https://github.com/keras-team/keras
Tools:
It looks like a lot of libraries, but it's a standard python toolset for data analysis / machine learning.
The purpose of this tutorial is to learn how to build a machine learning model that will be able to distinguish an user from a bid-bot.
A list of bots has been collected from https://steembottracker.com and can be found here. Now we need list of users. Let's choose those users who added a post in the category #utopian-io in July 2018. We will use the following script and database SteemSQL.
SELECT DISTINCT author
FROM Comments (NOLOCK)
WHERE depth = 0 AND
category = 'utopian-io' AND
YEAR(created) = 2018 AND
MONTH(created) = 7
The obtained list is several times bigger than the bot list, but we will not modify it, because this is very common in machine learning. Rarely data is perfect. The list can be found here: users.txt
Now we want to extract statistics for each account from the list. We will use the beem library.
We will use the following attributes:
from beem.account import Account
bid_bots = set(map(str.strip, open('bid_bots.txt', 'r').readlines()))
users = (map(str.strip, open('users.txt', 'r').readlines()))
with open ('data.csv' , 'w') as f:
f.write(','.join(['name', 'followers', 'followings', 'foll. ratio', 'muters', 'rep', 'eff. sp', 'own sp', 'sp ratio', 'is bot?']) + '\n')
for name in bid_bots.union(users):
account = Account(name)
foll = account.get_follow_count()
row = (name,
foll['follower_count'],
foll['following_count'],
foll['following_count'] / foll['follower_count'],
len(account.get_muters()),
account.get_reputation(),
account.get_steem_power(),
account.get_steem_power(onlyOwnSP=True),
account.get_steem_power() / account.get_steem_power(onlyOwnSP=True),
1 if name in bid_bots else 0)
f.write(','.join(map(str, row)) + '\n')
The result file has been saved as data.csv. A sample of the file is shown below. The last column determines whether an account is a bot, and all previous ones are the attributes which we will use to teach our model.
name followers followings foll. ratio muters rep eff. sp own sp sp ratio bid-bot?
0 beggars 1144 66 0.057692 4 58.485711 457.254297 457.254297 1.000000 0
1 anthonyadavisii 3477 2295 0.660052 19 59.185663 575.470517 1210.696343 0.475322 0
2 amvanaken 3026 175 0.057832 10 60.324424 491.175030 716.217811 0.685790 0
3 shaikhraz1986 229 11 0.048035 3 46.766451 15.051121 12.462512 1.207712 0
4 harpagon 306 39 0.127451 0 55.577631 389.858866 389.858866 1.000000 0
5 famil 661 274 0.414523 3 58.858699 7.859833 207.836914 0.037817 0
6 swapsteem 197 31 0.157360 0 39.812361 15.441267 0.907584 17.013591 0
7 kr-nahid 773 366 0.473480 6 58.179101 130.890891 130.890891 1.000000 0
8 thefairypark 153 1 0.006536 0 32.825241 15.000498 0.559973 26.787898 0
9 eosbake 115 0 0.000000 0 25.000000 3.002430 3.002430 1.000000 0
10 official-hord 619 139 0.224556 0 59.131711 65.469489 65.469489 1.000000 0
.. ... ... ... ... ... ... ... ... ... ...
Let us first look at the dataset. We can expect that:
Let's check if our assumptions are confirmed.
for c in columns:
print('%s|%.3f|%.3f' % (c, df[df['bid-bot?'] == 0][c].mean(), df[df['bid-bot?'] == 1][c].mean()))
It turns out that the presumptions turn out to be true. We can see great differences in average values here.
| Attribute | User average value | Bid-bot average value |
|---|---|---|
| followers | 1078.974 | 1926.841 |
| followings | 382.238 | 204.068 |
| foll. ratio | 0.389 | 0.170 |
| muters | 3.524 | 12.330 |
| rep | 55.065 | 45.564 |
| eff. sp | 5494.488 | 224684.003 |
| own sp | 2110.639 | 13383.966 |
| sp ratio | 18.165 | 126.915 |
However, calculating the average is not enough. Let's look at some charts.
followers + followings:
sns.FacetGrid(df1, hue="bid-bot?", size=7).map(plt.scatter, "followers", "followings")
plt.legend(['user', 'bid-bot'])
muters + rep:
sns.FacetGrid(df1, hue="bid-bot?", size=7).map(plt.scatter, "muters", "rep")
plt.legend(['user', 'bid-bot'])
foll. ratio + sp ratio:
sns.FacetGrid(df1, hue="bid-bot?", size=7).map(plt.scatter, "foll. ratio", "sp ratio")
plt.legend(['user', 'bid-bot'])
Visualization gives us a much better picture of the situation. We can see that different classes are pretty separable.
We now have to separate the attributes (X) and the decision class (y), and then split the data into training and test data. The former are used to train the neural network, while the test data are used to check the effectiveness of the classification (it would not be a good idea to teach and test with the same data).
X_cols = ['followers', 'followings', 'foll. ratio', 'muters', 'rep', 'eff. sp', 'own sp', 'sp ratio']
y_cols = ['bid-bot?']
X = df[X_cols].apply(pd.to_numeric)
y = df[y_cols].apply(pd.to_numeric)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
Our model is a neural network, consisting of:
The code building the neural network model is relatively simple.
model = Sequential()
model.add(Dense(24, activation='relu', input_dim=8))
model.add(Dense(12, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.add - adds new layer
Dense - fully connected layer
relu - ReLU activation function
sigmoid - sigmoid activation function
binary_crossentropy - measures the performance of a classification, where output is a probability between 0 and 1
adam - Adaptive Moment Estimation optimizer, basically RMSProp with momentum
accuracy - percentage of correctly classified inputs
We can display the characteristics of the neural network.
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_10 (Dense) (None, 24) 216
_________________________________________________________________
dense_11 (Dense) (None, 12) 300
_________________________________________________________________
dense_12 (Dense) (None, 1) 13
=================================================================
Total params: 529
Trainable params: 529
Non-trainable params: 0
We can see that we have 3 layers:
Of course, you can set different values here or even add more layers and check what the results will be.
Now is the time to train the neural network. The epochs parameter means how many iterations will be performed, during which the entire data set is used, and the batch_size parameter determines how many data will be used simultaneously.
model.fit(X_train, y_train,epochs=50, batch_size=1, verbose=1)
Let's see a summary of the recent learning iterations.
...
Epoch 45/50
305/305 [==============================] - 0s 2ms/step - loss: 3.0651 - acc: 0.8098
Epoch 46/50
305/305 [==============================] - 0s 1ms/step - loss: 3.0651 - acc: 0.8098
Epoch 47/50
305/305 [==============================] - 0s 1ms/step - loss: 3.0651 - acc: 0.8098
Epoch 48/50
305/305 [==============================] - 0s 1ms/step - loss: 3.0651 - acc: 0.8098
Epoch 49/50
305/305 [==============================] - 0s 2ms/step - loss: 3.0651 - acc: 0.8098
Epoch 50/50
305/305 [==============================] - 0s 1ms/step - loss: 3.0651 - acc: 0.8098
Frankly speaking, it does not look good. The loss value should be as close as possible to 0, while the acc value should be as close as possible to 1.
Let's look at the confusion matrix.
y_pred = model.predict_classes(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
It turns out that the classification process has failed completely. All records (except 1) were classified as user. By the way, we see that the acc metric is not entirely reliable if we are dealing with unbalanced classes.
[[101 1]
[ 30 0]]
We need to make some modifications so that the network correctly classifies the objects. The choice is very wide:
However, there is not much data, so a simple network should be good enough. Maybe the problem is in the data itself? Let's try to make some preprocessing before adding them to the neural network. As we have seen in the previous charts, the attribute value ranges are quite large. However, for a neural network it is better if these ranges are as close as possible to [0, 1]. That is why we will normalize the input data. We will first try StandardScaler, which standardizes features by removing the mean and scaling to unit variance.
X = pd.DataFrame(StandardScaler().fit_transform(X))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
model.fit(X_train, y_train,epochs=50, batch_size=1, verbose=1)
y_pred = model.predict_classes(X_test)
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize = (4, 4))
sns.heatmap(cm, annot=True, cmap="Greens")
print('precision:', precision_score(y_test, y_pred),
'\nrecall:', recall_score(y_test, y_pred),
'\nf1:', f1_score(y_test, y_pred))
As we can see the results are much better - the loss has decreased, the acc has increased, the confusion matrix looks much better. However, these results are still far from ideal.
Epoch 50/50
305/305 [==============================] - 0s 1ms/step - loss: 0.1310 - acc: 0.9410
Let us now take a different approach to normalization: QuantileTransformer.
X = pd.DataFrame(StandardScaler().fit_transform(X))
The results are already acceptable.
We were using the number of iterations equal to 50 all the time, let's see what the result will be if we reduce the number of iterations to 10.
model.fit(X_train, y_train,epochs=10, batch_size=1, verbose=1)
It turns out that the result is basically the same and a large number of iterations were not necessary.
Let's look what is the difference between raw data and normalized data.
| Raw data | StandardScaler | QuantileTransformer |
|---|---|---|
It turns out that StandardScaler is not at all a very good choice, because the data are not in the range of [0, 1]. And the QuantileTransformer made the data not only in the range of [0,1] but also evenly distributed throughout the area.