I am doing binary classification for title sentences in news. (To determinate whether the new is political biased)
I am using the Bert embedding from https://pypi.org/project/bert-embedding/ to embedding training sentences (one raw one title sentence) in Dataframes then feed vectorised Data into logistic regression, but the output data shape from the Bert embedding doesn't support logistic regression model. How can I parse this to make it fit logistic regression model?
Before I used tifdVectorizer it works perfectly and the output is numpy array like
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
each row is vectorised data for one sentence and It's an array with size of 1903
And I have 516 titles in training data.
The output shapes are:
train_x.shape: (516, 1903) test_x.shape (129, 1903)
train_y.shape: (516,) test_y.shape (129,)
But after I switched into Bert_Embedding
the output vector for ONE row is numpy array list like
[list([array([ 9.79349554e-01, -7.06475616e-01 ...... ]dtype=float32),
array([ ........ ],dtype=float32), ......................
array([ ........ ],dtype=float32)]
the output shape is like:
train_x.shape: (516, 1) test_x.shape (129, 1)
train_y.shape: (516,) test_y.shape (129,)
def transform_to_Bert(articles_file: str, classified_articles_file: str):
df = get_df_from_articles_file(articles_file, classified_articles_file)
df_train, df_test, _, _ = train_test_split(df, df.label, stratify=df.label, test_size=0.2)
bert_embedding = BertEmbedding()
df_titles_values=df_train.title.values.tolist()
result_train = bert_embedding(df_titles_values)
result_test = bert_embedding(df_test.title.values.tolist())
train_x = pd.DataFrame(result_train, columns=['A', 'Vector'])
train_x = train_x.drop(columns=['A'])
test_x = pd.DataFrame(result_test, columns=['A', 'Vector'])
test_x=test_x.drop(columns=['A'])
test_x=test_x.values
train_x=train_x.values
print(test_x)
print(train_x)
train_y = df_train.label.values
test_y = df_test.label.values
return {'train_x': train_x, 'test_x': test_x, 'train_y': train_y, 'test_y': test_y, 'input_length': train_x.shape[1], 'vocab_size': train_x.shape[1]}
Column A is the original title string in the result. So I just drop it.
Below is the code where I use tifd vectoriser which works for logistical model.
def transform_to_tfid(articles_file: str, classified_articles_file: str):
df = get_df_from_articles_file(articles_file, classified_articles_file)
df_train, df_test, _, _ = train_test_split(df, df.label, stratify=df.label, test_size=0.2)
vectorizer = TfidfVectorizer(stop_words='english', )
vectorizer.fit(df_train.title)
train_x= vectorizer.transform(df_train.title)
train_x=train_x.toarray()
print(type(train_x))
print(train_x)
test_x= vectorizer.transform(df_test.title)
test_x=test_x.toarray()
print(test_x)
train_y = df_train.label.values
test_y = df_test.label.values
return {'train_x': train_x, 'test_x': test_x, 'train_y': train_y, 'test_y': test_y, 'input_length': train_x.shape[1], 'vocab_size': train_x.shape[1]}
model=LogisticRegression(solver='lbfgs')
model.fit(train_x, train_y)
the error is ValueError: setting an array element with a sequence.
I expected output shape from Bert: train_x.shape: (516, 1) test_x.shape (129, 1) is like that from tifd: train_x.shape: (516, 1903) test_x.shape (129, 1903)so that it fits the logistic model
ok that was my mistake or maybe it's bad convention from the library author:
[list([array([ 9.79349554e-01, -7.06475616e-01 ...... ]dtype=float32),
array([ ........ ],dtype=float32), ......................
array([ ........ ],dtype=float32)]
actually it's :
[[list([array([ 9.79349554e-01, -7.06475616e-01 ...... ]dtype=float32),
array([ ........ ],dtype=float32), ......................
array([ ........ ],dtype=float32)]]
So you gotta index 0 to reach that
Related
I have a GRU model
GRU = keras.models.Sequential([keras.layers.GRU(32),
keras.layers.Dense(32, activation= 'relu'),
keras.layers.Dense(1, activation=None)])
GRU.compile(loss="mae", optimizer="adam")
resultsGRU = GRU.fit_generator(generator = train, validation_data = train, epochs = 3, verbose= 1, shuffle = False)
If I convert train data to numpy array, I can see I don't have any zero or Nan values (I also dropna values before)
trainArray= np.array(train)
print(trainArray)
I copied only a part of array, just so you can see the values:
[[array([[[-0.86286026, 0.51805955, 1.0427724 , ..., 0.27464896,
0.08823532, -1.1183959 ],
[-0.3186916 , 0.00295895, 0.740636 , ..., 0.27464896,
0.08823532, -1.1304985 ],
[-0.31057638, 0.00295895, 0.5593542 , ..., 0.27464896,
-0.5521559 , -1.1183959 ],
...,
If I print resultsGRU
print(resultsGRU.history.values())
I get
dict_values([[0.597104012966156, 0.5652544498443604, 0.5574262142181396], [0.6241905093193054, 0.6183988451957703, 0.6134349703788757]])
Then I use predict, but values are returned 0
predictGRU = GRU.predict(test)
print(predictGRU)
[0. 0. 0. ... 0. 0. 0.]
I then save this model and use it for API and the values are NaN.
What is the problem here? How do I get the model to predict a different, reasonable value?
I also use metrics later on
print(metrics.mean_absolute_error(test, predictGRU))
print(metrics.mean_squared_error(test, predictGRU))
print(metrics.explained_variance_score(test, predictGRU))
And I get normal numbers
0.6471065
0.50334525
0.23076766729354858
I don't know how to fix this on my own.
My initial data is:
[[ 8375.5 0. 8374.14285714 8374.14285714]
[ 8354.5 0. 8383.39285714 8371.52380952]
...
[11060. 0. 11055.21428571 11032.53702732]
[11076.5 0. 11061.60714286 11038.39875701]]
I create MinMax scaler to transform data to values from 0 to 1
scaler = MinMaxScaler(feature_range = (0, 1))
T = scaler.fit_transform(T)
So data now is:
[[0.5186697 , 0. , 0.46812344, 0.46950912],
[0.5161844 , 0. , 0.46935928, 0.46915412],
...,
[0.72264636, 0. , 0.6767292 , 0.6807525 ],
[0.7198651 , 0. , 0.6785377 , 0.6833385 ]]
I do some magic to prepare this data for LSTM layer and this is the result:
X_train variable of shape (6989, 4, 200)
[[[0.5186697 0. 0.46812344 ... 0. 0.45496237 0.45219505]
[0.48742527 0. 0.45273864 ... 0. 0.43144143 0.431924 ]
[0.4800284 0. 0.43054438 ... 0. 0.425362 0.4326681 ]
[0.5007989 0. 0.4290794 ... 0. 0.4696839 0.47831726]]
...
[[0.61240304 0. 0.57254803 ... 0. 0.5749577 0.57792616]
[0.61139715 0. 0.5746571 ... 0. 0.5971378 0.6017289 ]
[0.6365465 0. 0.59772 ... 0. 0.62671924 0.63145673]
[0.65719867 0. 0.62684333 ... 0. 0.6757128 0.6772785 ]]]
I process the data using this model with Dense(1) layer at the end:
model = Sequential()
model.add(LSTM(units = 50, activation = 'relu', #return_sequences = True,
input_shape = (X_train.shape[1], window_size)))
model.add(Dropout(0.2))
model.add(Dense(1, activation = 'linear'))
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
And when I set return_sequences to false the shape of new data after fit is (6989, 1) and when I want to inverse_transform scaler.inverse_transform(train_predict) using this scalar I get an error:
ValueError: non-broadcastable output operand with shape (6989,1) doesn't match the broadcast shape (6989,4)
When I do set return_sequences to true the new shape is (6989, 4, 1) and when I inverse_transform I get other error:
ValueError: Found array with dim 3. None expected <= 2.
============
I think I know why I get these errors, because scaler requires shape of (6989,4), but what and how can I do to transform this data so that I will be able to inverse_transform?
How can I inverse_transform the data of new shape of (6989, 1)?
How can I inverse_transform the data of new shape of (6989, 4, 1)?
Is it doable? My scaler can be used? Or should I create new scaler? Can you suggest something? What am I missing?
I will appreciate any help, thanks!
I've made a convolutional neural networks algorithm to classify images, and now I want to make a Naive Bayes algorithm for comparison. My images are 3D, and I think that's the cause of the error I'm getting.
The error:
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (1776, 3)
My code:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
import numpy as np
much_data = np.load('muchdata-50-50-30-normalizado.npy', allow_pickle=True)
X = [data[0] for data in much_data]
y = [data[1] for data in much_data]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
gnb = GaussianNB()
y_pred = gnb.fit(X_train, y_train).predict(X_test)
print("Number of mislabeled points out of a total %d points : %d" % (X_test.shape[0], (y_test != y_pred).sum()))
My X[0] is in the following format:
[[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]
...
[[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
...
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]
[0. 0. 0. ... 0. 0. 0.]]]
And my y[0]:
[0 1 0]
If someone can help me understand what I'm doing wrong, it will be really helpful!
Thank you so much!
By looking at your y[0] it seems like you have 3 classes in one-hot encoded format. sklearn's machine learning algorithms in general do not accept target values in one-hot encoded format. Moreover, the input (X) to model should have the shape of (no_samples, no_features). Therefore, you must flatten the 3D images.
Get rid of one-hot encodings in the target (y) and obtain a 1D array in the format of (no_samples,). You may achieve this by defining the 3 classes as 1, 2, 3.
Flatten the images. You may do this with X = [data[0].flatten() for data in much_data]
I have tried the example with keras but was not with LSTM. My model is with LSTM in Tensorflow and I am willing to predict the output in the form of classes as the keras model thus with predict_classes.
The Tensorflow model I am trying is something like this:
seq_len=10
n_steps = seq_len-1
n_inputs = x_train.shape[2]
n_neurons = 50
n_outputs = y_train.shape[1]
n_layers = 2
learning_rate = 0.0001
batch_size =100
n_epochs = 1000
train_set_size = x_train.shape[0]
test_set_size = x_test.shape[0]
tf.reset_default_graph()
X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_outputs])
layers = [tf.contrib.rnn.LSTMCell(num_units=n_neurons,activation=tf.nn.sigmoid, use_peepholes = True) for layer in range(n_layers)]
multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layers)
rnn_outputs, states = tf.nn.dynamic_rnn(multi_layer_cell, X, dtype=tf.float32)
stacked_rnn_outputs = tf.reshape(rnn_outputs, [-1, n_neurons])
stacked_outputs = tf.layers.dense(stacked_rnn_outputs, n_outputs)
outputs = tf.reshape(stacked_outputs, [-1, n_steps, n_outputs])
outputs = outputs[:,n_steps-1,:]
loss = tf.reduce_mean(tf.square(outputs - y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
training_op = optimizer.minimize(loss)
I am encoding the with sklearn LabelEncoder as:
encoder_train = LabelEncoder()
encoder_train.fit(y_train)
encoded_Y_train = encoder_train.transform(y_train)
y_train = np_utils.to_categorical(encoded_Y_train)
The data is converted to sparse matrix kinda thing in binary format.
When I tried to predict the output I got the following:
actual==> [[0. 0. 1.]
[1. 0. 0.]
[1. 0. 0.]
[0. 0. 1.]
[1. 0. 0.]
[1. 0. 0.]
[1. 0. 0.]
[0. 1. 0.]
[0. 1. 0.]]
predicted==> [[0.3112209 0.3690182 0.31357136]
[0.31085992 0.36959863 0.31448898]
[0.31073445 0.3703295 0.31469804]
[0.31177694 0.37011752 0.3145326 ]
[0.31220382 0.3692756 0.31515726]
[0.31232828 0.36947766 0.3149037 ]
[0.31190437 0.36756667 0.31323162]
[0.31339088 0.36542615 0.310322 ]
[0.31598282 0.36328828 0.30711085]]
What I was expecting for the label based on the encoding done. As the Keras model thus. See the following:
predictions = model.predict_classes(X_test, verbose=True)
print("REAL VALUES:",reverse_category(Y_test,axis=1))
print("PRED VALUES:",predictions)
print("REAL COLORS:")
print(encoder.inverse_transform(reverse_category(Y_test,axis=1)))
print("PREDICTED COLORS:")
print(encoder.inverse_transform(predictions))
The output is something like the following:
REAL VALUES: [1 1 1 ... 1 2 1]
PRED VALUES: [2 1 1 ... 1 2 2]
REAL COLORS:
['ball' 'ball' 'ball' ... 'ball' 'bat' 'ball']
PREDICTED COLORS:
['bat' 'ball' 'ball' ... 'ball' 'bat' 'bat']
Kindly, let me know what I can do in the tensorflow model that will get me the result with respect to the encoding done.
I am using Tensorflow 1.12.0 and Windows 10
You are trying to map the predicted class probabilities back to class labels. Each row in the list of output predictions contains the three predicted class probabilities. Use np.argmax to obtain the one with the highest predicted probability in order to map to the predicted class label:
import numpy as np
predictions = [[0.3112209, 0.3690182, 0.31357136],
[0.31085992, 0.36959863, 0.31448898],
[0.31073445, 0.3703295, 0.31469804],
[0.31177694, 0.37011752, 0.3145326 ],
[0.31220382, 0.3692756, 0.31515726],
[0.31232828, 0.36947766, 0.3149037 ],
[0.31190437, 0.36756667, 0.31323162],
[0.31339088, 0.36542615, 0.310322 ],
[0.31598282, 0.36328828, 0.30711085]]
np.argmax(predictions, axis=1)
Gives:
array([1, 1, 1, 1, 1, 1, 1, 1, 1])
In this case, class 1 is predicted 9 times.
As noted in the comments: this is exactly what Keras does under the hood, as you'll see in the source code.
I am trying to make a simple proof-of-concept where I can see the probabilities of different classes for a given prediction.
However, everything I try seems to only output the predicted class, even though I am using a softmax activation. I am new to machine learning, so I'm not sure if I am making a simple mistake or if this is a feature not available in Keras.
I'm using Keras + TensorFlow. I have adapted one of the basic examples given by Keras for classifying the MNIST dataset.
My code below is exactly the same as the example, except for a few (commented) extra lines that exports the model to a local file.
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import h5py # added import because it is required for model.save
model_filepath = 'test_model.h5' # added filepath config
batch_size = 128
num_classes = 10
epochs = 20
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(model_filepath) # added saving model
print('Model saved') # added log
Then the second part of this is a simple script that should import the model, predict the class for some given data, and print out the probabilities for each class. (I am using the same mnist class included with the Keras codebase to make an example as simple as possible).
import keras
from keras.datasets import mnist
from keras.models import Sequential
import keras.backend as K
import numpy
# loading model saved locally in test_model.h5
model_filepath = 'test_model.h5'
prev_model = keras.models.load_model(model_filepath)
# these lines are copied from the example for loading MNIST data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
# for this example, I am only taking the first 10 images
x_slice = x_train[slice(1, 11, 1)]
# making the prediction
prediction = prev_model.predict(x_slice)
# logging each on a separate line
for single_prediction in prediction:
print(single_prediction)
If I run the first script to export the model, then the second script to classify some examples, I get the following output:
[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
This is great for seeing which class each is predicted to be, but what if I want to see the relative probabilities of each class for each example? I am looking for something more like this:
[ 0.94 0.01 0.02 0. 0. 0.01 0. 0.01 0.01 0.]
[ 0. 0. 0. 0. 0.51 0. 0. 0. 0.49 0.]
...
In other words, I need to know how sure each prediction is, not just the prediction itself. I thought seeing the relative probabilities was a part of using a softmax activation in the model, but I can't seem to find anything in the Keras documentation that would give me probabilities instead of the predicted answer. Am I making some kind of silly mistake, or is this feature not available?
So it turns out that the problem was I was not fully normalizing the data in the prediction script.
My prediction script should have had the following lines:
# these lines are copied from the example for loading MNIST data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_train = x_train.astype('float32') # this line was missing
x_train /= 255 # this line was missing too
Because the data was not cast to float, and divided by 255 (so it would be between 0 and 1), it was just showing up as 1s and 0s.
Keras predict indeed returns probabilities, and not classes.
Cannot reproduce your issue with my system configuration:
Python version 2.7.12
Tensorflow version 1.3.0
Keras version 2.0.9
Numpy version 1.13.3
Here is my prediction output for your x_slice with the loaded model (trained for 20 epochs, as in your code):
print(prev_model.predict(x_slice))
# Result:
[[ 1.00000000e+00 3.31656316e-37 1.07806675e-21 7.11765177e-30
2.48000320e-31 5.34837679e-28 3.12470132e-24 4.65175406e-27
8.66994134e-31 5.26426367e-24]
[ 0.00000000e+00 5.34361977e-30 3.91144999e-35 0.00000000e+00
1.00000000e+00 0.00000000e+00 1.05583665e-36 1.01395577e-29
0.00000000e+00 1.70868685e-29]
[ 3.99137559e-38 1.00000000e+00 1.76682222e-24 9.33333581e-31
3.99846307e-15 1.17745576e-24 1.87529709e-26 2.18951752e-20
3.57518280e-17 1.62027896e-28]
[ 6.48006586e-26 1.48974980e-17 5.60530329e-22 1.81973780e-14
9.12573406e-10 1.95987500e-14 8.08566866e-27 1.17901132e-12
7.33970447e-13 1.00000000e+00]
[ 2.01602060e-16 6.58242856e-14 1.00000000e+00 6.84244084e-09
1.19809885e-16 7.94907624e-14 3.10690434e-19 8.02848586e-12
4.68330721e-11 5.14736501e-15]
[ 2.31014903e-35 1.00000000e+00 6.02224725e-21 2.35928828e-23
7.50006509e-15 4.06930881e-22 1.13288827e-24 4.20440718e-17
4.95182972e-17 1.85492109e-18]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00
0.00000000e+00 6.30200370e-27 0.00000000e+00 5.19937755e-33
1.63205659e-31 1.21508034e-20]
[ 1.44608573e-26 1.00000000e+00 1.78712268e-18 6.84598301e-19
1.30042071e-11 2.53873986e-14 5.83169942e-17 1.20201071e-12
2.21844570e-14 3.75015198e-15]
[ 0.00000000e+00 6.29184453e-34 9.22474943e-29 0.00000000e+00
1.00000000e+00 3.05067233e-34 1.43097161e-28 1.34234082e-29
4.28647272e-36 9.29760838e-34]
[ 4.68828449e-30 5.55172479e-20 3.26705529e-19 9.99999881e-01
3.49577992e-22 1.27715460e-11 4.99185615e-36 1.19164204e-20
4.21086124e-16 1.52631387e-07]]
I suspect some rounding issue when printing (or you have trained for much more epochs, and your probabilities for the training set have gotten very close to 1)...
To convince yourself that you indeed get probabilities and not class predictions, I suggest to try getting predictions from your model trained for a single epoch; normally you should see much less 1.0's - here is the case here for a model trained for epochs=1:
print(model.predict(x_slice))
# Result:
[[ 9.99916673e-01 5.36548761e-08 6.10747229e-05 8.21199933e-07
6.64725164e-08 6.78853041e-07 9.09637220e-06 4.56192402e-06
1.62688798e-06 5.23997733e-06]
[ 7.59836894e-07 1.78043920e-05 1.79073555e-04 2.95592145e-05
9.98031914e-01 1.75839632e-05 5.90557102e-06 1.27705920e-03
3.94643757e-06 4.36416740e-04]
[ 4.48473330e-08 9.99895334e-01 2.82608235e-05 5.33154832e-07
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