RovOpt/rov_train_paddle.py

import pandas as pd
import numpy as np
import _pickle as cPickle
from paddle.io import Dataset
from paddle import paddle

class RovDataset(Dataset):
    def __init__(self, path):
        super(RovDataset, self).__init__()
        self.path = path

    def _parse_dataset(self):
        self.data = []
        self.labels = []
        with open(self.path, "rb") as input_file:
            df = cPickle.load(input_file)
            y = df['futre7dayreturn']
            df_vids = df['videoid']
            x = df.drop(['videoid', 'videotags', 'words_without_tags', 'dt'], axis=1)
            x = x.drop(['futre7dayreturn'], axis=1)
            features = list(x)
            drop_features = [f for f in features if (f.find('day30')!=-1 or f.find('day60')!=-1)]
            x = x.drop(drop_features, axis=1)
            x = x.apply(lambda x: (x - np.min())/ (np.max(x) - np.min(x)))
            #features = [f for f in features if f not in drop_features]
            self.data = x
            self.labels = y
            #return x, y , df_vids, features

    def __getitem__(self, idx):
        data, label = self.data.iloc[idx], self.labels.iloc[idx]
        return data.astype('float21'), label.astype('float32')

    def __len__(self):
        return len(self.labels)


def train():
    feature_dim = 0
    result_dim = 1
    batch_size = 100

    train_dataset = RovDataset("train_data.pickle")
    test_dataset = RovDataset("predict_data.pickle")

    linear=paddle.nn.Sequential(
            paddle.nn.Linear(feature_dim, 4096),
            paddle.nn.ReLU(),
            paddle.nn.Linear(4096, 1024),
            paddle.nn.ReLU(),
            paddle.nn.Dropout(0.2),
            paddle.nn.Linear(1024,19),
            paddle.nn.ReLU(),
            paddle.nn.Linear(19,1)
            )

    model=paddle.Model(linear)
    model.prepare(paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()),
                paddle.nn.MSELoss())
    model.fit(train_dataset, epochs=3, batch_size=batch_size, verbose=1)

    model.evaluate(test_dataset,batch_size=batch_size,verbose=1)
    

def pack_result(y_, y, vid, fp):
    #y_ = y_.astype(int)
    y_.reshape(len(y_),1) 
    df = pd.DataFrame(data=y_, columns=['score'])
    if len(vid) >0:
        df['vid'] = vid
    df['y'] = y
    df = df.sort_values(by=['score'], ascending=False)
    df.to_csv(fp, index=False)

    
if __name__ == '__main__':

    #train
    pass