我正在使用SVM，但我的问题是程序的执行停留在model.fit(X_test, y_test)中，这与拟合SVM模型相对应。如何解决？这是我的代码：
# Make Predictions with Naive Bayes On The Iris Dataset
from csv import reader
from math import sqrt
from math import exp
from math import pi
from sklearn.cross_validation import train_test_split
from sklearn import datasets
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
from sklearn import tree
import collections
#Import svm model
from sklearn import svm
# Function to split the dataset
def splitdataset(balance_data, column_count):
# Separating the target variable
X = balance_data.values[:, 1:column_count]
Y = balance_data.values[:, 0]
# Splitting the dataset into train and test
X_train, X_test, y_train, y_test = train_test_split(
X, Y, test_size = 0.3, random_state = 100)
return X, Y, X_train, X_test, y_train, y_test
def importdata():
balance_data = pd.read_csv( 'dataExtended.txt', sep= ',')
row_count, column_count = balance_data.shape
# Printing the dataswet shape
print ("Dataset Length: ", len(balance_data))
print ("Dataset Shape: ", balance_data.shape)
print("Number of columns ", column_count)
# Printing the dataset obseravtions
print ("Dataset: ",balance_data.head())
balance_data['gold'] = balance_data['gold'].astype('category').cat.codes
balance_data['Program'] = balance_data['Program'].astype('category').cat.codes
return balance_data, column_count
# Driver code
def main():
print("hey")
# Building Phase
data,column_count = importdata()
X, Y, X_train, X_test, y_train, y_test = splitdataset(data, column_count)
#Create a svm Classifier
model = svm.SVC(kernel='linear',probability=True) # Linear Kernel
print('before fitting')
model.fit(X_test, y_test)
print('fitting over')
predicted = model.predict(X_test)
print('prediction over')
print(metrics.classification_report(y_test, predicted))
print('classification over')
print(metrics.confusion_matrix(y_test, predicted))
probs = model.predict_proba(X_test)
probs_list = list(probs)
y_pred=[None]*len(y_test)
y_pred_list = list(y_pred)
y_test_list = list(y_test)
i=0
threshold=0.7
while i<len(probs_list):
#print('probs ',probs_list[i][0])
if (probs_list[i][0]>=threshold) & (probs_list[i][1]<threshold):
y_pred_list[i]=0
i=i+1
elif (probs_list[i][0]<threshold) & (probs_list[i][1]>=threshold):
y_pred_list[i]=1
i=i+1
else:
#print(y_pred[i])
#print('i==> ',i, ' probs length ', len(probs_list), ' ', len(y_pred_list), ' ', len(y_test_list))
y_pred_list.pop(i)
y_test_list.pop(i)
probs_list.pop(i)
#print(y_pred_list)
print('confusion matrix\n',confusion_matrix(y_test_list,y_pred_list))
print('classification report\n', classification_report(y_test_list,y_pred_list))
print('accuracy score', accuracy_score(y_test_list, y_pred_list))
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test_list, y_pred_list))
print('Mean Squared Error:', metrics.mean_squared_error(y_test_list, y_pred_list))
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test_list, y_pred_list)))
if __name__=="__main__":
main()