http://scikit-learn.org/stable/modules/ensemble.html
# !/usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "abdata"
import time
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
# from sklearn import datasets
# import matplotlib.pyplot as plt
mdata = pd.read_csv('http://data.galaxystatistics.com/blog_data/regression/iris.csv')
X = mdata.iloc[:,2:6]
print(X.head())
print(X.shape)
print(type(X))
y = mdata.iloc[:,1]
t0 = time.time()
clf = RandomForestClassifier(n_estimators=100)
clf = clf.fit(X, y)
t = time.time() - t0
print(t)
print(clf.predict(X))
print(type(clf))
######
### 示例1：
######
# from sklearn.datasets import load_iris
# from sklearn.ensemble import RandomForestClassifier
# import pandas as pd
# import numpy as np
#
# iris = load_iris()
# df = pd.DataFrame(iris.data, columns=iris.feature_names)
#
# print(df)
# print(iris.target)
# print(iris.target_names)
#
# df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75
# # df['species'] = pd.Factor(iris.target, iris.target_names)
# df['species'] = iris.target
#
# print(df.head())
#
# train, test = df[df['is_train']==True], df[df['is_train']==False]
#
# features = df.columns[:4]
# print(features)
#
# clf = RandomForestClassifier(n_jobs=2)
#
# y, _ = pd.factorize(train['species'])
# print(y)
#
# clf.fit(train[features], y)
#
# preds = iris.target_names[clf.predict(test[features])]
# print(preds)
#
# result = pd.crosstab(test['species'], preds, rownames=['actual'], colnames=['preds'])
# print(result)
######
### 示例2：
######
# from sklearn.ensemble import RandomForestClassifier
# X = [[0, 0], [1, 1]]
# Y = [0, 1]
# clf = RandomForestClassifier(n_estimators=10)
# clf = clf.fit(X, Y)
# print(clf)
# print(clf.predict([[0.5, 0.5]]))
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 0 5.1 3.5 1.4 0.2
## 1 4.9 3.0 1.4 0.2
## 2 4.7 3.2 1.3 0.2
## 3 4.6 3.1 1.5 0.2
## 4 5.0 3.6 1.4 0.2
## (150, 4)
## <class 'pandas.core.frame.DataFrame'>
## 0.11530709266662598
## ['setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa' 'setosa'
## 'setosa' 'setosa' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'versicolor' 'versicolor' 'versicolor' 'versicolor'
## 'versicolor' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica' 'virginica' 'virginica' 'virginica'
## 'virginica' 'virginica' 'virginica']
## <class 'sklearn.ensemble.forest.RandomForestClassifier'># !/usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "abdata"
import time
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
# from sklearn import datasets
# import matplotlib.pyplot as plt
mdata = pd.read_csv('http://data.galaxystatistics.com/blog_data/regression/iris.csv')
X = mdata.iloc[:,3:6]
print(X.head())
print(X.shape)
print(type(X))
y = mdata.iloc[:,2]
t0 = time.time()
clf = RandomForestRegressor(n_estimators=100)
clf = clf.fit(X, y)
t = time.time() - t0
print(t)
pre = clf.predict(X)
print(pre)
print(pre.shape)
print(type(clf))
## Sepal.Width Petal.Length Petal.Width
## 0 3.5 1.4 0.2
## 1 3.0 1.4 0.2
## 2 3.2 1.3 0.2
## 3 3.1 1.5 0.2
## 4 3.6 1.4 0.2
## (150, 3)
## <class 'pandas.core.frame.DataFrame'>
## 0.09976530075073242
## [ 5.118 4.7915 4.5557 4.7512 5.036 5.4175
## 4.80016667 5.0558881 4.5185 4.83625 5.31638333 4.9 4.771
## 4.4946 5.65515 5.5415 5.436 5.054 5.5365
## 5.18841667 5.20841667 5.17883333 4.93 5.07 4.9755 4.931
## 5.049 5.17900476 5.08 4.743 4.8315 5.24696667
## 5.33575 5.46065 4.7512 4.8064 5.348 4.968 4.5081
## 5.0558881 5.126 4.505 4.5557 5.04 5.2205 4.762
## 5.14273333 4.632 5.31638333 4.89766667 6.793 6.31133333
## 6.794 5.596 6.34733333 5.91266667 6.366 4.967 6.444
## 5.452 5.19 5.763 5.826 6.266 5.632 6.595
## 5.61326667 5.777 5.944 5.581 6.118 5.924 6.23
## 6.173 6.209 6.38853333 6.591 6.503 5.9101 5.48
## 5.53 5.54 5.73 6.052 5.61326667 6.09766667
## 6.69133333 6.13316667 5.692 5.536 5.72316667 6.25243333
## 5.749 5. 5.68633333 5.73566667 5.76483333 6.209 5.077
## 5.766 6.604 5.839 6.956 6.355 6.667 7.615
## 5.33133333 7.387 6.711 7.308 6.45 6.277 6.721
## 5.788 5.879 6.516 6.508 7.663 7.662 6.105
## 6.814 5.806 7.683 6.202 6.69 7.059 6.203
## 6.093 6.417 6.917 7.393 7.7 6.412 6.302
## 6.232 7.519 6.322 6.512 6.03333333 6.767 6.693
## 6.737 5.839 6.859 6.619 6.588 6.092 6.431
## 6.27 6.066 ]
## (150,)
## <class 'sklearn.ensemble.forest.RandomForestRegressor'>