"""
Description: A python 2.7 implementation of gcForest proposed in [1]. A demo implementation of gcForest library as well as some demo client scripts to demostrate how to use the code. The implementation is flexible enough for modifying the model or
fit your own datasets.
Reference: [1] Z.-H. Zhou and J. Feng. Deep Forest: Towards an Alternative to Deep Neural Networks. In IJCAI-2017. (https://arxiv.org/abs/1702.08835v2 )
Requirements: This package is developed with Python 2.7, please make sure all the demendencies are installed, which is specified in requirements.txt
ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou(zhouzh@lamda.nju.edu.cn)
ATTN2: This package was developed by Mr.Ji Feng(fengj@lamda.nju.edu.cn). The readme file and demo roughly explains how to use the codes. For any problem concerning the codes, please feel free to contact Mr.Feng.
"""
import numpy as np
from scipy.sparse import issparse
from .utils.log_utils import get_logger
LOGGER = get_logger('gcforest.exp_utils')
def load_model_config(model_path, log_name=None):
import json
from .utils.config_utils import load_json
config = load_json(model_path)
if log_name is not None:
logger = get_logger(log_name)
logger.info(log_name)
logger.info("\n" + json.dumps(config, sort_keys=True, indent=4, separators=(',', ':')))
return config
def concat_datas(datas):
if type(datas) != list:
return datas
for i, data in enumerate(datas):
datas[i] = data.reshape((data.shape[0], -1))
return np.concatenate(datas, axis=1)
def data_norm(X_train, X_test):
X_mean = np.mean(X_train, axis=0)
X_std = np.std(X_train, axis=0)
X_train -= X_mean
X_train /= X_std
X_test -= X_mean
X_test /= X_std
return X_mean, X_std
def append_origin(X, X_origin):
return np.hstack(( X.reshape((X.shape[0]), -1), X_origin.reshape((X_origin.shape[0], -1)) ))
def prec_ets(n_trees, X_train, y_train, X_test, y_test, random_state=None):
"""
ExtraTrees
"""
from sklearn.ensemble import ExtraTreesClassifier
if not issparse(X_train):
X_train = X_train.reshape((X_train.shape[0], -1))
if not issparse(X_test):
X_test = X_test.reshape((X_test.shape[0], -1))
LOGGER.info('start predict: n_trees={},X_train.shape={},y_train.shape={},X_test.shape={},y_test.shape={}'.format(
n_trees, X_train.shape, y_train.shape, X_test.shape, y_test.shape))
clf = ExtraTreesClassifier(n_estimators=n_trees, max_depth=None, n_jobs=-1, verbose=1, random_state=random_state)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
prec = float(np.sum(y_pred == y_test)) / len(y_test)
LOGGER.info('prec_ets{}={:.6f}%'.format(n_trees, prec*100.0))
return clf, y_pred
def prec_rf(n_trees, X_train, y_train, X_test, y_test):
"""
ExtraTrees
"""
from sklearn.ensemble import RandomForestClassifier
if not issparse(X_train):
X_train = X_train.reshape((X_train.shape[0], -1))
if not issparse(X_test):
X_test = X_test.reshape((X_test.shape[0], -1))
LOGGER.info('start predict: n_trees={},X_train.shape={},y_train.shape={},X_test.shape={},y_test.shape={}'.format(
n_trees, X_train.shape, y_train.shape, X_test.shape, y_test.shape))
clf = RandomForestClassifier(n_estimators=n_trees, max_depth=None, n_jobs=-1, verbose=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
prec = float(np.sum(y_pred == y_test)) / len(y_test)
LOGGER.info('prec_rf{}={:.6f}%'.format(n_trees, prec*100.0))
return clf, y_pred
def xgb_eval_accuracy(y_pred_proba, y_true):
"""
y_true (DMatrix)
"""
y_pred = np.argmax(y_pred_proba, axis=1)
y_true = y_true.get_label()
acc = float(np.sum(y_pred == y_true)) / len(y_pred)
return 'accuracy', -acc
def prec_xgb(n_trees, max_depth, X_train, y_train, X_test, y_test, learning_rate=0.1):
"""
ExtraTrees
"""
import xgboost as xgb
X_train = X_train.reshape((X_train.shape[0], -1))
X_test = X_test.reshape((X_test.shape[0], -1))
LOGGER.info('start predict: n_trees={},X_train.shape={},y_train.shape={},X_test.shape={},y_test.shape={}'.format(
n_trees, X_train.shape, y_train.shape, X_test.shape, y_test.shape))
clf = xgb.XGBClassifier(n_estimators=n_trees, max_depth=max_depth, objective='multi:softprob',
seed=0, silent=True, nthread=-1, learning_rate=learning_rate)
eval_set = [(X_test, y_test)]
clf.fit(X_train, y_train, eval_set=eval_set, eval_metric="merror")
y_pred = clf.predict(X_test)
prec = float(np.sum(y_pred == y_test)) / len(y_test)
LOGGER.info('prec_xgb_{}={:.6f}%'.format(n_trees, prec*100.0))
return clf, y_pred
def prec_log(X_train, y_train, X_test, y_test):
from sklearn.linear_model import LogisticRegression
if not issparse(X_train):
X_train = X_train.reshape((X_train.shape[0], -1))
if not issparse(X_test):
X_test = X_test.reshape((X_test.shape[0], -1))
LOGGER.info('start predict: X_train.shape={},y_train.shape={},X_test.shape={},y_test.shape={}'.format(
X_train.shape, y_train.shape, X_test.shape, y_test.shape))
X_train = X_train.reshape((X_train.shape[0], -1))
X_test = X_test.reshape((X_test.shape[0], -1))
clf = LogisticRegression(solver='sag', n_jobs=-1, verbose=1)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
prec = float(np.sum(y_pred == y_test)) / len(y_test)
LOGGER.info('prec_log={:.6f}%'.format(prec*100.0))
return clf, y_pred
def plot_forest_all_proba(y_proba_all, y_gt):
from matplotlib import pylab
N = len(y_gt)
num_tree = len(y_proba_all)
pylab.clf()
mat = np.zeros((num_tree, N))
LOGGER.info('mat.shape={}'.format(mat.shape))
for i in range(num_tree):
mat[i,:] = y_proba_all[i][(range(N), y_gt)]
pylab.matshow(mat, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
pylab.grid(False)
pylab.show()
def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None):
from matplotlib import pylab
cm = np.asarray(cm, dtype=np.float32)
for i, row in enumerate(cm):
cm[i] = cm[i] / np.sum(cm[i])
#import matplotlib.pyplot as plt
#plt.ion()
pylab.clf()
pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
ax = pylab.axes()
ax.set_xticks(range(len(label_list)))
ax.set_xticklabels(label_list, rotation='vertical')
ax.xaxis.set_ticks_position('bottom')
ax.set_yticks(range(len(label_list)))
ax.set_yticklabels(label_list)
pylab.title(title)
pylab.colorbar()
pylab.grid(False)
pylab.xlabel('Predicted class')
pylab.ylabel('True class')
pylab.grid(False)
pylab.savefig('test.jpg')
pylab.show()
Datasets
Models
