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svm.py
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svm.py
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# -*- coding: utf-8 -*-
"""
Created on Sun May 17 17:54:11 2020
@author: louis
"""
#%%
import numpy as np
import pandas as pd
import tensorflow as tf
import pickle
from keras.preprocessing.image import ImageDataGenerator
from sklearn import svm, metrics, datasets
from sklearn.utils import Bunch
from sklearn.model_selection import GridSearchCV, train_test_split, StratifiedShuffleSplit
from joblib import dump, load
from preprocessing import convolution2D, laplacian_of_gaussian_33, median_cut
from sklearn import preprocessing
from skimage.io import imread
from skimage.transform import resize
from pathlib import Path
from skimage.color import rgb2gray
#%%
def load_image_files(container_path, dimension=(64, 64, 3)):
"""
Load image files with categories as subfolder names
which performs like scikit-learn sample dataset
Parameters
----------
container_path : string or unicode
Path to the main folder holding one subfolder per category
dimension : tuple
size to which image are adjusted to
Returns
-------
Bunch
"""
image_dir = Path(container_path)
folders = [directory for directory in image_dir.iterdir() if directory.is_dir()]
categories = [fo.name for fo in folders]
descr = "A image classification dataset"
images = []
flat_data = []
target = []
for i, direc in enumerate(folders):
for file in direc.iterdir():
img = imread(file)
img_resized = resize(img, dimension, anti_aliasing=True, mode='reflect')
flat_data.append(img_resized.flatten())
images.append(img_resized)
target.append(i)
flat_data = np.array(flat_data)
target = np.array(target)
images = np.array(images)
return Bunch(data=flat_data,
target=target,
target_names=categories,
images=images,
DESCR=descr)
#%%
train_set = load_image_files("C:/Users/louis/Documents/Louis/ecole/2A/Méthodes d'apprentissage/chest_xray/train")
test_set = load_image_files("C:/Users/louis/Documents/Louis/ecole/2A/Méthodes d'apprentissage/chest_xray/test")
#%%
scaler = preprocessing.StandardScaler().fit(train_set.data)
normed_train_set = scaler.transform(train_set.data,True)
normed_test_set = scaler.transform(test_set.data, True)
#%%
gray_train_set = [rgb2gray(img) for img in train_set.images]
gray_test_set = [rgb2gray(img) for img in test_set.images]
#%%
laplacian_train_set = [convolution2D(img, laplacian_of_gaussian_33).flatten() for img in gray_train_set]
laplacian_test_set = [convolution2D(img, laplacian_of_gaussian_33).flatten() for img in gray_test_set]
#%%
median_cut_train_set = [median_cut(img).flatten() for img in gray_train_set]
median_cut_test_set = [median_cut(img).flatten() for img in gray_test_set]
#%%
param_grid = [
{'C': [1, 10, 100, 1000], 'gamma': [0.0001,0.001, 0.01], 'kernel': ['rbf']},
]
svc = svm.SVC()
clf = GridSearchCV(svc, param_grid)
clf.fit(median_cut_train_set, train_set.target)
#%%
dump(clf, 'median_cut_grid_search.joblib')
results = pd.DataFrame(clf.cv_results_)
#%%
clf = load('trained_grid_search.joblib')
results = pd.DataFrame(clf.cv_results_)
#%%
pred = clf.predict(median_cut_test_set)
#%%
print(metrics.accuracy_score(test_set.target, pred))
print(metrics.f1_score(test_set.target, pred))
print(metrics.confusion_matrix(test_set.target, pred))
#%%
param_grid2 = [{'C': [100, 1000, 10000], 'gamma': [0.01,0.001], 'kernel': ['rbf']}]
clf2 = GridSearchCV(svc, param_grid2)
clf2.fit(train_set.data, train_set.target)
#%%
dump(clf2, 'trained_grid_search2.joblib')
#%%
results2 = pd.DataFrame(clf2.cv_results_)
#%%
pred = clf2.predict(test_set.data)
print(metrics.accuracy_score(test_set.target, pred))
print(metrics.f1_score(test_set.target, pred))
print(metrics.confusion_matrix(test_set.target, pred))
#%%
param_grid3 = [{'C': [100, 1000], 'gamma': [0.01,0.001], 'kernel': ['rbf'], 'class_weight' : ['balanced'] }]
clf3 = GridSearchCV(svc, param_grid3)
clf3.fit(train_set.data, train_set.target)
#%%
dump(clf3, 'trained_grid_search3.joblib')
#%%
results3 = pd.DataFrame(clf3.cv_results_)
pred = clf3.predict(test_set.data)
print(metrics.accuracy_score(test_set.target, pred))
print(metrics.f1_score(test_set.target, pred))
print(metrics.confusion_matrix(test_set.target, pred))
#%%
param_grid4 = [{'C': [100, 1000], 'gamma': [0.001, 0.0001], 'kernel': ['rbf']}]
clf4 = GridSearchCV(svc, param_grid4)
clf4.fit(train_set.data, train_set.target)
#%%
dump(clf4, 'trained_grid_search4.joblib')
#%%
clf6 = svm.SVC(C=100000000, gamma= 0.1).fit(median_cut_train_set, train_set.target)
#%%
pred = clf6.predict(median_cut_test_set)
print(metrics.accuracy_score(test_set.target, pred))
print(metrics.f1_score(test_set.target, pred))
print(metrics.confusion_matrix(test_set.target, pred))
#%%
C_range = np.logspace(-2, 10, 13)
gamma_range = np.logspace(-9, 3, 13)
param_grid5 = dict(gamma=gamma_range, C=C_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=42)
clf5 = GridSearchCV(svc, param_grid5, cv = cv)
clf5.fit(train_set.data, train_set.target)
clf5 = svm.SVC(C= 10000, gamma= 0.0001, kernel = 'rbf')