#!/usr/bin/env python
# coding: utf-8

# # **학습데이터 Restful API 서버 만들기**
# 1. https://honeyteacs.tistory.com/6
# 1. https://scikit-learn.org/stable/modules/model_persistence.html
# 1. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html?highlight=randomforestclassifier#sklearn.ensemble.RandomForestClassifier

# In[41]:


from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=1000, n_features=4,
                           n_informative=2, n_redundant=0,
                           random_state=0, shuffle=False)
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X, y)
print(clf.feature_importances_)
print(clf.predict([[0, 0, 0, 0]]))


# In[45]:


# Save Model
import pickle
with open('data/train.pk', 'wb') as f:
    pickle.dump(clf, f)


# In[53]:


# Load Model
clf = pickle.load(open("data/train.pk", "rb"))
print(clf.predict([[0,0,1,1]]))


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import pickle
s = pickle.dumps(clf)
clf2 = pickle.loads(s)
clf2.predict(X[0:1])
array([0])
y[0]
0


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import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

iris = datasets.load_iris()
mapping = dict(zip(np.unique(iris.target), iris.target_names))
mapping


# In[38]:


X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.DataFrame(iris.target).replace(mapping)
X.head(3), y.head(3) # Train, Test DataFrame


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clf = RandomForestClassifier()
clf.fit(X,y) 


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# Model Save & Load


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import pickle
s = pickle.dumps(clf)
clf2 = pickle.loads(s)
clf2.predict(X[0:1])
array([0])
y[0]
0