class my_classification_model:
def __init__(self, model_name, no_class):
self.model_name = model_name
self.no_class = no_class
if model_name == 'lr':
self.model = LogisticRegression()
if model_name == 'sv':
self.model = SVC()
if model_name == 'rf':
self.model = RandomForestClassifier()
if model_name == 'dnn1':
self.model = keras.Sequential([
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(self.no_class, activation='softmax')
])
self.model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
if model_name == 'dnn2':
self.model = keras.Sequential([
keras.layers.Dense(256, activation='relu'),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(self.no_class, activation='softmax')
])
self.model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
def fit(self, X_train, y_train):
if model_name in ['lr', 'sv', 'rf']:
self.model.fit(X_train, y_train)
if model_name in ['dnn1', 'dnn2']:
self.model.fit(X_train, y_train, epochs=150, verbose=0)
def predict(self, X_test):
if model_name in ['lr', 'sv', 'rf']:
return self.model.predict(X_test)
if model_name in ['dnn1', 'dnn2']:
return np.argmax(self.model.predict(X_test), axis=1)
def predict_s(self, X_test):
if model_name == 'sv':
return self.model.predict(X_test)
if model_name in ['lr', 'rf']:
return self.model.predict_proba(X_test) @ np.arange(self.no_class)
if model_name in ['dnn1', 'dnn2']:
return self.model.predict(X_test) @ np.arange(self.no_class)
def main():
width = 128
height = 128
depth = 3
classes = 2
NUM_EPOCHS = 50
#initialize the optimizer and model
opt = tf.keras.optimizers.SGD(lr=0.01)
project_dir = "deepfake-detection-challenge"
#train_metadata, train_videos, labels, originals = load_json(project_dir)
train_sub_dir = "/train_sample_videos/"
dest_train_1 = '/train_1/'
#break_to_frames_train(project_dir, train_videos, labels, width, height)
#test_video_names, test_videos = load_test_videos(project_dir)
test_sub_dir = "/test_videos/"
dest_test_1 = '/test_1/'
#break_to_frames_test(project_dir, test_videos, width, height)
train_new_csv = make_dataframe_train(project_dir)
test_new_csv = make_dataframe_test(project_dir)
train_new_csv = '/train_new.csv'
X_train, y_train, X_test, y_test, train, y_train_original, y_test_original = get_Xy(project_dir,train_new_csv, width, height, depth)
#Normalization
X_train = X_train.astype("float")/ 255.0
X_test = X_test.astype ("float")/ 255.0
#One hot encode y
choice = 4
if choice == 1: #not working
base_model = vgg16Model(X_train, X_test, width, height, depth, classes)
# checkpointing to save the weights of best model
mcp_save = tf.keras.callbacks.ModelCheckpoint('weight.hdf5', save_best_only=True, monitor='val_loss', mode='min')
# compiling the model
base_model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
# training the model
H = base_model.fit(X_train, y_train, epochs=50, validation_data=(X_test, y_test), callbacks=[mcp_save], batch_size=128)
print ("Base Model - Test Data Loss and Accuracy: ", model.evaluate(X_test, y_test))
print("Final Plot ")
plotAccLoss(H, NUM_EPOCHS)
if choice == 2:
# Feature Extraction and Usage of Secondary Model
vggModel = tf.keras.applications.VGG16(weights='imagenet', include_top=False, input_shape=(width, height, depth))
print(vggModel.summary())
X_train_new = vggModel.predict(X_train)
X_train_new = X_train_new.reshape(X_train_new.shape[0], -1)
X_val_new = vggModel.predict(X_test)
X_val_new = X_val_new.reshape(X_val_new.shape[0], -1)
secondary_model = 'random_forest'
if (secondary_model == 'random_forest'):
print("Secondary Model - Random Forest ")
model = RandomForestClassifier(200)
model.fit(X_train_new, y_train)
# evaluate the model
results = model.predict(X_val_new)
print ("Random Forest Accuracy ", metrics.accuracy_score(results, y_test))
if(secondary_model == 'naive_bayes'):
print("Secondary Model - Using Naive Bayes")
nBayes = GaussianNB()
nBayes = nBayes.fit( X_train_new , y_train)
accuracy = nBayes.score(X_val_new, y_test)
print ("Naive Bayes Accuracy ", accuracy)
if choice == 3:
# not working
# FineTuning
inceptionV3Model= tf.keras.applications.InceptionV3(weights = 'imagenet',include_top =False, input_shape =(width, height,depth))
inceptionV3Model.trainable = False
model =tf.keras.models.Sequential()
model.add (inceptionV3Model)
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dropout (0.5))
model.add(tf.keras.layers.Dense (256, 'relu'))
model.add(tf.keras.layers.Dense (classes, activation='sigmoid'))
print (model.summary)
NUM_EPOCHS =50
opt = tf.keras.optimizers.SGD(lr=0.001)
model.compile(loss="sparse_categorical_crossentropy", optimizer=opt,metrics=["accuracy"])
H = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))
plotAccLoss(H, NUM_EPOCHS)
print ("\n Phase B - Fine Tune Fully Connected Layer and Selected Convolutional Layers \n")
inceptionV3Model.trainable = True
trainableFlag = False
for layer in inceptionV3Model.layers:
if layer.name == 'block4_conv1':
trainableFlag = True
layer.trainable = trainableFlag
opt = tf.keras.optimizers.SGD(lr=0.00001)
model.compile(loss="sparse_categorical_crossentropy", optimizer=opt,metrics=["accuracy"])
print (model.summary)
H = model.fit(trainX, trainY, epochs=NUM_EPOCHS, batch_size=32, validation_data=(testX, testY))
print("Final Plot ")
plotAccLoss(H, NUM_EPOCHS)
if choice == 4:
# works
# Feature Extraction and Usage of Secondary Model
inceptionV3Model= tf.keras.applications.InceptionV3(weights = 'imagenet',include_top =False, input_shape =(width, height,depth))
inceptionV3Model.trainable = False
print(inceptionV3Model.summary())
X_train_new = inceptionV3Model.predict(X_train)
X_train_new = X_train_new.reshape(X_train_new.shape[0], -1)
X_val_new = inceptionV3Model.predict(X_test)
X_val_new = X_val_new.reshape(X_val_new.shape[0], -1)
secondary_model = 'random_forest'
if(secondary_model == 'random_forest'):
print("Secondary Model - Random Forest ")
model = RandomForestClassifier(200)
model.fit(X_train_new, y_train)
# evaluate the model
accuracy = evaluate(model, X_val_new, y_test)
# results = model.predict(X_val_new)
# print ("Random Forest Accuracy ", metrics.accuracy_score(results, y_test))
print("Random Forest Accuracy ", accuracy)
if(secondary_model == 'naive_bayes'):
print("Secondary Model - Using Naive Bayes")
nBayes = GaussianNB()
nBayes = nBayes.fit( X_train_new , y_train)
accuracy = nBayes.score(X_val_new, y_test)
print ("Naive Bayes Accuracy ", accuracy)
if choice == 41:
# works
# Feature Extraction and Usage of Secondary Model
inceptionV3Model= tf.keras.applications.InceptionV3(weights = 'imagenet',include_top =False, input_shape =(width, height,depth))
inceptionV3Model.trainable = False
print(inceptionV3Model.summary())
X_train_new = inceptionV3Model.predict(X_train)
X_train_new = X_train_new.reshape(X_train_new.shape[0], -1)
X_val_new = inceptionV3Model.predict(X_test)
print("X_val_new b4 reshaping ", X_val_new)
X_val_new = X_val_new.reshape(X_val_new.shape[0], -1)
secondary_model = 'random_forest'
if(secondary_model == 'random_forest'):
print("Secondary Model - Random Forest ")
model = RandomForestClassifier(200)
model.fit(X_train_new, y_train)
# evaluate the model
predY = model.predict(X_val_new)
#accuracy on the images
print ("Images - Random Forest Accuracy ", metrics.accuracy_score(predY, y_test))
#name of the video , label in X_val_new
#collect all the images/group by all iamges with same irst name and count probability , if out of 11 frames atleast 3 are fake, then video is fake`
# storing the images and their class in a dataframe
# print("train.head() ", train.head(), train.shape )
# print("y_test ", y_test, y_test.shape )
# print("predY ", predY, predY.shape )
# print("predY[:,0] ", predY[:,0]) #this a series
# print("X_val_new ", X_val_new,X_val_new.shape )
# pred_data_frame = train.copy(deep=True)
# video_names = []
# image_names = train['image']
# for i in range(len(image_names)):
# #get the video name from the frame e.g. aagfhgtpmv.mp4_frame0.jpg
# video_names.append(image_names[i].split("_")[0])
# pred_data_frame['video'] = video_names
# print("pred_data_frame.head() ", pred_data_frame.head())
# pred_data_frame['pred_image_fake'] = predY[:,0]
# pred_data_frame['pred_image_real'] = predY[:,1]
# pred_video_label1 = []
# # #sort the df based on video names
# # pred_data_frame = pred_data_frame.sort_values(by=['video'])
# pred_video_label = pred_data_frame.groupby(['video'])['pred_image_label'].count()
# print(pred_video_label.head())
# print ("Video Classification Accuracy ", metrics.accuracy_score(predY, y_test))
# if(secondary_model == 'naive_bayes'):
# print("Secondary Model - Using Naive Bayes")
# nBayes = GaussianNB()
# nBayes = nBayes.fit( X_train_new , y_train)
# accuracy = nBayes.score(X_val_new, y_test)
# print ("Naive Bayes Accuracy ", accuracy)
if choice == 5:
#lstm
model = Sequential()
model.add(LSTM(256,dropout=0.2,input_shape=(train_data.shape[1],train_data.shape[2])))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(5, activation='softmax'))
sgd = SGD(lr=0.00005, decay = 1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
#model.load_weights('video_1_LSTM_1_512.h5')
callbacks = [ EarlyStopping(monitor='val_loss', patience=10, verbose=0), ModelCheckpoint('video_1_LSTM_1_1024.h5', monitor='val_loss', save_best_only=True, verbose=0) ]
nb_epoch = 500
model.fit(train_data,train_labels,validation_data=(validation_data,validation_labels),batch_size=batch_size,nb_epoch=nb_epoch,callbacks=callbacks,shuffle=True,verbose=1)
return model
if choice ==6:
#ensemble
vggModel= tf.keras.applications.VGG16 (weights = 'imagenet',include_top =False, input_shape =(128, 128,3))
model1 = tf.keras.models.Sequential()
model1.add (vggModel)
model1.add(tf.keras.layers.Flatten())
model1.add(tf.keras.layers.Dropout (0.5))
model1.add(tf.keras.layers.Dense (256, 'relu'))
model1.add(tf.keras.layers.Dense (17, activation='softmax'))
inceptionv3model= tf.keras.applications.InceptionV3(weights = 'imagenet',include_top =False, input_shape =(128, 128,3))
model2 = tf.keras.models.Sequential()
model2.add(inceptionv3model)
model2.add(tf.keras.layers.Flatten())
model2.add(tf.keras.layers.Dropout (0.5))
model2.add(tf.keras.layers.Dense (256, 'relu'))
model2.add(tf.keras.layers.Dense (17, activation='softmax'))
model_name = 'knn'
if(model_name == 'randomforest'):
model = RandomForestClassifier(200)
model.fit(featuresTrain, trainY)
# evaluate the model
results = model.predict(featuresVal)
print (metrics.accuracy_score(results, testY))
if(model_name == 'knn'):
print("using knn")
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(featuresTrain, trainY)
knn.predict(featuresVal)
results = knn.predict(featuresVal)
print (metrics.accuracy_score(results, testY))
if(model_name == 'naive_bayes'):
print("Using Naive Bayes")
nBayes = GaussianNB()
nBayes = nBayes.fit( featuresTrain , trainY)
accuracy = nBayes.score(featuresVal, testY)
print ("Naive Bayes Accuracy ", accuracy)
if(model_name == 'svm'):
print("Using SVM")
svc = SVC(gamma='auto')
svc = svc.fit(featuresTrain, trainY)
# accuracy = svc.score(test_features, test_labels)
accuracy = evaluate(svc, featuresVal, testY)
print ("SVM Accuracy ", accuracy)
# resnet50model = tf.keras.applications.resnet50(weights = 'imagenet',include_top =False, input_shape =(128, 128,3))
# model3 = tf.keras.models.Sequential()
# model3.add(resnet50model)
# model3.add(tf.keras.layers.Flatten())
# model3.add(tf.keras.layers.Dropout (0.5))
# model3.add(tf.keras.layers.Dense (256, 'relu'))
# model3.add(tf.keras.layers.Dense (17, activation='softmax'))
# Find the probabilities of all 17 classes in each instance of test data - should be 340 *17
predicted_vals1 = model1.predict(testX)
print("predicted_vals1 shape ", predicted_vals1.shape )
print("predicted_vals1 ", predicted_vals1 )
predicted_vals2 = model2.predict(testX)
print("predicted_vals2 shape ", predicted_vals2.shape )
print("predicted_vals2 ", predicted_vals2 )
# predicted_vals3 = model3.predict(testX)
# print("predicted_vals3 shape ", predicted_vals3.shape )
# print("predicted_vals3 ", predicted_vals3 )
# element wise addition will help, as we want to add probabilities of each class for each image. Then takke average,
# as I am using 3 models so 1/3 is multipled to the sum
predY_sum = predicted_vals1+ predicted_vals2
element_wise_sum_avg = predY_sum * (1/2)
# Now doing np.argmax
predY = np.argmax(element_wise_sum_avg, axis =1)
print("predY ", predY)
print("Checking shapes of testY and predY ", testY.shape, " ", predY.shape)
accuracy = accuracy_score(testY, predY)
print(accuracy)
if choice == 7:
resnet101model = tf.keras.applications.ResNet101(weights='imagenet', include_top=False, input_shape=(128, 128, 3))
print(resnet101model.summary())
featuresTrain = resnet101model.predict(trainX)
featuresTrain = featuresTrain.reshape(featuresTrain.shape[0], -1)
featuresVal = resnet101model.predict(testX)
featuresVal = featuresVal.reshape(featuresVal.shape[0], -1)
print(padded_test)
#%%
#RandomForest model fitting
model = RandomForestClassifier(n_estimators=100)
model.fit(padded_train, y_train)
#%%
y_pred = model.predict(padded_test)
acc = accuracy_score(y_pred, y_test)
print(acc * 100, "%")
# %%
#%%
vocab_size = 50_000
one_hots = [one_hot(word, vocab_size) for word in X_train]
print(one_hots)
# %%
padded = pad_sequences(one_hots, padding='post', maxlen=5)
print(padded)
# %%
model = Sequential()
model.add(Embedding(vocab_size, 50))
model.compile("adam", "mse")
# %%
predict = model.predict(padded)
# %%
predict.shape
# %%
# %%
def main ():
print("......... Welcome to SBA Loan Data Analysis ....... ")
print()
ip1 = int(input("What you want to do : \n 1) Prediction \n or \n 2) Analyze the data..?? \n\n "))
if ip1 == 1:
print("Menu :\n \
1)Random forest \n \
2)Decision Tree \n \
3)Naives Bayes \n \
4)SVM \n \
5)XG Boost \n \
6)KNN \n \
7)Keras Neural Network ")
ip2 = int(input("Enter a value from Above Menu : "))
# Importing the libraries
import numpy as np
import pandas as pd
# Importing the dataset
D7aFY1991_FY1999 = pd.read_csv('D:/CDAC_DATA\CDAC_PROJECT/15-1-MachineL/7aFY1991_FY1999_1.csv')
D7aFY2000_FY2009 = pd.read_csv('D:/CDAC_DATA\CDAC_PROJECT/15-1-MachineL/7aFY2000_FY2009_1.csv')
D7aFY2010_Present = pd.read_csv('D:/CDAC_DATA\CDAC_PROJECT/15-1-MachineL/7aFY2010_Present_1.csv')
#merge same dataframes
Data_7a = D7aFY1991_FY1999.append(D7aFY2000_FY2009)
Data_7a = Data_7a.append(D7aFY2010_Present)
#create sample data
#Data_sample_7a = Data_7a.sample(frac = 0.1,random_state = 0)
Data_sample_7a = Data_7a
Data_sample_7a = Data_sample_7a.iloc[:,[4,6,9,11,12,14,16,17,18,19,20,24,25,26,28,29]].values
#convert into pandas dataframe
Data_sample_7a = pd.DataFrame(data=Data_sample_7a)
# Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
imputer = imputer.fit(Data_sample_7a.iloc[:,10:11])
Data_sample_7a.iloc[:,10:11] = imputer.transform(Data_sample_7a.iloc[:,10:11])
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(Data_sample_7a.iloc[:,8:9])
Data_sample_7a.iloc[:,8:9] = imputer.transform(Data_sample_7a.iloc[:,8:9])
Data_sample_7a = Data_sample_7a.dropna()
#slpit data columns into dependent and independent variables
X7a = Data_sample_7a.iloc[:,0:14].values #independent
y7a = Data_sample_7a.iloc[:,[15]].values #dependent
# =============================================================================
#convert numpy objects to pandas dataframes
pd_X7a = pd.DataFrame(data=X7a[0:,0:])
pd_y7a = pd.DataFrame(data=y7a[0:,0:])
# =============================================================================
#encoding categorical data in independent variable
pd_X7a=np.asarray(pd_X7a)#convert pandas dataframe into numpy array
pd_X7a = encoder(pd_X7a)
#getuser data and encode
Userdata = getdata(pd_X7a)
Userdata=np.asarray(Userdata)#convert pandas dataframe into numpy array
Userdata = encoder(Userdata)
#encoding dependent variable
pd_y7a[0] = pd_y7a[0].replace(['PIF'],'0')
pd_y7a[0] = pd_y7a[0].replace(['CANCLD','EXEMPT','CHGOFF','COMMIT'],'1')
pd_y7a=np.asarray(pd_y7a)#convert pandas dataframe into numpy array
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(pd_X7a, pd_y7a, test_size = 0.25, random_state = 0)
# Feature Scaling of accuracy data
(X_train1, X_test) = scalingFunction(X_train,X_test)
# Feature Scaling of user data
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
Userdata = sc.fit_transform(Userdata)
if ip2 == 1:
# Fitting Random Forest Classification to the Training set
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train1, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = pd.DataFrame(data=y_pred[0:]) #converting to data frame
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the user set results
user_pred = classifier.predict(Userdata)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip2 == 2:
# Fitting Decision Tree Classification to the Training set
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the user set results
user_pred = classifier.predict(Userdata)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip2 == 3:
# Fitting Naive Bayes to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the user set results
user_pred = classifier.predict(Userdata)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip2 == 4:
# Fitting SVM to the Training set
from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the user set results
user_pred = classifier.predict(Userdata)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip2 == 5:
# Fitting XGBoost to the Training set
from xgboost import XGBClassifier
classifier = XGBClassifier()
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the user set results
user_pred = classifier.predict(Userdata)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip2 == 6:
# Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the user set results
user_pred = classifier.predict(Userdata)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip2 == 7:
# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
# Initialising the ANN
classifier = Sequential()
# Adding the input layer and the first hidden layer1
classifier.add(Dense(output_dim = 7, init = 'uniform', activation = 'relu', input_dim = 14))
# Adding the second hidden layer
classifier.add(Dense(output_dim = 7, init = 'uniform', activation = 'relu'))
# Adding the output layer
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))
# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)
# Part 3 - Making the predictions and evaluating the model
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix : \n")
print(cm)
print("Accuracy rate is : \n ")
print((cm[0,0]+cm[1,1])/(len(y_pred)))
# Predicting the Test set results
user_pred = classifier.predict(Userdata)
user_pred = (user_pred > 0.5)
converter(user_pred)
print("The Loan will be : ")
print(user_pred)
elif ip1 == 2:
print("Menu : \n \
1)Business wise JobsSupported \n \
2)Compare:Gross Aproval Vs SBA Aproval \n \
3)DistOffice wise SBAapproval \n \
4)GrossApproval Per LoanStatus \n \
5)GrossApproval Per DeliveryMethod \n \
6)JobsSupported per LoanStatus \n \
7)SBAapproval Loan Status \n \
")
ip2 = int(input("Enter a value from Above Menu : "))
from PIL import Image
if ip2 == 1:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\BusinnJobsSupp.png')
img.format = "PNG"
img.show()
elif ip2 == 2:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\Comp_GrossSBA.png')
img.format = "PNG"
img.show()
elif ip2 == 3:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\DistOff_wise_SBAappr.png')
img.format = "PNG"
img.show()
elif ip2 == 4:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\GrossAppLoanSt.png')
img.format = "PNG"
img.show()
elif ip2 == 5:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\GrossAppr_DeliveryMethod.png')
img.format = "PNG"
img.show()
elif ip2 == 6:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\JobsSuppLoanSt.png')
img.format = "PNG"
img.show()
elif ip2 == 7:
img = Image.open('D:\CDAC_DATA\CDAC_PROJECT\ggwp\SBAapprLoanSt.png')
img.format = "PNG"
img.show()
# Adding the second hidden layer
#classifier.add(Dense(output_dim = 150, init = 'uniform', activation = 'sigmoid'))
# Adding the third hidden layer
classifier.add(Dense(output_dim=80, init='uniform', activation='relu'))
# Adding the fourth hidden layer
classifier.add(Dense(output_dim=12, init='uniform', activation='sigmoid'))
# Adding the output layer
classifier.add(Dense(output_dim=1, init='uniform', activation='sigmoid'))
# Compiling the ANN
classifier.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size=10, nb_epoch=100)
# Part 3 - Making the predictions and evaluating the model
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)
# Making the Confusion Matrix
cmANN = confusion_matrix(y_test, y_pred)
print('\n Confusion Matrix using ANN')
from keras.layers import Dense
model = RandomForestClassifier()
cross_val_score(model, X, y_true)
X_train, X_test, y_train, y_test = train_test_split(X,
y_true,
test_size=0.3,
random_state=42)
import keras.backend as K
model = Sequential()
model.add(Dense(1, input_shape=(4, ), activation='sigmoid'))
model.compile(optimizer='Adam',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
epochs=30,
verbose=2,
validation_split=0.1)
#Evaluate gives the accuracy and loss, while the model.predict gives the prediction that is basically the output for the given input
#model.evaluate gives the loss and accuracy for 0 and 1 index respectively
result = model.evaluate(X_test, y_test)
history = pd.DataFrame(history.history, index=history.epoch)
history.plot(ylim=(0, 1))
plt.title('the accuracy for the test set is {:.3f}'.format(result[1] * 100),
# %tensorflow_version 2.x
# If you wish to use Tensorflow 1.X run the following line and then restart runtime
# %tensorflow_version 1.x
# You'll need to change your import statements from tensorflow.keras to keras
import tensorflow.keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential()
model.add(Dense(18, kernel_initializer = "uniform", activation = "relu", input_dim=16))
model.add(Dense(1, kernel_initializer = "uniform", activation = "sigmoid"))
model.compile(optimizer= "adam",loss = "binary_crossentropy",metrics = ["accuracy"])
# Display Model Summary and Show Parameters
model.summary()
# Start Training Our Classifier
batch_size = 10
epochs = 50
history = model.fit(X_train,
y_train,
batch_size = batch_size,
epochs = epochs,
verbose = 1,
)
def get_classifier(clf, input_shape=None):
"""
This function returns a classifier object defined by clf
INPUTS:
@str : a string indicating which classifier will be used
@input_shape: in the case that a CNN model will be trained, the input shape
(it is necessary to build the model)
OUTPUT
@classifier : a classifier object
"""
if clf == 'SVM':
classifier = svm.SVC(C=1e5, kernel='rbf', class_weight="balanced")
elif clf == 'log_reg':
classifier = linear_model.LogisticRegression(C=1e5,
class_weight="balanced")
elif clf == 'rf':
classifier = RandomForestClassifier(n_estimators=50,
max_depth=10,
class_weight="balanced")
elif clf == 'boost':
classifier = AdaBoostClassifier()
elif clf == 'cnn':
# CNN network to classify patches
input_img = Input(shape=input_shape)
x = input_img
x = Convolution2D(32, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = Convolution2D(32, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = Convolution2D(32, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(64, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = Convolution2D(64, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Convolution2D(128, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = Convolution2D(128, 3, 3, border_mode='same')(x)
x = LeakyReLU()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
f = x
x = Flatten()(x)
x = Dense(16)(x)
x = LeakyReLU()(x)
x = Dropout(0.5)(x)
x = Dense(2)(x)
o = Activation('softmax')(x)
# model train
classifier = Model(input_img, o)
classifier.summary()
classifier.compile(loss='categorical_crossentropy',
optimizer='Adadelta',
metrics=[fmeasure])
elif clf == 'Unet':
# unet network, https://github.com/jocicmarko/ultrasound-nerve-segmentation
inputs = Input(input_shape)
conv1 = Convolution2D(32, 3, 3, activation='relu',
border_mode='same')(inputs)
conv1 = Convolution2D(32, 3, 3, activation='relu',
border_mode='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Convolution2D(64, 3, 3, activation='relu',
border_mode='same')(pool1)
conv2 = Convolution2D(64, 3, 3, activation='relu',
border_mode='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Convolution2D(128, 3, 3, activation='relu',
border_mode='same')(pool2)
conv3 = Convolution2D(128, 3, 3, activation='relu',
border_mode='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Convolution2D(256, 3, 3, activation='relu',
border_mode='same')(pool3)
conv4 = Convolution2D(256, 3, 3, activation='relu',
border_mode='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Convolution2D(512, 3, 3, activation='relu',
border_mode='same')(pool4)
conv5 = Convolution2D(512, 3, 3, activation='relu',
border_mode='same')(conv5)
up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4],
mode='concat',
concat_axis=1)
conv6 = Convolution2D(256, 3, 3, activation='relu',
border_mode='same')(up6)
conv6 = Convolution2D(256, 3, 3, activation='relu',
border_mode='same')(conv6)
up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3],
mode='concat',
concat_axis=1)
conv7 = Convolution2D(128, 3, 3, activation='relu',
border_mode='same')(up7)
conv7 = Convolution2D(128, 3, 3, activation='relu',
border_mode='same')(conv7)
up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2],
mode='concat',
concat_axis=1)
conv8 = Convolution2D(64, 3, 3, activation='relu',
border_mode='same')(up8)
conv8 = Convolution2D(64, 3, 3, activation='relu',
border_mode='same')(conv8)
up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1],
mode='concat',
concat_axis=1)
conv9 = Convolution2D(32, 3, 3, activation='relu',
border_mode='same')(up9)
conv9 = Convolution2D(32, 3, 3, activation='relu',
border_mode='same')(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)
classifier = Model(input=inputs, output=conv10)
classifier.summary()
classifier.compile(optimizer=Adam(lr=1e-3),
loss='binary_crossentropy',
metrics=[fmeasure])
else:
sys.exit("The classifier you chose is not implemented")
return classifier
if args.classifier == "random_forest":
final_model = RandomForestClassifier(n_estimators=100,
max_depth=32,
random_state=0,
n_jobs=-1,
verbose=True)
elif args.classifier == "logistic_regression_keras":
classes = 26
final_model = Sequential()
final_model.add(
Dense(classes,
activation='softmax',
kernel_regularizer=regularizers.l1(0.0000001),
input_shape=(293, )))
final_model.compile(optimizer=optimizers.adam(lr=0.01),
loss='categorical_crossentropy',
metrics=['accuracy'])
final_model.fit(final_X,
to_categorical(final_Y),
epochs=100,
batch_size=32)
elif args.classifier == "logistic_regression_scikit":
final_model = LogisticRegression(penalty='l1',
C=1000,
multi_class="multinomial",
solver="saga",
max_iter=100,
verbose=True,
n_jobs=-1)
final_model.fit(final_X, final_Y)
# allAlgorithms = all_estimators(type_filter='regressor')
# print(allAlgorithms)
# print(len(allAlgorithms))
# print(type(allAlgorithms))
# for (name, algorithm) in allAlgorithms:
# model = algorithm()
# model.fit(x_train, y_train)
# y_pred = model.predict(x_test)
# print(name, "의 loss = ", r2_score(y_test, y_pred))
model = RandomForestClassifier()
model.fit(x1_train, y1_train)
'''
# 5. 모델 훈련
from keras.callbacks import EarlyStopping, TensorBoard
# td_hist = TensorBoard(log_dir='./graph',
# histogram_freq=0,
# write_graph=True,
# write_images=True)
early_stopping = EarlyStopping(monitor='loss', patience=60, mode='auto')
model.compile(loss='mae',
optimizer='adam',
metrics=['mse']) # adam=평타는 침. # 이 때문에 아래서 acc가 나온다.
model.fit(x1_train_scaled, y1_train,
epochs=10,
batch_size=10,
validation_split=0.2,
index = list(range(len(df)))
train_index = random.sample(index0, int(0.8 * len(index0))) + random.sample(
index1,
int(0.8 *
len(index1))) ##test_index is the index of test data随机选出2000个样本作为测试样本
test_index = [] ##train_index is the index of train data
for i in index:
if i not in train_index:
test_index.append(i)
print(len(train_index))
model = Sequential()
model.add(Dense(output_dim=50, input_dim=len(df[0]), activation='relu'))
model.add(Dense(output_dim=20, input_dim=50, activation='relu'))
model.add(Dense(output_dim=1, input_dim=20, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(df[train_index], label[train_index], nb_epoch=1000, batch_size=20)
pred = model.predict_classes(df[test_index]).reshape(len(test_index))
print(pred)
k = 0
for i in range(len(pred)):
if pred[i] == label[test_index][i]:
k = k + 1
print(k / len(test_index))
#model.save_weights('E:\...\my_model_weights.h5')
'''
神经网络的精度还是比较高的，并且网络的规模不需要太大，太大反而容易过拟合降低精度
def main():
# df = combine_datasets()
df = pd.read_csv('./data/combined.csv', index_col=0)
# df.fillna(-1, inplace=True)
# df = df.drop(df[~df['certificate'].isin(['G', 'PG', 'PG-13', 'R', 'Not Rated'])].index)
# df = add_award_points(df)
# Data preprocessing/encoding
df = df.drop(['movie', 'movie_id', 'synopsis', 'genre'], axis=1)
df['popularity'] = 1 / np.array(df['popularity']) * 100
df = pd.get_dummies(df, columns=['certificate'])
cols = df.columns.tolist()
cols = cols[df.columns.get_loc('oscar_animated') +
1:] + cols[:df.columns.get_loc('oscar_animated') + 1]
df = df[cols]
df = df.reset_index(drop=True)
splitIndex = df.index[df['year'] == 2018][0]
df = df.drop(['year'], axis=1)
# Splits data into training and testing sets
oscarStart = df.columns.get_loc('oscar_best_picture')
x = df.iloc[:, :oscarStart].values
y = df.iloc[:, oscarStart:].values
y[(y > 0) & (y < 1)] = 0.5 # winner is 1, nominee is 0.5, nothing is 0
xTrain, xTest = x[:splitIndex], x[splitIndex:]
yTrain, yTest = y[:splitIndex], y[splitIndex:]
# Checks how imbalanced the data is
unique, counts = np.unique(yTrain, return_counts=True)
print(dict(zip(unique, counts)))
# Scales inputs to avoid one variable having more weight than another
sc = StandardScaler()
xTrain = sc.fit_transform(xTrain)
xTest = sc.transform(xTest)
modelType = 'neuralnetwork'
predictCategory = True
if modelType == 'randomforest':
model = RandomForestClassifier(random_state=21)
model.fit(xTrain, yTrain)
yPred = model.predict(xTest)
p = np.where(yPred == 2)
v = np.where(yTest == 2)
elif modelType == 'neuralnetwork':
if not predictCategory:
# One hot encoding for softmax activation function
trainTargets = []
for i in yTrain:
if 1 in i:
trainTargets.append([1, 0, 0])
elif 0.5 in i:
trainTargets.append([0, 1, 0])
else:
trainTargets.append([0, 0, 1])
yTrain = np.array(trainTargets)
testTargets = []
for i in yTest:
if 1 in i:
testTargets.append([1, 0, 0])
elif 0.5 in i:
testTargets.append([0, 1, 0])
else:
testTargets.append([0, 0, 1])
yTest = np.array(testTargets)
model = Sequential()
model.add(Dense(256, input_dim=xTrain.shape[1]))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(3))
model.add(Activation('softmax'))
model.compile(optimizer=Adam(lr=0.01),
loss='categorical_crossentropy',
metrics=['mse'])
classWeights = {
0: counts.sum() / counts[2],
1: counts.sum() / counts[1],
2: counts.sum() / counts[0]
}
model.fit(xTrain,
yTrain,
epochs=512,
batch_size=32,
class_weight=classWeights)
else:
# One hot encoding for softmax activation function
trainTargets = [[] for i in range(0, 6)]
for i in yTrain:
for idx, j in enumerate(i):
if j == 1: # winner
trainTargets[idx].append([1, 0, 0])
elif j == 0.5: # nominee
trainTargets[idx].append([0, 1, 0])
else: # loser/nothing
trainTargets[idx].append([0, 0, 1])
yTrain = [np.array(i) for i in trainTargets]
testTargets = [[] for i in range(0, 6)]
for i in yTest:
for idx, j in enumerate(i):
if j == 1: # winner
testTargets[idx].append([1, 0, 0])
elif j == 0.5: # nominee
testTargets[idx].append([0, 1, 0])
else: # loser/nothing
testTargets[idx].append([0, 0, 1])
yTest = [np.array(i) for i in testTargets]
if os.path.exists('best.h5'):
model = load_model('best.h5')
else:
input = Input(shape=(xTrain.shape[1], ))
x = Dense(128, activation='relu')(input)
x = BatchNormalization()(x)
x = Dropout(0.2)(x)
output1 = Dense(3, activation='softmax')(x)
output2 = Dense(3, activation='softmax')(x)
output3 = Dense(3, activation='softmax')(x)
output4 = Dense(3, activation='softmax')(x)
output5 = Dense(3, activation='softmax')(x)
output6 = Dense(3, activation='softmax')(x)
model = Model(inputs=input,
outputs=[
output1, output2, output3, output4, output5,
output6
])
model.compile(optimizer=Adam(lr=0.01),
loss='categorical_crossentropy')
classWeights = {
0: counts.sum() / counts[2],
1: counts.sum() / counts[1],
2: counts.sum() / counts[0]
}
model.fit(xTrain,
yTrain,
epochs=512,
batch_size=32,
class_weight=classWeights)
# model.save('best.h5')
# Training accuracy (put training data back in) and testing accuracy
compute_model_accuracies(predictCategory, '(TRAINING)', model, xTrain,
yTrain, splitIndex)
compute_model_accuracies(predictCategory, '(TESTING)', model, xTest,
yTest, splitIndex)
def tenfoldcrossvalidation(feature_map, id_truth_map, index, id_tweet_map): feature_map = dict(sorted(feature_map.items(), key=operator.itemgetter(1))) tweets = [] truth = [] keys = [] for key, feature in feature_map.iteritems(): tweets.append(feature) truth.append(index[id_truth_map[key]]) keys.append(key) accuracy = 0.0 tp = 0.0 tn = 0.0 fp = 0.0 fn = 0.0 for i in xrange(10): tenth = len(tweets)/10 start = i*tenth end = (i+1)*tenth test_index = xrange(start,end) train_index = [i for i in range(len(tweets)) if i not in test_index] train_tweets = [] train_keys = [] test_tweets = [] test_keys = [] train_truth = [] test_truth = [] for i in xrange(len(tweets)): if i in train_index: train_tweets.append(tweets[i]) train_truth.append(truth[i]) train_keys.append(keys[i]) else: test_tweets.append(tweets[i]) test_truth.append(truth[i]) test_keys.append(keys[i]) new_train_tweets = featureselection(train_tweets, train_tweets, train_truth) new_test_tweets = featureselection(test_tweets, train_tweets, train_truth) if sys.argv[1] == "rbfsvm": print "RBF kernel SVM" clf = svm.SVC(kernel='rbf', C=1000, gamma=0.0001) clf.fit(np.array(new_train_tweets), np.array(train_truth)) test_predicted = clf.predict(np.array(new_test_tweets)) elif sys.argv[1] == "randomforest": # # Using Random forest for classification. print 'Random forest' clf = RandomForestClassifier(n_estimators=10, max_depth=None) clf.fit(np.array(new_train_tweets), np.array(train_truth)) test_predicted = clf.predict(np.array(new_test_tweets)) # getaccuracy(test_predicted, test_truth) elif sys.argv[1] == "linearsvm": # # Using Linear svm for classification. print 'Linear SVM' clf = svm.LinearSVC(random_state=20) clf.fit(np.array(new_train_tweets), np.array(train_truth)) test_predicted = clf.predict(np.array(new_test_tweets)) # print "F.score:" # print(f1_score(test_predicted, test_truth, average="micro")) # print "Accuracy:" # print(accuracy_score(test_predicted, test_truth, normalize="False")) # getaccuracy(test_predicted, test_truth) # elif sys.argv[1] == "polysvm": # print 'Poly SVM' # clf = svm.SVC(kernel='poly') # clf.fit(np.array(new_train_tweets), np.array(train_truth)) # test_predicted = clf.predict(np.array(new_test_tweets)) elif sys.argv[1] == "nn": print 'Neural Network' clf = Sequential() clf.add(Dense(7460, activation='relu')) clf.add(Dense(5000, activation='relu')) clf.add(Dense(2000, activation='relu')) clf.add(Dense(500, activation='relu')) clf.add(Dense(1, activation='softmax')) clf.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) clf.fit(np.array(new_train_tweets), np.array(train_truth), batch_size=64, epochs=10, validation_split=0.1) test_predicted = clf.predict(np.array(new_test_tweets)) print(f1_score(test_predicted, test_truth, average="micro")) elif sys.argv[1]=="xgb": xgb_model = xgb.XGBClassifier(objective="binary:logistic") xgb_model.fit(np.array(new_train_tweets), np.array(train_truth)) test_predicted = xgb_model.predict(np.array(new_test_tweets)) accuracy += getaccuracy(test_predicted, test_truth) tp += gettp(test_predicted, test_truth) tn += gettn(test_predicted, test_truth) fp += getfp(test_predicted, test_truth) fn += getfn(test_predicted, test_truth) if(sys.argv[1]=="nn"): print accuracy # print tp, tn, fp, fn precision = tp/(tp+fp) recall = tp/(tp+fn) print "F-score:" print (2*precision*recall)/(precision + recall) break print accuracy/10.0 # print tp, tn, fp, fn precision = tp/(tp+fp) recall = tp/(tp+fn) print "F-score:" print (2*precision*recall)/(precision + recall)
metrics = Metrics()
from sklearn.neural_network import MLPClassifier
model = MLPClassifier(hidden_layer_sizes=(200,), max_iter=500, alpha=0.0001,
solver='', verbose=10, random_state=0,tol=0.00000001,batch_size=100)
"""
model.add(Dense(8, input_dim=8, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
"""
#compile the model
"""
model.compile(loss='binary_crossentropy', optimizer='adagrad',metrics=['accuracy'])
model.summary()
"""
model.fit(X_train, y_train)
y_predict=model.predict(X_test)
print(accuracy_score(y_test, y_predict))
#print("Score of Neural Network--->", score[0])
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=42)
import keras.backend as K
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
K.clear_session() # clear model from memory
model = Sequential()
model.add(Dense(1, input_shape=(4, ), activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# train model
history = model.fit(X_train, y_train,
epochs=10) # record history of training progress
result = model.evaluate(X_test, y_test)
# visualize the training process
historydf = pd.DataFrame(history.history, index=history.epoch)
historydf.plot(ylim=(0, 1))
plt.title("Test accuracy: {:3.1f} %".format(result[1] * 100), fontsize=15)
# ===================================
# manually tune learning rate
# ===================================
def algorithm(method_A, OneVsRest, OneVsOne, randomized):
print("Selecting algorithm...")
print(" ")
if method_A == "svm":
print("Starting with " + method_A)
print(" ")
parameters_svm = {
'kernel': ('linear', 'rbf'),
'C': [1, 3, 10, 100],
'gamma': [0.01, 0.001]
}
model = svm.SVC()
model = search_par(randomized, model, parameters_svm)
if method_A == "random_forest":
print("Starting with " + method_A)
print(" ")
parameters_random = {
"max_depth": [2, 3, None],
"max_features": [2, 4, 6],
"min_samples_split": [2, 4, 6],
"min_samples_leaf": [2, 4, 6],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]
}
model = RandomForestClassifier(n_estimators=100)
model = search_par(randomized, model, parameters_random)
if method_A == "logistic":
print("Starting with " + method_A)
print(" ")
parameters_logistic = {'C': [100, 1000], 'tol': [0.001, 0.0001]}
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model = search_par(randomized, model, parameters_logistic)
if method_A == "neural_networks":
print("Starting with " + method_A)
print(" ")
#model = MLPClassifier()
model = Sequential()
model.add(
Dense(991, input_dim=179, init='normal')
) # number of features of the data +1 node for the bias term.
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(
Dense(495, init='normal')
) #In sum, for most problems, one could probably get decent performance (even without a second optimization step) by setting the hidden layer configuration using just two rules: (i) number of hidden layers equals one; and (ii) the number of neurons in that layer is the mean of the neurons in the input and output layers.
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(
Dense(99, init='normal')
) # If the NN is a classifier, then it also has a single node unless softmax is used in which case the output layer has one node per class label in your model.
model.add(Activation('softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
OneVsRest = False
OneVsOne = False
if OneVsRest:
print("Using OneVsRest ")
print(" ")
return OneVsRestClassifier(model)
if OneVsOne:
print("Using OneVsOne")
print(" ")
return OneVsOneClassifier(model)
print("Algorithm selected: " + method_A)
print(" ")
return model
# Add an input layer
model.add(Dense(16, activation='relu', input_dim=10))
# Add another input layer
model.add(Dense(12, activation='relu'))
# Add another input layer
model.add(Dense(12, activation='relu'))
# Add another input layer
model.add(Dense(8, activation='relu'))
# Add an output layer
model.add(Dense(
9,
activation='softmax')) # output 9 correspond to number of predicted class
# compile model and run the model
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(xx,
yy,
epochs=100,
validation_data=(val_x, pd.get_dummies(val_y).values))
# Also for evaluate, val_y have to change to one-hot encoding dummy variable
model.evaluate(val_x, pd.get_dummies(val_y).values)
model.summary()
class_weight='balanced',
n_estimators=50)
model.fit(x_train, y_train)
# add predictions to dataset
df['PREDICTIONS'] = model.predict(df['FEATURES'].values.tolist())
# train LSTM model
max_features = len(word_to_index)
maxlen = len(features[0])
batch_size = 32
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
# try using different optimizers and different optimizer configs
x_train, x_test, y_train, y_test = np.array(x_train), np.array(
x_test), np.array(y_train), np.array(y_test)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=1,
validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
# model
y_prediction = model.predict(X_test)
print("\naccuracy",
np.sum(y_prediction == df_test['label'].values) / float(len(y_test)))
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
start = time()
model = Sequential()
model.add(Dense(512, input_shape=(784, )))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
mode.fit(X_train, y_train_onehot)
print('\ntime taken %s seconds' % str(time() - start))
y_prediction = model.predict_classes(X_test)
print("\n\naccuracy", np.sum(y_prediction == y_test) / float(len(y_test)))
class Vorace_agent:
initialState = None
classifier = None
history = None
callbacks_list = None
epochs = 40
batch_size = 0
def __init__(self):
self.initialState = None
self.classifier = None
self.history = None
def __init__(self,
typeP,
nClasses,
inputLayer=None,
batch_size=0,
callbacks_list=None,
n_classifiers=10):
if typeP == 6:
typeP = random.randint(0, 5)
if typeP == 3:
typeP = random.randint(0, 2)
self.batch_size = batch_size
self.callbacks_list = callbacks_list
#print(typeP)
if typeP == 0:
self.classifier = Vorace_agent.getModel(nClasses, inputLayer)
self.classifier = Model(inputLayer, self.classifier)
if nClasses == 2:
self.classifier.compile(loss='binary_crossentropy',
metrics=['accuracy'],
optimizer='adam')
else:
self.classifier.compile(loss='categorical_crossentropy',
metrics=['accuracy'],
optimizer='adam')
self.initialState = self.classifier.get_weights()
elif typeP == 1:
if random.randint(0, 1) == 0:
self.classifier = DecisionTreeClassifier(
criterion="gini",
max_depth=random.randint(5, 25),
random_state=0)
else:
self.classifier = DecisionTreeClassifier(
criterion="entropy",
max_depth=random.randint(5, 25),
random_state=0)
self.initialState = clone(self.classifier)
elif typeP == 2:
A = math.log(pow(2, -5))
B = math.log(pow(2, 5))
c_value = math.exp(random.uniform(A, B))
if random.randint(0, 1) == 0:
self.classifier = svm.SVC(kernel='rbf',
C=c_value,
gamma='auto',
probability=True)
else:
A = 3
B = 5
degree = int(round(random.uniform(A, B)))
#print("C: {} DEGREE: {}".format(c_value, degree))
self.classifier = svm.SVC(kernel='poly',
degree=degree,
C=c_value,
gamma='auto',
probability=True)
self.initialState = clone(self.classifier)
elif typeP == 4:
value_lists = {
'bootstrap': [True, False],
'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 4],
'min_samples_split': [2, 5, 10],
'n_estimators': [10, 20, 50, 100, 200]
}
#'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}
params = {
'bootstrap':
random.choice(value_lists['bootstrap']),
'max_depth':
random.choice(value_lists['max_depth']),
'max_features':
random.choice(value_lists['max_features']),
'min_samples_leaf':
random.choice(value_lists['min_samples_leaf']),
'min_samples_split':
random.choice(value_lists['min_samples_split']),
'n_estimators':
random.choice(value_lists['n_estimators']),
}
self.classifier = RandomForestClassifier(**params)
self.initialState = clone(self.classifier)
elif typeP == 5:
self.classifier = xgb.XGBClassifier(
max_depth=random.randint(3, 25),
n_estimators=n_classifiers,
subsambple=random.random(),
colsample_bytree=random.random())
self.initialState = clone(self.classifier)
def reset(self):
#print(type(self.classifier))
if type(self.classifier) == Model:
self.classifier.set_weights(self.initialState)
else:
self.classifier = clone(self.initialState)
def fit(self, x, y, y_oneHot=None):
if type(self.classifier) == Model:
self.history = self.classifier.fit(x,
y_oneHot,
epochs=self.epochs,
batch_size=self.batch_size,
shuffle=True,
callbacks=self.callbacks_list,
verbose=0)
self.history = self.history.history['acc'][-1]
else:
self.classifier.fit(x, y)
y_pred = self.classifier.predict(x)
self.history = metrics.accuracy_score(y, y_pred)
def predict(self, x):
if type(self.classifier) == Model:
y_pred = self.classifier.predict(x)
else:
y_pred = self.classifier.predict_proba(x)
return y_pred
def getModel(nClass, inputLayer, nHLayers=4):
n = random.randint(2, nHLayers)
nInput = K.int_shape(inputLayer)[1]
#A=math.log(nInput)
#B=math.log(nInput**2)
A = math.log(16)
B = math.log(128)
#print("A:"+str(A))
#print("B:"+str(B))
activation = ('relu', 'tanh')
nNodes = int(round(math.exp(random.uniform(A, B))))
act_fun = random.randint(0, len(activation) - 1)
#print("nNodes:"+str(nNodes))
x = Dense(nNodes, activation=activation[act_fun])(inputLayer)
#print(K.int_shape(inputLayer)[1])
for i in range(1, n):
#nNodes = random.randint(nInput*2,nInput**2)
nNodes = int(round(math.exp(random.uniform(A, B))))
#print(nNodes)
act_fun = random.randint(0, len(activation) - 1)
#print(act_fun)
x = Dense(nNodes, activation=activation[act_fun])(x)
if nClass == 2:
x = Dense(nClass, activation='sigmoid')(x)
else:
x = Dense(nClass, activation='softmax')(x)
return x
# DecisionTree Classifier
tree_params = {"criterion": ["gini", "entropy"], "max_depth": list(range(2,4,1)),
"min_samples_leaf": list(range(5,7,1))}
grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)
grid_tree.fit(X_train, Y_train)
# tree best estimator
tree_clf = grid_tree.best_estimator_
model=Sequential()
model.add(Dense(128, init="uniform", input_dim=13, activation='relu'))
model.add(Dense(64, init ="uniform", activation="relu"))
model.add(Dense(1, init="uniform", activation="sigmoid"))
model.compile(loss="binary_crossentropy", metrics=['accuracy'], optimizer='adam')
model.summary()
history=model.fit(X_train,Y_train, epochs=100, batch_size=100)
plt.plot(history.history['loss'])
#plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
model.evaluate(X_test,Y_test)
#Kernel SVM Classifier - RBF - 94% accuracy on test set. Linear - 96% accuracy on test set
from sklearn.svm import SVC
classifier = SVC(kernel="linear", random_state=0)
cm_svm = evaluate_classifier(classifier, X_train, y_train)
#Neural Network
from keras.layers import Dense
from keras.models import Sequential
from keras.layers import Dropout
classifier = Sequential()
classifier.add(
Dense(input_dim=100, output_dim=50, activation="relu", init="uniform"))
classifier.add(Dropout(p=0.1))
classifier.add(Dense(output_dim=50, activation="relu", init="uniform"))
classifier.add(Dropout(p=0.1))
classifier.add(Dense(output_dim=6, activation="softmax", init="uniform"))
classifier.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"])
classifier.fit(X_train_pca, y_train, batch_size=25, epochs=100)
y_pred = classifier.predict(X_test_pca)
y_prediction = np.argmax(y_pred, axis=1)
y_test = np.argmax(y_test, axis=1)
#y_prediction = np.array([1,2,3,4,5,6])
from sklearn.metrics import confusion_matrix
cm_nn = confusion_matrix(y_test, y_prediction)
