError:
self.logger.error("failed to dump %s" % (file_name))
def load_model(self, file_name):
try:
self.clf = pickle.load( open(file_name, "r"))
except ValueError:
self.logger.error("failed to load %s" % (file_name))
def load_scaler(self, file_name):
try:
self.scaler = pickle.load( open(file_name, "r"))
if self.scaler:
self.scaling = True
except ValueError:
self.logger.error("failed to load %s" % (file_name))
def predict(self, X_test, y_test, **kwargs):
'''
return dictionary of results
'''
if self.scaling:
X_test = self.scaler.transform(X_test)
self.logger.info('y_test = %s', str(y_test.shape))
y_predict = self.clf.predict(X_test)
X_predict_prob = self.clf.predict_proba(X_test) if self.clf.probability else 0
results = {}
if 'score' in kwargs and kwargs['score'] == True:
results.update({'score': self.clf.score(X_test, y_test.tolist())})
self.logger.info('score = %f', results['score'])
if 'weighted_score' in kwargs and kwargs['weighted_score'] == True:
results.update({'weighted_score': self._weighted_score(y_test.tolist(), y_predict)})
self.logger.info('weighted_score = %f', results['weighted_score'])
if 'y_predict' in kwargs and kwargs['y_predict'] == True:
results.update({'y_predict': y_predict})
self.logger.info('y_predict = %f', results['y_predict'])
if 'X_predict_prob' in kwargs and kwargs['X_predict_prob'] == True:
results.update({'X_predict_prob': X_predict_prob[:, 1]})
self.logger.info('X_predict_prob = %s', str(results['X_predict_prob']))
if 'auc' in kwargs and kwargs['auc'] == True:
fpr, tpr, thresholds = roc_curve(y_test, X_predict_prob[:, 1])
results.update({'auc': auc(fpr, tpr)})
self.logger.info('auc = %f', results['auc'])
if 'decision_value' in kwargs and kwargs['decision_value'] == True:
results.update({'decision_value': self.clf.decision_function(X_test)})
self.logger.debug('decision_value = %s', str(results['decision_value']))
return results
def _weighted_score(self, y_test, y_predict):
# calc weighted score
n_pos = len([val for val in y_test if val == 1])
n_neg = len([val for val in y_test if val == -1])
temp_min = min(n_pos, n_neg)
weight_pos = 1.0/(n_pos/temp_min)
weight_neg = 1.0/(n_neg/temp_min)
correct_predict = [i for i, j in zip(y_test, y_predict) if i == j]
weighted_sum = 0.0
for answer in correct_predict:
weighted_sum += weight_pos if answer == 1 else weight_neg
wscore = weighted_sum / (n_pos * weight_pos + n_neg * weight_neg)
return wscore
def kfold(self, kfolder, **kwargs):
"""
return:
mean score for kfold training
required:
kfolder: generated by sklearn.cross_validatio.KFold
options:
same as _train
"""
#amend = False if "amend" not in kwargs else kwargs["amend"]
#if amend:
## amend dense matrix: replace NaN and None with float values
# self.check_and_amend()
#else:
# self.logger.debug("skip the amending process")
sum_score = 0.0
for (i, (train_index, test_index)) in enumerate(kfolder):
self.logger.info("cross-validation fold %d: train=%d, test=%d" % (i, len(train_index), len(test_index)))
X_train, X_test, y_train, y_test = self.X[train_index], self.X[test_index], self.y[train_index], self.y[test_index]
self._train(X_train, y_train, **kwargs)
score = self.predict(X_test, y_test, score=True)['score']
self.logger.info('score = %.5f' % (score))
sum_score += score
mean_score = sum_score/len(kfolder)
self.logger.info('*** C = %f, mean_score = %f' % (kwargs['C'], mean_score))
return mean_score
class SoundClassifier:
def __init__(self, algorithm):
if algorithm == 'knn':
self._classifier = KNeighborsClassifier(n_neighbors=6)
elif algorithm == 'linear':
self._classifier = LogisticRegression(
) # alternative => (C=100) / (C=0.01)
elif algorithm == 'linearMulti':
self._classifier = LinearSVC()
elif algorithm == 'sgd':
self._classifier = SGDClassifier(random_state=0)
elif algorithm == 'decisionTree':
self._classifier = DecisionTreeClassifier(
random_state=0) # alternative => max_depth=4
elif algorithm == 'randomForest':
self._classifier = RandomForestClassifier(n_estimators=10,
max_features=1300,
max_depth=8,
random_state=0)
elif algorithm == 'gradientBoosting':
self._classifier = GradientBoostingClassifier(
random_state=0
) # alternative => max_depth=1, learning_rate=0.01
elif algorithm == 'svm':
self._classifier = SVC(
C=1.3, kernel='rbf', gamma='scale'
) # alternative => C=1000, gamma=1000. Also pre-process data
elif algorithm == 'neuralNetworks':
self._classifier = MLPClassifier(
random_state=0
) # alternative => max_iter=1000, alpha=1. Also pre-process data
elif algorithm == 'gmm':
self._classifier = GaussianProcessClassifier(
kernel=RationalQuadratic(alpha=1, length_scale=1),
random_state=0)
elif algorithm == 'gnb':
self._classifier = GaussianNB()
else:
print('Algorithm not found')
def train_classifier(self, X_train, y_train):
self._classifier.fit(X_train, y_train)
def get_predictions(self, X_test):
return self._classifier.predict(X_test)
def get_accuracy(self, X_test, y_test):
return self._classifier.score(X_test, y_test)
def show_feature_importance(self, data, target):
"""Only works with algorithms that have feature_importances_ attribute"""
plt.plot(self._classifier.feature_importances_, 'o')
plt.xticks(range(data.shape[1]), target, rotation=90)
plt.show()
def print_decision_function(self, X_test):
"""Only works with algorithms that have decision_function method"""
print(self._classifier.decision_function(X_test))
# We can recover the predictions by computing the argmax
print(np.argmax(self._classifier.decision_function(X_test), axis=1))
print(self._classifier.predict(X_test))
def print_prediction_probability(self, X_test):
"""Only works with algorithms that have predict_proba method"""
print(self._classifier.predict_proba(X_test))
# We can recover the predictions by computing the argmax
print(np.argmax(self._classifier.decision_function(X_test), axis=1))
print(self._classifier.predict(X_test))
def plotshow(trainingSet,testSet,method):
y=[]
data=[]
label=[]
data_test=[]
label_test=[]
for i in range(len(trainingSet)):
data.append(list(map(eval,trainingSet[i][:-1])))
label.append(list(map(eval,trainingSet[i][-1])))
y.append(list(map(eval,trainingSet[i][-1])))
for n in range(len(testSet)):
data_test.append(list(map(eval,testSet[n][:-1])))
label_test.append(list(map(eval,testSet[n][-1])))
y.append(list(map(eval,testSet[n][-1])))
global clf
if method=="高斯朴素贝叶斯":
clf = GaussianNB(priors=None)
if method =="多项式分布贝叶斯":
clf = MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)
if method =="伯努利朴素贝叶斯":
clf = BernoulliNB(alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None)
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((3, 1), (2, 0))
ax1.plot([0, 1], [0, 1], "k:", label="Perfectly calibrated")
for clf, name in [(clf, "Naive Bayes")]:
clf = clf.fit(data, label)
if hasattr(clf, "predict_proba"):
prob_pos = clf.predict_proba(data_test)[:, 1]
else:
prob_pos = clf.decision_function(data_test)
prob_pos = \
(prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())
clf_score = brier_score_loss(label_test, prob_pos, pos_label=np.array(y).max())
fraction_of_positives, mean_predicted_value = \
calibration_curve(label_test, prob_pos, n_bins=10)
ax1.plot(mean_predicted_value, fraction_of_positives, "s-",
label="%s (%1.3f)" % (name, clf_score))
ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,
histtype="step", lw=2)
ax1.set_ylabel("Fraction of positives")
ax1.set_ylim([-0.05, 1.05])
ax1.legend(loc="lower right")
ax1.set_title('Calibration plots (reliability curve)')
ax2.set_xlabel("Mean predicted value")
ax2.set_ylabel("Count")
ax2.legend(loc="upper center", ncol=2)
plt.tight_layout()
plt.savefig("E:/Anaconda/Scripts/CorsApi/snippets/static/picture/bayes.jpg")
score = clf.score(data_test,label_test)
return score
