# Tensorflow implementation of DeepFM for CTR prediction

# tensorflow-DeepFM

This project includes a Tensorflow implementation of DeepFM [1].

# NEWS

- A modified version of DeepFM is used to win the 4th Place for Mercari Price Suggestion Challenge on Kaggle. See the slide here how we deal with fields containing sequences, how we incoporate various FM components into deep model.

# Usage

## Input Format

This implementation requires the input data in the following format:

- [ ]
**Xi**:*[[ind1_1, ind1_2, ...], [ind2_1, ind2_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]**indi_j*is the feature index of feature field*j*of sample*i*in the dataset

- [ ]
**Xv**:*[[val1_1, val1_2, ...], [val2_1, val2_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]**vali_j*is the feature value of feature field*j*of sample*i*in the dataset*vali_j*can be either binary (1/0, for binary/categorical features) or float (e.g., 10.24, for numerical features)

- [ ]
**y**: target of each sample in the dataset (1/0 for classification, numeric number for regression)

Please see `example/DataReader.py`

an example how to prepare the data in required format for DeepFM.

## Init and train a model

```
import tensorflow as tf
from sklearn.metrics import roc_auc_score
# params
dfm_params = {
"use_fm": True,
"use_deep": True,
"embedding_size": 8,
"dropout_fm": [1.0, 1.0],
"deep_layers": [32, 32],
"dropout_deep": [0.5, 0.5, 0.5],
"deep_layers_activation": tf.nn.relu,
"epoch": 30,
"batch_size": 1024,
"learning_rate": 0.001,
"optimizer_type": "adam",
"batch_norm": 1,
"batch_norm_decay": 0.995,
"l2_reg": 0.01,
"verbose": True,
"eval_metric": roc_auc_score,
"random_seed": 2017
}
# prepare training and validation data in the required format
Xi_train, Xv_train, y_train = prepare(...)
Xi_valid, Xv_valid, y_valid = prepare(...)
# init a DeepFM model
dfm = DeepFM(**dfm_params)
# fit a DeepFM model
dfm.fit(Xi_train, Xv_train, y_train)
# make prediction
dfm.predict(Xi_valid, Xv_valid)
# evaluate a trained model
dfm.evaluate(Xi_valid, Xv_valid, y_valid)
```

You can use early_stopping in the training as follow

```
dfm.fit(Xi_train, Xv_train, y_train, Xi_valid, Xv_valid, y_valid, early_stopping=True)
```

You can refit the model on the whole training and validation set as follow

```
dfm.fit(Xi_train, Xv_train, y_train, Xi_valid, Xv_valid, y_valid, early_stopping=True, refit=True)
```

You can use the FM or DNN part only by setting the parameter `use_fm`

or `use_dnn`

to `False`

.

## Regression

This implementation also supports regression task. To use DeepFM for regression, you can set `loss_type`

as `mse`

. Accordingly, you should use eval_metric for regression, e.g., mse or mae.

# Example

Folder `example`

includes an example usage of DeepFM/FM/DNN models for Porto Seguro's Safe Driver Prediction competition on Kaggle.

Please download the data from the competition website and put them into the `example/data`

folder.

To train DeepFM model for this dataset, run

```
$ cd example
$ python main.py
```

Please see `example/DataReader.py`

how to parse the raw dataset into the required format for DeepFM.

## Performance

### DeepFM

### FM

### DNN

## Some tips

- [ ] You should tune the parameters for each model in order to get reasonable performance.
- [ ] You can also try to ensemble these models or ensemble them with other models (e.g., XGBoost or LightGBM).

# Reference

[1] *DeepFM: A Factorization-Machine based Neural Network for CTR Prediction*, Huifeng Guo, Ruiming Tang, Yunming Yey, Zhenguo Li, Xiuqiang He.

# Acknowledgments

This project gets inspirations from the following projects:

- [ ] He Xiangnan's neural_factorization_machine
- [ ] Jian Zhang's YellowFin (yellowfin optimizer is taken from here)

## GitHub

### Comments

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