def deserialize_random_forest(model_dict):
model = RandomForestClassifier(**model_dict['params'])
estimators = [deserialize_decision_tree(decision_tree) for decision_tree in model_dict['estimators_']]
model.estimators_ = np.array(estimators)
model.classes_ = np.array(model_dict['classes_'])
model.n_features_ = model_dict['n_features_']
model.n_outputs_ = model_dict['n_outputs_']
model.max_depth = model_dict['max_depth']
model.min_samples_split = model_dict['min_samples_split']
model.min_samples_leaf = model_dict['min_samples_leaf']
model.min_weight_fraction_leaf = model_dict['min_weight_fraction_leaf']
model.max_features = model_dict['max_features']
model.max_leaf_nodes = model_dict['max_leaf_nodes']
model.min_impurity_decrease = model_dict['min_impurity_decrease']
model.min_impurity_split = model_dict['min_impurity_split']
if 'oob_score_' in model_dict:
model.oob_score_ = model_dict['oob_score_']
if 'oob_decision_function_' in model_dict:
model.oob_decision_function_ = model_dict['oob_decision_function_']
if isinstance(model_dict['n_classes_'], list):
model.n_classes_ = np.array(model_dict['n_classes_'])
else:
model.n_classes_ = model_dict['n_classes_']
return model
import pandas as pd #1
from sklearn.model_selection import train_test_split #2
from sklearn.ensemble import RandomForestClassifier #3
from sklearn.metrics import confusion_matrix #4
# import libs
datas = pd.read_csv("datas.csv") # read datas
#1
x = datas.iloc[:, 3:-3].values
y = datas.iloc[:, -2].values
# split values
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.10, random_state=0) # 90% for train, %10 for test
#2
rfc = RandomForestClassifier()
#3
rfc.max_depth = 100
rfc.criterion = "entropy" #select criterion,other criterion is 'gini'
rfc.n_estimators = 1
rfc.fit(x_train, y_train)
y_pred = rfc.predict(x_test)
cm = confusion_matrix(y_test, y_pred)
#4
print("RFC")
print(cm)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=20,
n_features=2,
centers=2,
cluster_std=2,
random_state=3)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, edgecolors='k')
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(random_state=8)
clf.max_depth = 1
clf.n_estimators = 1
clf.fit(X, y)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')
plotBoundary(X, clf)
for i in range(3, 10):
clf.n_estimators = i
clf.fit(X, y)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)
plotBoundary(X, clf)
plt.title("{0} estimators".format(i))
plt.show()
from sklearn import grid_search
clf = grid_search.GridSearchCV(model, parameters,
cv=4, verbose=10,
n_jobs=1)
print 'Grid Search for the model'
clf.fit(X_trn, y_trn)
print clf.best_params_
model.n_estimators = clf.best_params_['n_estimators']
model.oob_score = clf.best_estimator_['oob_score']
else:
model.n_estimators = 600
model.oob_score = False
model.max_depth = 20
model.n_jobs = 20
from sklearn import cross_validation as cv
if args.SGD:
from SGDRank import SGDClassifier
model = SGDClassifier()
print 'CV'
cv.cross_val_score(model, X_trn, y_trn, cv=3, n_jobs=3)
print 'Fit the model'
model.fit(X_trn, y_trn)