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Listing Page Classifier Progress (view source)
Revision as of 19:37, 17 May 2019
, 19:37, 17 May 2019→Progress Log
**WebDriverException: invalid session id occurred during the iteration (solved it by not closing the driver each time)
*test run site map
**BFS takes much more time than DFS when depth is big(trying to fix will look into this later)
'''4/22/2019'''
**e.g. https://bunkerlabs.org/
**get the scroll height before running headless browsers (Nope, doesn’t work)
**trying try out a different package ‘splinter’
https://splinter.readthedocs.io/en/latest/screenshot.html
'''4/23/2019'''
*Implement new screenshot tool (splinter package):
**Reading all text files from one directory, and take screenshot of each url from individual text files in that directory
**Filename modification (e.g. test7z_0i96__.png, autogenerates file name)
**Documentation on wiki
'''4/24/2019'''
*Documentation on wiki
*went back to the time complexity issue with BFS and DFS
**DFS algorithm has flaws!! (it does not visit all nodes, this is why DFS is much faster)
**need to look into the problem with the DFS tomorrow
'''4/25/2019'''
Site map:
*the recursive DFS will not work in this type of problem, and if we rewrite it in an iterative way, it will be similar to the BFS approach. So, I decided to only keep the BFS since the BFS is working just fine.
*Implement the BFS algorithm: trying out deque etc. to see if it runs faster
'''4/29/2019'''
*Image processing work assigned
*Documentation on wiki
'''4/30/19'''
Image Processing:
*Research on 3 packages for setting up CNN
**Comparison between the 3 packages: https://kite.com/blog/python/python-machine-learning-libraries
***Scikit: good for small dataset, easy to use. Does not support GPU computation
***Pytorch: Coding is easy, so it has a flatter learning curve, Supports dynamic graphs so you can adjust on-the-go, Supports GPU acceleration.
***TensorFlow: Flexibility, Contains several ready-to-use ML models and ready-to-run application packages, Scalability with hardware and software, Large online community, Supports only NVIDIA GPUs, A slightly steep learning curve
*Initiate the idea of data preprocessing: create proper input dataset for the CNN model
'''5/2/2019'''
*Work on data preprocessing
'''5/6/2019'''
*Keep working on data preprocessing
*Generate screenshot
'''5/7/2019'''
*some issues occurred during screenshot generating (Will work on this more tomorrow)
*try to set up CNN model
**https://www.datacamp.com/community/tutorials/cnn-tensorflow-python
'''5/8/2019'''
*fix the screenshot tool by switching to Firefox
*Data preprocessing
'''5/12/2019'''
*Finish image data preprocessing
'''5/13/2019'''
*Set up initial CNN model using Keras
**issue: Keras freezes on last batch of first epoch, make sure the following:
steps_per_epoch = number of train samples//batch_size
validation_steps = number of validation samples//batch_size
'''5/14/2019'''
*Implement the CNN model
*Work on some changes in the data preprocessing part (image data)
**place class label in image filename
'''5/15/2019'''
*Correct some out-of-date data in <code>The File to Rule Them ALL.csv</code>, new file saved as <code>The File to Rule Them ALL_NEW.csv</code>
*implement generate_dataset.py and sitmap tool
**regenerate dataset using updated data and tool
'''5/16/2019'''
*implementation on CNN
*Some problems to consider:
**some websites have more than 1 cohort page: a list of cohorts for each year
**class label is highly imbalanced:
https://towardsdatascience.com/deep-learning-unbalanced-training-data-solve-it-like-this-6c528e9efea6
'''5/17/2019'''
*have to go back with the old plan of separating image data :(
*documentation on wiki
*test run on the GPU server
