def run(npath, ppath):
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import plot_model
weightTrue = 0.8
class_weight = {0: (1 - weightTrue), 1: weightTrue}
# Setup all data for inputs
setup_data(npath, ppath)
# Retrieve data for model
X, Y = get_model_data_full(data)
# create model
model = KerasClassifier(build_fn=create_model,
epochs=50,
batch_size=32,
verbose=1)
plot_model(create_model(), show_shapes=True)
# evaluate using 10-fold cross validation
kfold = StratifiedKFold(n_splits=3, shuffle=True, random_state=1543)
results = cross_val_score(model, X, Y, cv=kfold)
print('=' * 30)
print(results.mean())
def run_grid(npath, ppath, weightTrue=0.75):
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import plot_model
weightTrue = 0.8
class_weight = {0: (1 - weightTrue), 1: weightTrue}
# Setup all data for inputs
setup_data(npath, ppath)
# Retrieve data for model
X, Y = get_model_data_full(data)
# Define parameters to grid search
param_grid = define_grid()
# create model
model = KerasClassifier(build_fn=create_model,
epochs=50,
batch_size=64,
verbose=1)
plot_model(create_model(), show_shapes=True)
# Setup grid object
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1)
# Fit model and find scores
grid_result = grid.fit(X,
Y,
validation_split=0.1,
class_weight=class_weight)
print("Best: %f using %s" %
(grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
def run(npath, ppath):
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import plot_model
from keras.callbacks import EarlyStopping
from ml_statistics import BaseStatistics
from keras import optimizers
import numpy as np
# Params
weightTrue = 0.8
class_weight = {0: (1 - weightTrue), 1: weightTrue}
earlyStopping = EarlyStopping(monitor='val_loss',
patience=10,
verbose=1,
mode='auto')
opt = optimizers.Nadam(lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
schedule_decay=0.004)
# opt = optimizers.RMSprop(lr=0.001, decay=0.0)
# Setup all data for inputs
setup_data(npath, ppath)
# Retrieve data for model
X, Y = get_model_data_full(data)
X = X * 100000
print X
print Y.shape
# plot_model(create_model(), show_shapes=True)
# evaluate using 10-fold cross validation
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=17)
# results = cross_val_score(model, X, Y, cv=kfold)
# print('='*30)
# print(results.mean())
kfold.get_n_splits(X, Y)
for train_index, test_index in kfold.split(X, Y):
# TRAIN DATA
X_train, y_train = X[train_index, :], Y[train_index]
# TEST DATA
X_test, y_test = X[test_index, :], Y[test_index]
# create model
# model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=32, verbose=1)
model = create_model()
# Compile Model
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['accuracy'])
print(model.summary())
history = model.fit(X_train,
y_train,
validation_split=0.3,
epochs=100,
batch_size=128,
callbacks=[earlyStopping],
class_weight=class_weight,
verbose=0)
print(history.history.keys())
Y_pred = model.predict(X_test, verbose=0)
stats = BaseStatistics(y_test, Y_pred)
print stats