ML_final_project

Authors: Sambhav Jain, Ferando Forero, Sahil Sachdev and Ana Ysasi.

Final project for Dr. Arnab Bose's Real Machine Learning & Predictive Analytics course at the University of Chicago's Master of Science in Analytics.

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Description:

In this project we aim to identify different fruits: apples, bananas, oranges and tomatoes through different Machine Learnign algorithms: CNN, XGBoost, InceptionV3 transfer learning and VGG16 transfer learning

Data:

Data retrieved from kaggle: Fruit Recognition. We exported the Folders: Apple, Banana, Orange and Tomato.

From this data, we also did a 20 % augmentation (image_augmentation.py). In total, with the image augmentation, we had 15325 train samples images (12834 train samples originally) and 400 test samples.

Example of the data with the different labels	Example of image augmentation

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Installation Guide

pip3 install gitpython

import os
from git.repo.base import Repo
Repo.clone_from("https://github.com/anapysasi/ML_final_project", "folderToSave")

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Quickstart Guide

Handeling the data.

File: train_test_split.py

This script create a train generator and labels the test data. It has two functions:

• train_generator_func(): uses ImageDataGenerator() and flow_from_directory from keras to create a train generator. The path= of the data, the shear_range, zoom_range, target_size and batch_size can be customize.
  If info=True return how many images are there in the gerator and from what classes. If plot=True plots 4 random images with its labels.
• test_label_func(): retunrs a vector with the files names(files), the labels of the classes for each corresponding file (test_label) and a vector with the corresponding fruit (test_label_fruit).

File: train_test_cv2.py

Has a function train_generator_func() that return the x_train, y_train, x_test, y_test from the dataset of images. Uses cv2.IMREAD_GRAYSCALE to read the images. You can customize the resize of the images (by changing the variables img_size1, img_size_2). The default size is (68,46).

The default of the function is to reshape the data and normailize it with StandardScaler(). This functionality can be trun of by setting reshaped=False as an argument.

By default it takes all the images available, including the ones form the data augmentation. If you set augmentation=False it would only use the origianl images to train the model.

File: image_augmentation.py

Creates a 20% of image augmention from the original dataset. It applies the following changes to the images:

• Horizontal and Vertical Shift Augmentation
• Random Rotation Augmentation
• Random Brightness Augmentation
• Random Zoom Augmentation

It saves the new imgages in '/data/augmentation/'. If plot=True it reurns an example of 9 images after the transofmrations are applied.

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CNN

File: cnn_complex_model.py

File that creates a more complex model by adding 28 layers to the CNN including Conv2D, LeakyRelu, Dropout and MaxPooling. The image size in this case is 256x256 and we tested the model with 3 epochs and 3 steps per epoch.

File: cnn_final_model.py

- This file requires TENSFORFLOW 2.1.0

File that creates a model with the best results using CNN with Keras. Adding 8 layers to the CNN including Conv2D, MaxPooling, Flatten and Dense layers.

Accuracy score: 0.701

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XGBoost

File: xgboost_simple_model.py

File that creates and saves a simple XGBoost model for the data. All the parameters are the default ones. The model is saves as xgboost_simple_model.pickle.dat.

File: xgboost_model.py

Creates a more complex XGBoost model, by switching some of the parameters. It uses RandomizedSearchCV() to decide what parameters fit the data best. It can use either the original data or the augmented data depending on the parameter augmentation, the dafault is True.

File: xgboost_eval.py

Evaluates the different XGBoost models. It opens one of the saved models (xgboost_simple_model.pickle.dat, xgboost_model.pickle.dat, xgboost_model2.pickle.dat, xgboost_model2_aug.pickle.dat) and fits the data (original or augmented).

It returns the Classification report, the Confusion matrix for the test and train data and the accuracy score of the model

Model: xgboost_simple_model.pickle.dat

Run with the default variables. No changes to the function.

• Learning Rate: 0.3
• Gamma: 0
• Max Depth: 6
• Subsample: 1
• Alpha: 0
• Lambda: 1
• Min Sum of Instance Weight to Make Child: 1
• Number of Trees: 100

[[ 1.   0.     0.     0.    ]
[ 0.51   0.42   0.06   0.01   ]
[ 0.36   0.07   0.57   0.     ]
[ 0.58   0.03   0.07   0.32 ]]

Accuracy score: 0.5775

Model: xgboost_model.pickle.dat

RandomizedSearchCV() with a dictionary of parameters. Best parameters:

• Learning Rate: 0.1
• Gamma:1
• Max Depth:7
• Subsample:0.7
• Alpha:1
• Lambda:3
• Min Sum of Instance Weight to Make Child: 7
• Number of Trees: 500

[[ 1.   0.     0.     0.    ]
[ 0.44   0.47   0.07   0.02   ]
[ 0.35   0.06   0.59   0.     ]
[ 0.53   0.03   0.03   0.41 ]]

Accuracy score: 0.6175

Model: xgboost_model2.pickle.dat

RandomizedSearchCV() based on the results of the previous model.

• Learning Rate: 0.1
• Gamma:1
• Max Depth:6
• Subsample:0.6
• Alpha:1
• Lambda:2.5
• Min Sum of Instance Weight to Make Child: 10
• Number of Trees: 600

[[ 1.   0.     0.     0.    ]
[ 0.41   0.52   0.06   0.01   ]
[ 0.33   0.03   0.64   0.     ]
[ 0.5   0.04   0.03   0.43 ]]

Accuracy score: 0.6475

Model: xgboost_model2_augm.pickle.dat

RandomizedSearchCV() based on the results of the previous model + image augmentation

• Learning Rate: 0.1
• Gamma:1
• Max Depth:6
• Subsample:0.6
• Alpha:1
• Lambda:2.5
• Min Sum of Instance Weight to Make Child: 10
• Number of Trees: 800

[[ 1.   0.     0.     0.    ]
[ 0.41   0.56   0.03   0.     ]
[ 0.29   0.08   0.63   0.     ]
[ 0.49   0.02   0.03   0.46 ]]

Accuracy score: 0.6625

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InceptionV3 transfer learning

File: transfer_learning.py

File that creates and saves an InceptionV3 transfer learning model for the data. The base model is loaded and the top layers are added. One GlobalAveragePooling2D layer and two Dense layers. The model is saved as inceptionv3_transferlearning_v2.

File: transfer_learning_eval.py

Loads and evaluates an InveptionV3 model. It prints the Accuracy Score, Classification report, the Confusion matrix for the test and train data for the model that is loaded.

[[ 0.37   0.00   0.63   0.00  ]
[ 0.23   0.00   0.77   0.02  ]
[ 0.25   0.00   0.75   0.00  ]
[ 0.41   0.00   0.59   0.00 ]]

Accuracy score: 0.2800

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VGG16 transfer learning

File: transfer_learning_vgg.py

File that creates and saves a VGG16 transfer learning model's weights for the data. The base model is loaded and the top layers are added. One Flatten layer, two Dense, and one Dropout layer. The model weights are saved as t1_model_v1.weights.best.hdf5.

File: transfer_learning_vgg_eval.py

Loads and evaluates the VGG16 model weights. It prints the Accuracy Score, Classification report, the Confusion matrix for the test and train data for the model that is loaded.

[[ 0.99   0.00   0.00   0.01  ]
[ 0.00   1.00   0.00   0.00  ]
[ 0.00   0.00   1.00   0.00  ]
[ 0.01   0.00   0.00   0.99 ]]

Accuracy score: 0.9950