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IRIS_predictor.py
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IRIS_predictor.py
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import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score,cross_val_predict,GridSearchCV
from collections import defaultdict
classes = ['','Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
def conver_number(x):
try:
return float(x)
except:
if(x == 'Iris-setosa'):
return 1
elif x == 'Iris-versicolor':
return 2
else:
return 3
converteres = defaultdict(conver_number)
datasets = pd.read_csv('Data/iris/iris.data' ,header=None,converters=converteres )
print(datasets.ix[:5])
X = datasets[[0,1,2,3]].values
print(X[:5])
y = datasets[4].values
for i in range(len(y)):
if y[i] == classes[1]:
y[i] = int(1)
elif y[i] == classes[2]:
y[i] = int(2)
elif y[i] == classes[3]:
y[i] = int(3)
else:
y[i] = int(0)
y = np.array(y , dtype='int')
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y)
parameter_space={
'max_depth':[k for k in range(1,50)],
'max_features':[1,2,3,4]
}
grid = GridSearchCV(DecisionTreeClassifier(random_state=25), parameter_space)
grid.fit(X_train,y_train)
x = [[4.1,4.2,4.8,3]]
y_predict = grid.predict(np.array(x))
print(y_predict)
print(grid.best_estimator_)