Very low performance even after oversampling dataset - machine-learning

I'm using an MLPClassifier for classification of heart diseases. I used imblearn.SMOTE to balance the objects of each class. I was getting very good results (85% balanced acc.), but i was advised that i would not use SMOTE on test data, only for train data. After i made this changes, the performance of my classifier fell down too much (~35% balanced accuracy) and i don't know what can be wrong.
Here is a simple benchmark with training data balanced but test data unbalanced:
And this is the code:
def makeOverSamplesSMOTE(X,y):
from imblearn.over_sampling import SMOTE
sm = SMOTE(sampling_strategy='all')
X, y = sm.fit_sample(X, y)
return X,y
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)
## Normalize data
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
## SMOTE only on training data
X_train, y_train = makeOverSamplesSMOTE(X_train, y_train)
clf = MLPClassifier(hidden_layer_sizes=(20),verbose=10,
learning_rate_init=0.5, max_iter=2000,
activation='logistic', solver='sgd', shuffle=True, random_state=30)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
I'd like to know what i'm doing wrong, since this seems to be the proper way of preparing data.

The first mistake in your code is when you are transforming data into standard format. You only need to fit StandardScaler once and that is on X_train. You shouldn't refit it on X_test. So the correct code will be:
def makeOverSamplesSMOTE(X,y):
from imblearn.over_sampling import SMOTE
sm = SMOTE(sampling_strategy='all')
X, y = sm.fit_sample(X, y)
return X,y
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20)
## Normalize data
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
## SMOTE only on training data
X_train, y_train = makeOverSamplesSMOTE(X_train, y_train)
clf = MLPClassifier(hidden_layer_sizes=(20),verbose=10,
learning_rate_init=0.5, max_iter=2000,
activation='logistic', solver='sgd', shuffle=True, random_state=30)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
For the machine learning model, try reducing the learning rate. it is too high. the default learning rate in sklearn is 0.001. Try changing the activation function and the number of layers. Also not every ML model works on every dataset so you might need to look at your data and choose ML model accordingly.

Hope you have already got better result for your model.I tried by changing few parameter, and I getting accuracy of 65%, when I change it to 90:10 sample I got an accuracy of 70%.
But accuracy can mislead,so I calculated F1 score which give you better picture of prediction.
from sklearn.neural_network import MLPClassifier
clf = MLPClassifier(hidden_layer_sizes=(1,),verbose=False,
learning_rate_init=0.001,
max_iter=2000,
activation='logistic', solver='sgd', shuffle=True, random_state=50)
clf.fit(X_train_res, y_train_res)
y_pred = clf.predict(X_test)
from sklearn.metrics import accuracy_score, confusion_matrix ,classification_report
score=accuracy_score(y_test, y_pred, )
print(score)
cr=classification_report(y_test, clf.predict(X_test))
print(cr)
Accuracy = 0.65
classification report :
precision recall f1-score support
0 0.82 0.97 0.89 33
1 0.67 0.31 0.42 13
2 0.00 0.00 0.00 6
3 0.00 0.00 0.00 4
4 0.29 0.80 0.42 5
micro avg 0.66 0.66 0.66 61
macro avg 0.35 0.42 0.35 61
weighted avg 0.61 0.66 0.61 61
confusion_matrix:
array([[32, 0, 0, 0, 1],
[ 4, 4, 2, 0, 3],
[ 1, 1, 0, 0, 4],
[ 1, 1, 0, 0, 2],
[ 1, 0, 0, 0, 4]], dtype=int64)

Related

how to predict new values when a machine learning model was standardized StandardScaler

I'm working on a machine learning model, I have a dataframe with the data
I normalize the data with a standard distribution
scaler = StandardScaler()
df = scaler.fit_transform(df)
I divide the datasets into target and characteristics
X_df = df[X_characteristics_list]
y_df = df[target]
I split into train and test then I train the model
X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size = 0.25)
forest = RandomForestRegressor()
forest.fit(X_train, y_train)
I predict the test to validate the effectiveness
y_test_pred = forest.predict(X2_test)
mse = mean_squared_error(y_test, y_test_pred)
But when is time to test in real life I need to leave the model ready to predict
If i Want to predict just one record
let say [100,20,34]
I can't because I need the record standardized, and transform it with StandardScaler does not work because it depends on standard deviation so I would need the original dataset
What's the best way to solve this problem.
See below:
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.preprocessing import StandardScaler
# Create our input and output matrices
>>> X, y = make_classification()
# Split train-test... "test" will be production/unobserved/"real-life" data
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)
# What does X_train look like?
>>> X_train
array([[-0.08930702, -2.71113991, -0.93849926, ..., 0.21650905,
0.68952722, 0.61365789],
[-0.31143977, -1.87817904, 0.08287492, ..., -0.41332943,
-0.58967179, 1.7239411 ],
[-1.62287589, 1.10691318, -0.630556 , ..., -0.35060008,
1.11270562, 0.08106694],
...,
[-0.59797041, 0.90218081, 0.89983074, ..., -0.54374315,
1.18534841, -0.03397969],
[-1.2006559 , 1.01890955, -1.21617181, ..., 1.76263322,
1.38280423, -1.0192972 ],
[ 0.11883425, 1.42952643, -1.23647358, ..., 1.02509208,
-1.14308885, 0.72096531]])
# Let's scale it
>>> scaler = StandardScaler()
>>> X_train = scaler.fit_transform(X_train)
>>> X_train
array([[ 0.08867642, -1.97950269, -1.1214106 , ..., 0.22075623,
0.57844552, 0.46487917],
[-0.10736984, -1.34896243, 0.00808597, ..., -0.37670234,
-0.6045418 , 1.57819736],
[-1.26479555, 0.91071257, -0.78086855, ..., -0.3171979 ,
0.96979563, -0.06916763],
...,
[-0.36025134, 0.7557329 , 0.91152449, ..., -0.50041152,
1.03697478, -0.18452874],
[-0.89215959, 0.84409499, -1.42847749, ..., 1.68739437,
1.21957946, -1.17253964],
[ 0.27237431, 1.15492649, -1.4509284 , ..., 0.98777012,
-1.116335 , 0.57247992]])
# Fit the model
>>> model = LogisticRegression()
>>> model.fit(X_train, y_train)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
warm_start=False)
# Now let's use the already-fitted StandardScaler object to simply transform
# *not fit_transform* the test data
>>> X_test = scaler.transform(X_test)
>>> model.predict(X_test)
array([1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
0, 0, 0])
Note that using joblib or pickle you can save the scaler object and re-load it for scaling in "real-time" later on.

What could be the best way to make your SVM faster and reliable?

I'm new to data mining. I have implemented my linear SVM as following.
X_train, X_test, y_train, y_test, = train_test_split(X, y, test_size=0.1, random_state = 0)
#print X_train.shape, y_train.shape
#print X_test.shape, y_test.shape
clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
print clf.score(X_test, y_test)
clf = svm.SVC(kernel='linear', C=1)
scores = cross_val_score(clf, X, y, cv=10)
print scores
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std()*2 ))
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],'C': [1, 10, 100, 1000]},{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
scores = ['precision', 'recall']
svr = svm.SVC(C=1)
for score in scores:
print("# Tuning hyper-parameters for %s"% score)
clf =GridSearchCV(svr, tuned_parameters, cv=10,scoring='%s_macro'% score)
clf.fit(X_train, y_train)
print("best parameters %s" % clf.best_params_)
Here, My data is too huge so what should I do to make my linear svm to run it very fast?
Do parameter tuning only on a sample.
Once you have found good parameters, then use the entire data set.

Linear regression model accuracy is always 1.0 in tensorflow

Problem:
I am building a model that will predict housing price. So, firstly I
decided to build a Linear regression model in Tensorflow. But when I
start training I see that my accuracy is always 1
I am new to machine learning. Please, someone, tell me what's going wrong I can't figure it out. I searched in google but doesn't find any answer that solves my problem.
Here's my code
df_train = df_train.loc[:, ['OverallQual', 'GrLivArea', 'GarageArea', 'SalePrice']]
df_X = df_train.loc[:, ['OverallQual', 'GrLivArea', 'GarageArea']]
df_Y = df_train.loc[:, ['SalePrice']]
df_yy = get_dummies(df_Y)
print("Shape of df_X: ", df_X.shape)
X_train, X_test, y_train, y_test = train_test_split(df_X, df_yy, test_size=0.15)
X_train = np.asarray(X_train).astype(np.float32)
X_test = np.asarray(X_test).astype(np.float32)
y_train = np.asarray(y_train).astype(np.float32)
y_test = np.asarray(y_test).astype(np.float32)
X = tf.placeholder(tf.float32, [None, num_of_features])
y = tf.placeholder(tf.float32, [None, 1])
W = tf.Variable(tf.zeros([num_of_features, 1]))
b = tf.Variable(tf.zeros([1]))
prediction = tf.add(tf.matmul(X, W), b)
num_epochs = 20000
# calculating loss
cost = tf.reduce_mean(tf.losses.softmax_cross_entropy(onehot_labels=y, logits=prediction))
optimizer = tf.train.GradientDescentOptimizer(0.00001).minimize(cost)
correct_prediction = tf.equal(tf.argmax(prediction, axis=1), tf.argmax(y, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(num_epochs):
if epoch % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={X: X_train, y: y_train})
print('step %d, training accuracy %g' % (epoch, train_accuracy))
optimizer.run(feed_dict={X: X_train, y: y_train})
print('test accuracy %g' % accuracy.eval(feed_dict={
X: X_test, y: y_test}))
Output is:
step 0, training accuracy 1
step 100, training accuracy 1
step 200, training accuracy 1
step 300, training accuracy 1
step 400, training accuracy 1
step 500, training accuracy 1
step 600, training accuracy 1
step 700, training accuracy 1
............................
............................
step 19500, training accuracy 1
step 19600, training accuracy 1
step 19700, training accuracy 1
step 19800, training accuracy 1
step 19900, training accuracy 1
test accuracy 1
EDIT:
I changed my cost function to this
cost = tf.reduce_sum(tf.pow(prediction-y, 2))/(2*1241)
But still my output is always 1.
EDIT 2:
In response to lejlot comment:
Thanks lejlot. I changed my accuracy code to this
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter("/tmp/hpp1")
writer.add_graph(sess.graph)
for epoch in range(num_epochs):
if epoch % 5:
s = sess.run(merged_summary, feed_dict={X: X_train, y: y_train})
writer.add_summary(s, epoch)
sess.run(optimizer,feed_dict={X: X_train, y: y_train})
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: X_train, y: y_train})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: X_train, y: y_train})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
But the output is all nan
Output:
....................................
Epoch: 19900 cost= nan W= nan b= nan
Epoch: 19950 cost= nan W= nan b= nan
Epoch: 20000 cost= nan W= nan b= nan
Optimization Finished!
Training cost= nan W= nan b= nan
You want to use linear regression, but you actually use logistic regression. Take a look at tf.losses.softmax_cross_entropy: it outputs a probability distribution, i.e. a vector of numbers that sum up to 1. In your case, the vector has size=1, hence it always outputs [1].
Here are two examples that will help you see the difference: linear regression and logistic regression.

Tensorflow: Visualizing trained weights for linear classifier on MNIST dataset

I have trained a linear classifier on the MNIST dataset with 92% accuracy. Then I fixed the weights and optimized the input image such that softmax probability for 8 was maximized. But the softmax loss doesn't decrease below 2.302 (-log(1/10)) which means that my training has been useless. What am I doing wrong?
Code for training the weights:
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels,
mnist.test.images, mnist.test.labels
X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, 10])
w = tf.Variable(tf.random_normal([784, 10], stddev=0.01))
b = tf.Variable(tf.zeros([10]))
o = tf.nn.sigmoid(tf.matmul(X, w)+b)
cost= tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=o, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(o, 1)
sess=tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(100):
for start, end in zip(range(0, len(trX), 256), range(256, len(trX)+1, 256)):
sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})
print(i, np.mean(np.argmax(teY, axis=1) == sess.run(predict_op, feed_dict={X: teX})))
Code for training the image for fixed weights:
#Copy trained weights into W,B and pass them as placeholders to new model
W=sess.run(w)
B=sess.run(b)
X=tf.Variable(tf.random_normal([1, 784], stddev=0.01))
Y=tf.constant([0, 0, 0, 0, 0, 0, 0, 0, 1, 0])
w=tf.placeholder("float")
b=tf.placeholder("float")
o = tf.nn.sigmoid(tf.matmul(X, w)+b)
cost= tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=o, labels=Y))
train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)
predict_op = tf.argmax(o, 1)
sess.run(tf.global_variables_initializer())
for i in range(1000):
sess.run(train_op, feed_dict={w:W, b:B})
if i%50==0:
sess.run(cost, feed_dict={w:W, b:B})
print(i, sess.run(predict_op, feed_dict={w:W, b:B}))
You shouldn't call tf.sigmoid on the output of your net. softmax_cross_entropy_with_logits assumes your inputs are logits, i.e. unconstrained real numbers. Using
o = tf.matmul(X, w)+b
increases your accuracy to 92.8%.
With this modification, your second training works. The cost reaches 0 although the resulting image is anything but appealing.

Is F1 micro the same as Accuracy?

I have tried many examples with F1 micro and Accuracy in scikit-learn and in all of them, I see that F1 micro is the same as Accuracy. Is this always true?
Script
from sklearn import svm
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import f1_score, accuracy_score
# prepare dataset
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# svm classification
clf = svm.SVC(kernel='rbf', gamma=0.7, C = 1.0).fit(X_train, y_train)
y_predicted = clf.predict(X_test)
# performance
print "Classification report for %s" % clf
print metrics.classification_report(y_test, y_predicted)
print("F1 micro: %1.4f\n" % f1_score(y_test, y_predicted, average='micro'))
print("F1 macro: %1.4f\n" % f1_score(y_test, y_predicted, average='macro'))
print("F1 weighted: %1.4f\n" % f1_score(y_test, y_predicted, average='weighted'))
print("Accuracy: %1.4f" % (accuracy_score(y_test, y_predicted)))
Output
Classification report for SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma=0.7, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
precision recall f1-score support
0 1.00 0.90 0.95 10
1 0.50 0.88 0.64 8
2 0.86 0.50 0.63 12
avg / total 0.81 0.73 0.74 30
F1 micro: 0.7333
F1 macro: 0.7384
F1 weighted: 0.7381
Accuracy: 0.7333
F1 micro = Accuracy
In classification tasks for which every test case is guaranteed to be assigned to exactly one class, micro-F is equivalent to accuracy. It won't be the case in multi-label classification.
This is because we are dealing with a multi class classification , where every test data should belong to only 1 class and not multi label , in such case where there is no TN , we can call True Negatives as True Positives.
Formula wise ,
correction : F1 score is 2* precision* recall / (precision + recall)
Micoaverage precision, recall, f1 and accuracy are all equal for cases in which every instance must be classified into one (and only one) class. A simple way to see this is by looking at the formulas precision=TP/(TP+FP) and recall=TP/(TP+FN). The numerators are the same, and every FN for one class is another classes's FP, which makes the denominators the same as well. If precision = recall, then f1 will also be equal.
For any inputs should should be able to show that:
from sklearn.metrics import accuracy_score as acc
from sklearn.metrics import f1_score as f1
f1(y_true,y_pred,average='micro')=acc(y_true,y_pred)
I had the same issue so I investigated and came up with this:
Just thinking about the theory, it is impossible that accuracy and the f1-score are the very same for every single dataset. The reason for this is that the f1-score is independent from the true-negatives while accuracy is not.
By taking a dataset where f1 = acc and adding true negatives to it, you get f1 != acc.
>>> from sklearn.metrics import accuracy_score as acc
>>> from sklearn.metrics import f1_score as f1
>>> y_pred = [0, 1, 1, 0, 1, 0]
>>> y_true = [0, 1, 1, 0, 0, 1]
>>> acc(y_true, y_pred)
0.6666666666666666
>>> f1(y_true,y_pred)
0.6666666666666666
>>> y_true = [0, 1, 1, 0, 1, 0, 0, 0, 0]
>>> y_pred = [0, 1, 1, 0, 0, 1, 0, 0, 0]
>>> acc(y_true, y_pred)
0.7777777777777778
>>> f1(y_true,y_pred)
0.6666666666666666

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