Python DBN.predict方法代码示例

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def benchmark(k, epochs):
  print("*" * 80)
  print("k: %d, epochs: %d\n" % (k, epochs))

  #select = SelectKBest(score_func=chi2, k=k)
  select = TruncatedSVD(n_components=k)
  X_train_trunc = select.fit_transform(X_train, Y_train)
  X_test_trunc = select.transform(X_test)

  print('done truncating')

  clf = DBN([X_train_trunc.shape[1], k, 4], learn_rates=0.3, learn_rate_decays=0.9, epochs=epochs, verbose=1)
  clf.fit(X_train_trunc, Y_train)
  pred = clf.predict(X_test_trunc)

  if CREATE_SUBMISSION:
    X_submit_trunc = select.transform(X_submit)
    pred_submit = clf.predict(X_submit_trunc)
    dump_csv(pred_submit, k, epochs)

  score = metrics.f1_score(Y_test, pred)
  print("f1-score:   %0.3f" % score)

  print("classification report:")
  print(metrics.classification_report(Y_test, pred))

  print("confusion matrix:")
  print(metrics.confusion_matrix(Y_test, pred))

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def train_model(data_set_path='/home/devin.fisher/Kingdoms/treadstone/_samples/still_data/still_training_data.pkl'):
    # data_set = None
    with open(data_set_path, 'rb') as f:
        data_set = pickle.load(f)

    # with open('/home/devin.fisher/Kingdoms/lol/still_training_data2.pkl', 'rb') as f:
    #     data_set = pickle.load(f)

    # (train_x, test_x, train_y, test_y) = train_test_split(data_set['data'], data_set['target'], test_size=0.1)

    train_x = data_set['data']
    test_x = data_set['data']
    train_y = data_set['target']
    test_y = data_set['target']

    dbn = DBN(
        [-1, 300, -1],
        learn_rates=0.3,
        learn_rate_decays=0.9,
        epochs=60,
        verbose=1)
    dbn.fit(train_x, train_y)

    joblib.dump(dbn, 'digit_model.pkl', compress=9)

    # dbn = joblib.load('digit_model.pkl')

    # compute the predictions for the test data and show a classification report
    preds = dbn.predict(test_x)
    print classification_report(test_y, preds)

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def main():
  data_id = 'B'
  data_path = '/broad/compbio/maxwshen/data/1-MAKETRAINTEST/complete/golf/'
  
  print 'train...', datetime.datetime.now()
  train_set = readin(data_id, 'train', data_path)
  print 'valid...', datetime.datetime.now()
  valid_set = readin(data_id, 'valid', data_path)
  print 'test...', datetime.datetime.now()
  test_set = readin(data_id, 'test', data_path)

  # Input to 300 node RBM to 2 node output
  dbn = DBN( \
    [xtrain.shape[1], 300, 2], \
    learn_rates = 5, \
    learn_rate_decays = 0.9, \
    epochs = 31, \
    verbose = 1)
  dbn.fit(dat_train, y_train)

  preds = dbn.predict(dat_test)
  print classification_report(y_test, preds)

  out_fn = 'dbn.pickle'
  with open(out_fn, 'w') as f:
    pickle.dump(dbn, out_fn)

  return

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def run():
    X_train, Y_train = load_training_data()

    X_train, Y_train = rotate_dataset(X_train, Y_train, 8)
    X_train, Y_train = nudge_dataset(X_train, Y_train)

    n_features = X_train.shape[1]
    n_classes = 10
    classifier = DBN([n_features, 8000, n_classes], 
        learn_rates=0.4, learn_rate_decays=0.9 ,epochs=75, verbose=1)

    classifier.fit(X_train, Y_train)

    test_data = get_test_data_set()
    predictions = classifier.predict(test_data)
    write_predictions_to_csv(predictions)

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
class DigitProphet(object):
	def __init__(self):
		# load train.csv
		# train = pd.read_csv("data/train.csv")
		# data_train=train.as_matrix()
		# values_train=data_train[:,0]
		# images_train=data_train[:,1:]
		# trainX, _trainX, trainY, _trainY = train_test_split(images_train/255.,values_train,test_size=0.5)

		# #load test.csv
		# test = pd.read_csv("data/test.csv")
		# data_test=test.as_matrix()
		# testX, _testX = train_test_split(data_test/255.,test_size=0.99)
		
		# Random Forest
		# self.clf = RandomForestClassifier()
		
		# Stochastic Gradient Descent
		# self.clf = SGDClassifier()
		
		# Support Vector Machine
		# self.clf = LinearSVC()
		
		# Nearest Neighbors
		# self.clf = KNeighborsClassifier(n_neighbors=13)
		
		
		train = pd.read_csv("data/train.csv")
		data_train=train.as_matrix()
		values_train=data_train[:,0]
		images_train=data_train[:,1:]
		trainX, _trainX, trainY, _trainY = train_test_split(images_train/255.,values_train,test_size=0.995)
		
		# Neural Network
		self.clf = DBN([trainX.shape[1], 300, 10],learn_rates=0.3,learn_rate_decays=0.9,epochs=10,verbose = 1)
		
		#Training
		self.clf.fit(trainX, trainY)
		
		pass

	def predictImage(self,array):
		image=np.atleast_2d(array)
		return self.clf.predict(image)[0]

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def train_dbn_dataset(dataset, x_test, y_test, alpha, nhidden, epochs, batch_size, noises=[]):
    from nolearn.dbn import DBN
    num_classes = len(set(y_test))
    print "Number of classes", num_classes
    x_train, y_train = dataset
    dbn_model = DBN([x_train.shape[1], nhidden, num_classes],
                    learn_rates = alpha,
                    learn_rate_decays = 0.9,
                    epochs = epochs,
                    verbose = 1,
                    nesterov=False,
                    minibatch_size=batch_size,
                    noises = noises)

    dbn_model.fit(x_train, y_train)
    from sklearn.metrics import classification_report, accuracy_score
    y_true, y_pred = y_test, dbn_model.predict(x_test) # Get our predictions
    print(classification_report(y_true, y_pred)) # Classification on each digit
    print(roc_auc_score(y_true, y_pred)) # Classification on each digit
    return y_pred, roc_auc_score(y_true, y_pred)

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
	 def test(self):
                 #iris = datasets.load_iris()
                 #X, y = iris.data, iris.target
                 X, y = self.dataMat,self.labelMat
                 X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.6, random_state=12)
                 #clf = RandomForestClassifier(max_depth=6,min_samples_split=9,min_samples_leaf=15,n_estimators=5)
                 #clf = DBN([X.shape[1], 24, 2],scales=0.5,learn_rates=0.02,learn_rate_decays = 0.95, learn_rate_minimums =0.001,epochs=500,l2_costs = 0.02*0.031, dropouts=0.2,verbose=0)
                 #cvnum = ShuffleSplit(2013,n_iter=10,test_size=0.6,train_size=0.4,random_state=0)
                 for scal in arange(4.5, 5.0, 0.5):
                     print "**************************************************************"
                     print "DBN scal=",scal
                     clf = DBN([X.shape[1], 24,48, 2],scales=0.5,learn_rates=0.01,learn_rate_decays = 0.95, learn_rate_minimums =0.001,epochs=50,l2_costs = 0.02*0.001, dropouts=0.0,verbose=0)
                     clf.fit(X_train, y_train);
                     scores = cross_val_score(clf,X,y,cv=3,scoring='roc_auc')
                     y_pred = clf.predict(X_test);
                     y_predprob = clf.predict_proba(X_test);
                     prf=precision_recall_fscore_support(y_test, y_pred, average='binary')
                     print ("Accuracy: %0.5f (+/- %0.5f)" % (scores.mean(), scores.std() * 2))
                     print  classification_report(y_test,y_pred)
                     print 'The accuracy is: ', accuracy_score(y_test,y_pred)
                     print 'The log loss is:', log_loss(y_test, y_predprob)
                     print 'The ROC score is:', roc_auc_score(y_test,y_predprob[:,1])

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def main():
    data_fn = "/home/ec2-user/Kellis/data/bravo.formatted/dat.all.txt"
    blacklist_fn = "/home/ec2-user/Kellis/data/bravo.formatted/dat.blacklist.txt"
    y_fn = "/home/ec2-user/Kellis/data/bravo.formatted/dat.y.txt"

    data = read_delimited_txt(data_fn, "\t")
    blacklist = read_delimited_txt(blacklist_fn, "\t")
    y = read_delimited_txt(y_fn, "\t")

    # Get names and remove the first element of each row which is the row number
    names = data[0]
    data = data[1:]
    for i in range(len(data)):
        data[i] = data[i][1:]

    y = y[1:]
    for i in range(len(y)):
        y[i] = y[i][-1]
    y = convert_y_binary(y)

    # Normalizes column-wise so all values are between 0 and 1
    data = normalize_0_1(data)

    # Split into training, testing
    xtrain, xtest, ytrain, ytest = train_test_split(data, y, test_size=0.2, random_state=1)

    # Input to 300 node RBM to 2 node output
    dbn = DBN([xtrain.shape[1], 300, 2], learn_rates=5, learn_rate_decays=0.9, epochs=501, verbose=1)
    dbn.fit(xtrain, ytrain)

    preds = dbn.predict(xtest)
    print classification_report(ytest, preds)

    out_fn = "dbn.pickle"
    with open(out_fn, "w") as f:
        pickle.dump(dbn, out_fn)

    return

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def runOfflineML(y, X, classifiers, savemodel=False):
    X_train, X_test, y_train, y_test = train_test_split(X, y.astype("int0"), test_size=0.20, random_state=0)
    data = dict(x_train=X_train, x_test=X_test, y_train=y_train, y_test=y_test)
    cls_stats = initClsStats(classifiers)
    for cls_name, cls in classifiers.items():
        cls_stats[cls_name]["n_train"] = data["x_train"].shape[0]
        cls_stats[cls_name]["n_test"] = data["x_test"].shape[0]
        cls_stats[cls_name]["n_features"] = data["x_train"].shape[1]
        tick = time.time()
        if cls_name == "DBN":
            data = dataNormalise(data)
            clf = DBN([data["x_train"].shape[1], 300, 2], learn_rates=0.3, learn_rate_decays=0.9, epochs=10, verbose=1)
            clf.fit(data["x_train"], data["y_train"])
        else:
            clf = classifiers[cls_name].fit(data["x_train"], data["y_train"])
        if savemodel:
            pickle.dump(clf, open(cls_name + ".dat", "w"))
            clf = pickle.load(open(cls_name + ".dat", "r"))
        cls_stats[cls_name]["training_time"] += time.time() - tick
        # check the accuracy on the training set
        tick = time.time()
        predicted = clf.predict(data["x_test"])
        cls_stats[cls_name]["testing_time"] += time.time() - tick
        acc = metrics.accuracy_score(data["y_test"], predicted)
        cls_stats[cls_name]["accuracy"] = acc
        print cls_name, "accuracy is: " + str(acc)
        # auc = metrics.roc_auc_score(data['y_test'], probs[:, 1])
        conf_matrix = metrics.confusion_matrix(data["y_test"], predicted)
        cls_stats[cls_name]["conf_matrix"] = conf_matrix
        # print conf_matrix
        precision, recall, fscore, support = metrics.precision_recall_fscore_support(data["y_test"], predicted)
        cls_stats[cls_name]["precision"] = precision
        cls_stats[cls_name]["recall"] = recall
        cls_stats[cls_name]["fscore"] = fscore
        cls_stats[cls_name]["support"] = support
    return cls_stats

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
dbn = DBN(
        [300,1024,120000],
        learn_rates = 0.025,
        learn_rate_decays = 0.98,
        l2_costs = 0.0001,
        minibatch_size=256,
        epochs=5,
        momentum = 0.9,
        #dropouts=0.22,
        verbose = 2)

dbn.fit(train_x, train_y)
print 'validation score is:' ,dbn.score(vali_x,vali_y)

result = dbn.predict(test_x)
with open('data/result','w') as f:
    for el in result:
        f.write(el+'\n')

#predicted_y_proba = dbn.predict_proba(test_x)


#if __name__ == "__main__":
    #p_proba_str = cPickle.dumps(predicted_y_proba)
    '''import sys
    file_name = sys.argv[1]
    with open(file_name, 'w') as a:
        a.write(p_proba_str)'''

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
    if dst == "q":
        break
    else:
        try:
            img=cv2.imread(dst)
            img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
            if img.shape != (32,20):
                img=cv2.resize(img,(20,32))

            _,img=cv2.threshold(img,100,255,cv2.THRESH_BINARY)
            img=img/255.0
            print img.shape
            img=img.reshape(1,s)
            img=img.astype(np.float32)
            #prediction:
            pred=dbn.predict(img)
            print pred
        except:
            print "error reading image.."
        

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
from sklearn.cross_validation import train_test_split
from sklearn.datasets import fetch_mldata

mnist = fetch_mldata("MNIST original")
X_train, X_test, y_train, y_test = train_test_split(mnist.data / 255.0, mnist.target)

from nolearn.dbn import DBN

clf = DBN([X_train.shape[1], 300, 10], learn_rates=0.3, learn_rate_decays=0.9, epochs=10, verbose=1)

clf.fit(X_train, y_train)

from sklearn.metrics import classification_report
from sklearn.metrics import zero_one_loss

y_pred = clf.predict(X_test)
print "Accuracy:", 1 - zero_one_loss(y_test, y_pred)
print "Classification report:"
print classification_report(y_test, y_pred)

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
def do_operation_(X_train,X_test,y_train,y_test,l_r,d_r):
	clf = DBN([np.shape(X_train)[1],300,10],learn_rates = l_r,learn_rate_decays = d_r,epochs = 30,verbose = 1 )
	clf.fit(X_train,y_train)
	y_test,y_pred = y_test, clf.predict(X_test)
	result = np.sum(y_test == y_pred)
	return (result,l_r,d_r)

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
	Y = np.concatenate([Y for _ in range(5)], axis=0)
	return X, Y

def rotate_dataset(X):
	XX = np.zeros(X.shape)
	for index in range(X.shape[0]):
		angle = np.random.randint(-7,7)
		XX[index,:] = nd.rotate(np.reshape(X[index,:],((28,28))),angle,reshape=False).ravel()
	return XX

# Load Data
mnist = pd.read_csv("data_5k.csv")
#mnist = mnist[:50]
y_train = mnist['label'].values
X_train = mnist.loc[:,'pixel0':].values
X_test = pd.read_csv("test.csv").values
X_test = X_test[:2000]
X_train = np.asarray(X_train / 255.0, 'float32')
X_test = np.asarray(X_test / 255.0, 'float32')
#X_train, y_train = nudge_dataset(X_train, y_train)
#X_train = rotate_dataset(X_train)
clf = DBN([X_train.shape[1], 350, 10],\
		learn_rates=0.3,\
		learn_rate_decays=0.95,\
		learn_rates_pretrain=0.005,\
		epochs=120,\
		verbose=1)
clf.fit(X_train, y_train)
subm = pd.read_csv("rf_benchmark.csv")
subm.Label = clf.predict(X_test)
subm.to_csv("result.csv", index_label='ImageId', col=['Label'], index=False)

# 需要导入模块: from nolearn.dbn import DBN [as 别名]
# 或者: from nolearn.dbn.DBN import predict [as 别名]
data_train = data_train.astype('float') / 255.
labels_train = labels_train
data_test = test_batch['data'].astype('float') / 255.
labels_test = np.array(test_batch['labels'])
n_feat = data_train.shape[1]
n_targets = labels_train.max() + 1

net = DBN(
    [n_feat, n_feat / 3, n_targets],
    epochs=100,
    learn_rates=0.01,
    learn_rate_decays=0.99,
    learn_rate_minimums=0.005,
    verbose=1,
    )
net.fit(data_train, labels_train)

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

expected = labels_test
predicted = net.predict(data_test)

print "Classification report for classifier %s:\n%s\n" % (
    net, classification_report(expected, predicted))
print "Confusion matrix:\n%s" % confusion_matrix(expected, predicted)
print "prediction over expected" % predicted/expected

joblib.dump(net, 'nl_dbn.pkl', compress=9)
#nl_clone = joblib.load('nl_dbn.pkl')