import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_curve, auc
from dimension_reduction import elasticNet
import utils.tools as utils
from gcforest.gcforest import GCForest
from gcforest.utils.config_utils import load_json
start = time.time()
path1 = 'gcforest4.json'
config = load_json(path1)
gc = GCForest(config)
mask_data = sio.loadmat('yeast_elastic_mask_scale_0.03_0.1.mat')
mask = mask_data.get('yeast_elastic_mask')
extraction = sio.loadmat('yeast_feature_end.mat')
proteinA = extraction.get('feature_A')
protein_A = np.array(proteinA)
proteinB = extraction.get('feature_B')
protein_B = np.array(proteinB)
X_ = np.concatenate((protein_A, protein_B), axis=1)
X_ = np.array(X_)
[row, column] = np.shape(X_)
label_P = np.ones(int(row / 2))
label_N = np.zeros(int(row / 2))
label_ = np.hstack((label_P, label_N))
parser = argparse.ArgumentParser()
parser.add_argument("--model",
dest="model",
type=str,
default=None,
help="gcfoest Net Model File")
args = parser.parse_args()
return args
if __name__ == "__main__":
# config
args = parse_args()
if args.model == 'ca':
config = load_json('./mnist-ca.json')
elif args.model == 'gc':
config = load_json('./mnist-gc.json')
else:
config = load_json('./mnist-gc.json')
gc = GCForest(config)
# gc.set_keep_model_in_mem(False)
gc.set_keep_model_in_mem(True)
# data
data_num_train = 60000 # The number of figures
data_num_test = 10000 # test num
fig_w = 45 # width of each figure
"n_jobs": -1
})
ca_config["estimators"].append({
"n_folds": 5,
"type": "LogisticRegression"
})
config["cascade"] = ca_config
return config
if __name__ == "__main__":
args = parse_args()
if args.model is None:
config = get_toy_config()
else:
config = load_json(args.model)
gc = GCForest(config)
# If the model you use cost too much memory for you.
# You can use these methods to force gcforest not keeping model in memory
gc.set_keep_model_in_mem(False) # default is TRUE.
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# X_train, y_train = X_train[:2000], y_train[:2000]
X_train = X_train[:, np.newaxis, :, :]
X_test = X_test[:, np.newaxis, :, :]
X_train_enc = gc.fit_transform(X_train, y_train)
# X_enc is the concatenated predict_proba result of each estimators of the last layer of the GCForest model
# X_enc.shape =
# (n_datas, n_estimators * n_classes): If cascade is provided
training = np.loadtxt(open("/home/jluo80/Desktop/training_1.csv","rb"),delimiter=",",skiprows=1)
testing = np.loadtxt(open("/home/jluo80/Desktop/testing_1.csv","rb"),delimiter=",",skiprows=1)
# X_train = training[:, :-1]
# y_train = training[:, -1]
# X_test = testing[:, :-1]
# y_test = testing[:, -1]
return (training[:, :-1], training[:, -1]), (testing[:, :-1], testing[:, -1])
if __name__ == "__main__":
args = parse_args()
if args.model is None:
config = get_toy_config()
else:
config = load_json(args.model) # type(config): <class 'dict'>
gc = GCForest(config)
# If the model you use cost too much memory for you.
# You can use these methods to force gcforest not keeping model in memory
# gc.set_keep_model_in_mem(False), default is TRUE.
(X_train, y_train), (X_test, y_test) = claim_data_load()
# X_train, y_train = X_train[:2000], y_train[:2000]
X_train = X_train[:,np.newaxis,:,np.newaxis]
X_test = X_test[:,np.newaxis,:,np.newaxis]
X_train_enc = gc.fit_transform(X_train, y_train)
# X_enc is the concatenated predict_proba result of each estimators of the last layer of the GCForest model
# X_enc.shape =
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model",
dest="model",
type=str,
default=None,
help="gcfoest Net Model File")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
config = load_json('IP.json')
gc = GCForest(config)
# If the model you use cost too much memory for you.
# You can use these methods to force gcforest not keeping model in memory
# gc.set_keep_model_in_mem(False), default is TRUE.
f = h5py.File('IP28-28-27.h5', 'r')
train_images = f['data'][:]
train_labels = f['label'][:]
f.close()
#f=open('paviaU.data','rb')
#train_images=pickle.load(f)
#train_labels=pickle.load(f)
#f.close()
train_labels = np.argmax(train_labels, 1)
def model_gcforest(config_file):
config = load_json(config_file)
model = gc = GCForest(config)
return model
def _make_clf(self, args):
config = load_json(str(args.config))
return gcforest.GCForest(config)
ca_config["estimators"].append(
{"n_folds": 3, "type": "ExtraTreesClassifier", "n_estimators": 500, "max_depth": None, "n_jobs": -1,"max_features":1})
ca_config["estimators"].append(
{"n_folds": 3, "type": "ExtraTreesClassifier", "n_estimators": 500, "max_depth": None, "n_jobs": -1,"max_features":1})
config["cascade"] = ca_config
return config
if __name__ == "__main__":
# args = parse_args()
# if args.model is None:
# config = get_toy_config()
# else:
# config = load_json(args.model)
config1 = load_json("/home/qiang/repo/python/experiment-gcForest/cascade_clf/examples/demo_ca.json")
# If the model you use cost too much memory for you.
# You can use these methods to force gcforest not keeping model in memory
# gc.set_keep_model_in_mem(False), default is TRUE.
config2 = get_toy_config()
acc_st = []
acc_gc = []
acc_rf = []
for i in range(10):
(X_train, y_train), (X_test, y_test) = uci_yeast.load_data()
gc1 = GCForest(config1)
gc1.fit_transform(X_train, y_train)
y_pred = gc1.predict(X_test)
acc = accuracy_score(y_test, y_pred)
