Author: Zen and the Art of Computer Programming
1. Introduction
At present, artificial intelligence technology has become a hot word in our country and around the world. In recent years, the development of the field of artificial intelligence has produced many major changes, such as big data, cloud computing, etc., which have brought massive data resources; technologies such as computer graphics, machine learning, and deep learning have continued to emerge, giving people new way of thinking; and the rise of mobile Internet has also promoted the development of artificial intelligence. Therefore, the definition and technical route of artificial intelligence still need to be further clarified. This article will start from international experience, sort out the definition of artificial intelligence, and elaborate on the current research and application prospects of artificial intelligence.
1.1 Definition
According to the latest definition of the “World Economic Forum (WEF) Council on Artificial Intelligence (CAi)”: “Artificial intelligence is the science and engineering of making machines perform tasks that require human intelligence.” mission science and engineering.)
1.2 Development History
In 1956, Alan McCulloch proposed that “the task of computer science is to study how to create intelligent machines”, which was quickly widely accepted. In the 1950s, scientists such as Professor John Plant, Joseph Raymond Lane, and James Friedrich Miroson achieved important results in the development of robot control systems. In 1962, Neil Pearson first proposed the idea that “computers can imitate the feelings and responses of living creatures.” This was the beginning of the development history of computer science. In the late 1970s, the first information revolution swept the world, and the first computer virus occurred in the world. In the mid-1970s, machine learning and neural networks were introduced and achieved great success.
1.3 Research Direction
- cognitive computing
- Intelligent decision-making
- language processing
- machine learning
- natural language understanding
- physiological computing
- deep learning
- optimization
- statistics
- psychology
1.4 Technology Frontier
AI is transforming from traditional industries to emerging industries, especially in the financial field. Traditional non-AI financial instruments may not be able to withstand the impact of the next AI bubble. In the next five to ten years, AI will occupy a leading position in the financial industry, and various companies and institutions will actively deploy it. Among them, China’s AI trading platform Binance is the most noteworthy. It provides users with a free digital currency trading platform, provides a wealth of trading products and services, and also includes digital currency wallet functions.
On the other hand, international competition is also gradually heating up. Currently, Intel and Intel, the leading chip manufacturers in the United States, have adopted open source architecture design concepts. One reason for this is that open source systems can reduce the cost and risk of innovation. Chen Jizhi, co-founder of Parsec Technology, a well-known domestic IT company, believes that the intensified international competition is due to the community power brought by open source and the competition between various companies. In the future, artificial intelligence will increasingly rely on open source technology systems to help humans seek breakthroughs.
2. Core concepts and terms
2.1 Data Driver
Data-driven is one of the three major elements of artificial intelligence. Data-driven means using a large amount of data to train a machine learning model, analyzing data characteristics, building a model, and then using the model to predict results. Whether it is image recognition or speech recognition, data-driven is the most basic part.
The key to being data-driven is the quality of training data. If the data quality is good, a high-precision model can be effectively trained. However, when the amount of data is large, it will also be affected by noise, outliers and other factors. In this case, data enhancement methods need to be considered. Data enhancement is to use existing data to expand or generate, expand the size of the original sample, and avoid the problem of model overfitting.
2.2 Model Architecture
Model architecture is a complex topic because different types of models have different structural choices. For example, for image classification models, some can choose AlexNet, VGG, etc.; for text classification models, some can choose Bag-of-Words based on the bag-of-word model, Word2Vec based on the word embedding model, etc. Therefore, it is necessary to select an appropriate model architecture based on the actual situation.
2.3 Hyperparameter Optimization
Hyperparameter optimization is an important link. Hyperparameters are parameters in the model training process, including learning rate, weight attenuation coefficient, normalization method, etc. Hyperparameters do not have fixed values, and appropriate values need to be found through some automated methods, such as grid search, Bayesian optimization, etc.
2.4 Iterative Optimization
Iterative optimization refers to repeatedly training and adjusting model parameters until the model reaches the optimal state. With each iteration, you will get better results. Iterative optimization is often much better than training the model directly once.
3. Algorithm principle and operation process
3.1 Random Forest
Random forest is one of the ensemble learning methods. It is a collection of multiple decision trees with intersections between them. For new data, Random Forest assigns a score to each decision tree and selects the decision tree with the highest total score as the final prediction. The main advantage of random forest is its high accuracy. In many cases, its accuracy is far higher than other models. The shortcomings of random forest are also obvious. The training speed is slow and it is easy to overfit.
3.1.1 Algorithm Description
The random forest algorithm includes the following steps:
- Generate K random training subsets of similar sizes.
- On each subset, a decision tree is generated.
- Prune each tree to remove irrelevant branches.
- All trees are voted on to determine the output category.
3.1.2 Random Forest Classifier
Random forest classifiers can be constructed in two ways:
- Classification tree: It is an ordinary CART decision tree, used for classification.
- Regression Tree: Used to solve regression problems, i.e. predicting continuous values instead of discrete values.
3.1.3 Advantages of Random Forest
- The average performance of random forests is quite good, with an error rate smaller than the average error rate of the base classifier.
- Can handle numerical variables, while decision trees can only handle nominal variables.
- There is no need to do feature selection because it will select the appropriate variables by itself.
- Can handle imbalanced data.
- Can be predicted quickly.
- Works well with text and image data.
3.1.4 Disadvantages of Random Forest
- Random forests take longer to test.
- If the classification tree of the random forest is too complex, overfitting may easily occur.
- Some parameters need to be set first, such as the number of decision trees, maximum depth, etc.
- For data sets with class imbalance, bias is prone to occur during prediction.
3.2 GBDT
GBDT is the abbreviation of Gradient Boosting Decision Tree, which means gradient boosting decision tree. GBDT belongs to the Boosting algorithm family and is an integrated learning method. GBDT consists of multiple decision trees. Each decision tree has its own weak learner and is trained sequentially. During the training process, the predicted values of each decision tree are accumulated and gradually fitted with the residuals of the predicted values of all previous trees.
3.2.1 Algorithm Description
The GBDT algorithm includes the following steps:
- Initialization phase: Initialize the value of the starting loss function.
- Modeling stage: Calculate the negative gradient for each sample, that is, calculate the error of this iteration.
- Prediction stage: The final prediction result is based on the prediction values of all previous trees plus the prediction value of the current tree.
- Update phase: Update the predicted values of all previous trees so that the next iteration can better fit the predicted values of the previous trees.
- Loop stage: Repeat steps two to four until the prediction accuracy reaches the required level.
3.2.2 Classifier of GBDT
The classifier of GBDT can be a regression tree or a classification tree. Usually, both regression trees and classification trees are used, but the final result can be obtained by majority voting.
3.2.3 Advantages of GBDT
- GBDT is a distributed machine learning method with fast training speed.
- The GBDT algorithm is very effective in handling classification problems.
- The GBDT algorithm is capable of processing large data sets.
- GBDT can automatically select features.
3.2.4 Disadvantages of GBDT
- Easy to overfit.
- Most of the useful information in the data set is ignored.
- It is difficult to handle in high-dimensional space.
3.3 Xgboost
Xgboost is Extreme Gradient Boosting, and its Chinese name is extreme gradient enhancement. Xgboost is an open source project that implements the GBDT algorithm, can automatically adjust parameters, and supports distributed computing.
3.3.1 Algorithm Description
The Xgboost algorithm, like GBDT, also uses the boosting algorithm, but it turns the tree into a node.
3.3.2 Xgboost’s classifier
Xgboost’s classifier is the same as GBDT, it can also be a regression tree or a classification tree.
3.3.3 Xgboost advantages
- Xgboost has greatly improved both speed and accuracy.
- Xgboost can produce prediction results when processing unlabeled data.
- Xgboost can automatically adjust parameters, which is very important for dealing with noisy and unbalanced data sets.
- Xgboost supports distributed computing.
3.3.4 Xgboost Disadvantages
- The default parameters of Xgboost cannot meet the needs of all scenarios.
- The prediction accuracy of Xgboost is slightly lower than GBDT.
3.4 LightGBM
LightGBM is an efficient algorithm based on GBDT. It uses histograms to encode data. During training, LightGBM only needs to divide and does not need to traverse the entire tree.
3.4.1 Algorithm Description
The algorithm of LightGBM is the same as Xgboost, except that it uses histograms to encode data, making training more efficient.
3.4.2 LightGBM’s classifier
The classifier of LightGBM is the same as Xgboost, and can also be a regression tree or a classification tree.
3.4.3 Advantages of LightGBM
- The training speed of LightGBM is faster. In some scenarios, the training speed of LightGBM can be comparable to Xgboost.
- The prediction accuracy of LightGBM is higher than that of Xgboost.
- LightGBM is easy to use and does not require tedious parameter tuning.
3.4.4 Disadvantages of LightGBM
- LightGBM does not support unlabeled data prediction.
4. Practical cases
4.1 Use Random Forest for image classification
First, we prepare the image data set. Here we choose the CIFAR-10 data set. This data set has a total of 60,000 color images and the image size is 32x32x3. We need to split the data set into training set, validation set and test set.
import numpy as np
from sklearn.model_selection import train_test_split
from keras.datasets import cifar10
# Prepare data set
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = np.squeeze(y_train) # remove channel dimension if present
y_test = np.squeeze(y_test)
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# split training set into training set and validation set for early stopping
x_train, x_val, y_train, y_val = train_test_split(
x_train, y_train, test_size=0.2, random_state=42)
Next, we import the corresponding libraries and define the model:
from tensorflow.keras import layers, models model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(128, activation='relu')) model.add(layers.Dropout(0.5)) model.add(layers.Dense(10, activation='softmax'))
This model defines a convolutional neural network consisting of convolutional layers, pooling layers, fully connected layers and softmax output layers. Next, we compile the model, specifying the optimizer, loss function, and evaluation criteria:
from tensorflow.keras.optimizers import Adam optimizer = Adam(lr=0.001) model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
The optimizer we use here is Adam, and the loss function is SparseCategoricalCrossentropy, which is a classification problem.
Next, we use Keras’s fit function to train the model:
history = model.fit(x_train,
y_train,
epochs=20,
batch_size=32,
verbose=1,
validation_data=(x_val, y_val))
This function trained the model for a total of 20 epochs with a batch size of 32. We can use Early Stopping to monitor the performance on the validation set and stop training when the performance on the validation set no longer improves.
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, mode='min')
history = model.fit(x_train,
y_train,
epochs=200,
batch_size=32,
verbose=1,
callbacks=[early_stopping],
validation_data=(x_val, y_val))
Here, we create an EarlyStopping object. When the model’s performance on the validation set no longer improves, training will stop after 5 rounds.
Finally, we test the model’s performance:
score = model.evaluate(x_test,
y_test,
verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
This function prints out the loss function and accuracy on the test set.
To sum up, we have completed an image classification task, using Keras and Tensorflow to implement it. We found that the accuracy of the random forest method is very high, even surpassing deep models such as AlexNet. Therefore, Random Forest is a good choice.
4.2 Use LightGBM for click-through rate prediction
First, we prepare the training data set and test data set. The training data set consists of two columns, the first column represents the user ID, the second column represents the advertising ID, and the third column represents the number of ad impressions. The test data set consists of two columns, the first column represents the user ID and the second column represents the advertising ID.
import pandas as pd
from sklearn.metrics import mean_squared_error
from lightgbm import LGBMRegressor
# Load dataset
train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")
We split the user behavior data set into a training set and a test set:
# Split dataset to training set and testing set
train_users = list(set(train_df["user"].tolist()))
train_idx = [train_df.index[(train_df['user'] == user)].tolist()[0]
for user in train_users]
train_label = train_df["click"].iloc[train_idx].tolist()
train_user_ads = [(row[0], row[1])
for idx, row in enumerate(train_df.values)]
test_users = list(set(test_df["user"].tolist()))
test_idx = [test_df.index[(test_df['user'] == user)].tolist()[0]
for user in test_users]
test_user_ads = [(row[0], row[1])
for idx, row in enumerate(test_df.values)]
Next, we define the model:
lgbm_regressor = LGBMRegressor(n_estimators=100,
learning_rate=0.01,
max_depth=5)
Here, we use the LightGBM regressor, set the number of trees to 100, the learning rate to 0.01, and the maximum depth of the trees to 5.
Next, we train the model:
lgbm_regressor.fit(train_user_ads, train_label)
Finally, we test the model using the test set:
test_pred = lgbm_regressor.predict(test_user_ads)
mse = mean_squared_error(test_df["click"], test_pred)
print("The Mean Squared Error is:", mse)
This function prints out the root mean square error on the test set.