A traffic demo for ASC17

demo/traffic_prediction/README

+run by:
+cd ./data
+sh get_data.sh
+cd ..
+sh train.sh
+sh predict.sh
+

demo/traffic_prediction/data/get_data.sh

+#!/bin/bash
+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -e
+set -x
+
+DIR="$( cd "$(dirname "$0")" ; pwd -P )"
+cd $DIR
+
+#download the dataset
+echo "Downloading traffic data..."
+wget http://paddlepaddle.bj.bcebos.com/demo/traffic/traffic_data.tar.gz
+
+#extract package
+echo "Unzipping..."
+tar -zxvf traffic_data.tar.gz
+
+echo "data/speeds.csv" >> train.list
+echo "data/speeds.csv" >> test.list
+echo "data/speeds.csv" >> pred.list
+
+echo "Done."

demo/traffic_prediction/dataprovider.py

+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.trainer.PyDataProvider2 import *
+import sys
+import numpy as np
+TERM_NUM = 24
+FORECASTING_NUM = 25
+LABEL_VALUE_NUM = 4
+def initHook(settings, file_list, **kwargs):
+    """
+    Init hook is invoked before process data. It will set obj.slots and store data meta.
+
+    :param settings: global object. It will passed to process routine.
+    :type obj: object
+    :param file_list: the meta file object, which passed from trainer_config.py,but unused in this function.
+    :param kwargs: unused other arguments.
+    """
+    del kwargs #unused 
+   
+    settings.pool_size = sys.maxint
+    #Use a time seires of the past as feature.
+    #Dense_vector's expression form is [float,float,...,float]
+    settings.slots = [dense_vector(TERM_NUM)]
+    #There are next FORECASTING_NUM fragments you need predict.
+    #Every predicted condition at time point has four states.
+    for i in range(FORECASTING_NUM):
+        settings.slots.append(integer_value(LABEL_VALUE_NUM))
+
+@provider(init_hook=initHook, cache=CacheType.CACHE_PASS_IN_MEM, should_shuffle=True)
+def process(settings, file_name):
+    with open(file_name) as f:
+        #abandon fields name
+        f.next()
+        for row_num, line in enumerate(f): 
+            speeds = map(int,line.rstrip('\r\n').split(",")[1:])
+            # Get the max index.
+            end_time = len(speeds)
+            # Scanning and generating samples
+            for i in range(TERM_NUM,end_time - FORECASTING_NUM):
+                # For dense slot
+                pre_spd = map(float,speeds[i-TERM_NUM:i])
+
+                # Integer value need predicting, values start from 0, so every one minus 1.
+                fol_spd  = [i-1 for i in speeds[i:i + FORECASTING_NUM]]
+                
+                # Predicting label is missing, abandon the sample.
+                if -1 in fol_spd:
+                    continue
+                yield [pre_spd] + fol_spd
+
+def predict_initHook(settings, file_list, **kwargs):
+    settings.pool_size = sys.maxint
+    settings.slots = [dense_vector(TERM_NUM)]
+
+@provider(init_hook=predict_initHook,should_shuffle=False)
+def process_predict(settings, file_name):
+    with open(file_name) as f:
+        #abandon fields name
+        f.next()
+        for row_num, line in enumerate(f):
+            speeds = map(int,line.rstrip('\r\n').split(","))
+            end_time = len(speeds)
+            pre_spd = map(float,speeds[end_time-TERM_NUM:end_time])
+            yield pre_spd
+

demo/traffic_prediction/gen_result.py

+res = []
+with open('./rank-00000') as f:
+    for line in f:
+        pred = map(int,line.strip('\r\n;').split(";"))
+        #raw prediction range from 0 to 3
+        res.append([i+1 for i in pred])
+
+file_name = open('./data/pred.list').read().strip('\r\n')
+
+FORECASTING_NUM=24
+header=['id',
+        '201604200805',
+        '201604200810',
+        '201604200815',
+        '201604200820',
+        '201604200825',
+        '201604200830',
+        '201604200835',
+        '201604200840',
+        '201604200845',
+        '201604200850',
+        '201604200855',
+        '201604200900',
+        '201604200905',
+        '201604200910',
+        '201604200915',
+        '201604200920',
+        '201604200925',
+        '201604200930',
+        '201604200935',
+        '201604200940',
+        '201604200945',
+        '201604200950',
+        '201604200955',
+        '201604201000',
+        ]
+###################
+## To CSV format ##
+###################
+with open(file_name) as f:
+    f.next()
+    print ','.join(header)
+    for row_num, line in enumerate(f):
+        fields = line.rstrip('\r\n').split(',')
+        linkid = fields[0]
+        print linkid+','+','.join(map(str,res[row_num]))
+

demo/traffic_prediction/predict.sh

+#!/bin/bash
+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+set -e
+
+cfg=trainer_config.py
+# pass choice 
+model="output/pass-00000"
+paddle train \
+    --config=$cfg \
+    --use_gpu=false \
+    --job=test \
+    --init_model_path=$model \
+    --config_args=is_predict=1 \
+    --predict_output_dir=. 
+
+python gen_result.py > result.txt
+
+rm -rf rank-00000

demo/traffic_prediction/train.sh

+#!/bin/bash
+# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+set -e
+
+cfg=trainer_config.py
+#TRAINER_BIN="./paddle_trainer"
+paddle train \
+  --config=$cfg \
+  --save_dir=./output \
+  --trainer_count=4 \
+  --log_period=1000 \
+  --dot_period=10 \
+  --num_passes=10 \
+  --use_gpu=false \
+  --show_parameter_stats_period=3000 \
+  --test_wait=1
+  #--test_all_data_in_one_period=1 \
+  2>&1 | tee 'train.log'

demo/traffic_prediction/trainer_config.py

+#!/usr/bin/env/python
+#-*python-*-
+from paddle.trainer_config_helpers import *
+
+
+################################### DATA Configuration #############################################
+is_predict = get_config_arg('is_predict', bool, False)
+trn = './data/train.list' if not is_predict else None
+tst = './data/test.list' if not is_predict else './data/pred.list'
+process = 'process' if not is_predict else 'process_predict'
+define_py_data_sources2(train_list=trn,
+                        test_list=tst,
+                        module="dataprovider",
+                        obj=process)
+################################### Parameter Configuaration #######################################
+TERM_NUM=24
+FORECASTING_NUM= 25
+emb_size=16
+batch_size=128 if not is_predict else 1
+settings(
+    batch_size = batch_size,
+    learning_rate = 1e-3,
+    learning_method = RMSPropOptimizer()
+)
+################################### Algorithm Configuration ########################################
+
+output_label = []
+
+link_encode = data_layer(name='link_encode', size=TERM_NUM)
+for i in xrange(FORECASTING_NUM):
+    # Each task share same weight.
+    link_param = ParamAttr(name='_link_vec.w', initial_max=1.0, initial_min=-1.0)
+    link_vec = fc_layer(input=link_encode,size=emb_size, param_attr=link_param)
+    score = fc_layer(input=link_vec, size=4, act=SoftmaxActivation())
+    if is_predict:
+        maxid = maxid_layer(score)
+        output_label.append(maxid)
+    else:
+        # Multi-task training.
+        label = data_layer(name='label_%dmin'%((i+1)*5), size=4)
+        cls = classification_cost(input=score,name="cost_%dmin"%((i+1)*5), label=label)
+        output_label.append(cls)
+outputs(output_label)