无法弄清楚如何定义我的 y_test

Posted 2023-03-12

【中文标题】无法弄清楚如何定义我的 y_test

【英文标题】：Not able to figure out how to define my y_test

【发布时间】：2020-03-26 16:01:00

【问题描述】：

我是 Python 和 sklearn 的新手，如有任何帮助，将不胜感激。我之前唯一的经验是使用 mnist，我不确定如何在使用 csv 时定义 y_test。

我已经尝试了其他一些迭代，到目前为止没有任何效果。我没有包括进口和实用程序。

dataDir = '/content/drive/My Drive/Colab Notebooks/Final/dataQ2/' # Directory with input files
trainFile = 'q2train.csv' # Training examples
labelFile = 'q2label.csv' # Test label
validFile = 'q2valid.csv' # Valid Files

train = pd.read_csv(dataDir+trainFile) # Read training data
valid = pd.read_csv(dataDir+validFile) # Read test data
label = pd.read_csv(dataDir+labelFile) # Unlabeled file data

x_train = train[list(train)[1:]].values
x_test = valid[list(train)[1:]].values

# Specify output directories
modelDir = 'model/' # directory for saved models
outputDir = 'output/' #directory for output files

# Create Directories if needed:
os.makedirs(os.path.dirname(modelDir), exist_ok=True)
os.makedirs(os.path.dirname(outputDir), exist_ok=True)

#Display directory names
print('Models saved in %s' %modelDir)
print('Outputs saved in %s' %outputDir)

models =  #dictoionary of SciKit-Learn classifiers with non-default parameters
models['NB'] = MultinomialNB()
models['DT'] = DecisionTreeClassifier()
models['RF'] = RandomForestClassifier(n_estimators=100)
models['KNN'] = KNeighborsClassifier(n_neighbors=10, algorithm='brute')
models['SVM'] = SVC(kernel='poly', gamma='auto')
models['LRM'] = LogisticRegression()

#Define function to evaluate classification accuracy
def evaluatePredictions(modelName, actual, predicted):
  """Returns classification accuracy
  -Saves confusion matrix in outputDir
  -Displays classification report
  -Saves predicted classes in pandas data frame 'predictedDF'"""
  acc = accuracy_score(actual, predicted) # accuracy
  print("Accuracy with test data: %4.2f%%\n" %(100*acc))
  print("CONFUSION MATRIX (Rows correspond to True Values):\n")
  cm = confusion_matrix(actual, predicted) #confusion_matrix
  cm = pd.DataFrame(cm) #convert to pandas data frame
  print(cm) # print confusion matrix
  cm.to_csv(outputDir+modelName+'confusionMatrix.csv') # save confusion matrix
  print("\nCLASSIFICATION REPORT:\n")
  print(classification_report(actual, predicted)) #classification report
  return acc #returns accuracy

def displayDigits(images, labels, nCols=10):
  """Displays images with labels (nCols per row)
  -images: list of vectors with 784 (28/28) grayscale values
  -labels: list of labels for images"""
  nRows = np.ceil(len(labels)/nCols).astype('int') # number of rows
  plt.figure(figsize=(2*nCols,2*nRows)) #figure size
  for i in range(len(labels)):
    plt.subplot(nRows,nCols,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(images[i].reshape(28, 28), interpolation='nearest')
    plt.xlabel(str(labels[i]), fontsize=14)
  plt.show()
  return


def get_data(trainFile, test_prop=0.2, seed=2019): #I am pretty sure this is line is my issue.
  """returns data for training, testing, and data characteristics"""
  data = data_sets[data_set_name]
  X, y = data.data, data.target
  X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                      test_size=test_prop, 
                                                      random_state=seed)
  nF = X.shape[1] # number of features
  nC = len(np.unique(y)) # number of classes
  nTrain, nTest = len(y_train), len(y_test)
  print("\nData set: %s" %data_set_name)
  print("\tNumber of features %d" %nF)
  print("\tNumber of output classes = %d" %(nC))
  print("\tNumber of training examples = %d" %(nTrain))
  print("\tNumber of testing examples = %d" %(nTest))
  return X_train, X_test, y_train, y_test, nF, nC, nTrain, nTest

#Train and test Scikit-Learn models
result = [] #stores accuracy and time for training models
predictedTest = pd.DataFrame()
predictedTest['label'] = y_test

for m in models_used:
  model = models[m]
  print("Training classifier:\n%s\n" %model)

  #train model
  st = time.time()
  model.fit(x_train, y_train)
  tTrain = time.time() - st
  print("Time to train classifier: %4.2f seconds\n" %(tTrain))

  #predict test examples with trained model
  st = time.time() # start time for prediction
  predicted = model.predict(x_test) #predict test labels with trained model
  tTest = time.time() - st #time to predict test examples
  print("Time to test classifier: %4.2f seconds\n" %(tTest))

  #Save trained model
  modelFile = modelDir + m + '.sav' #name for saved Scikit-Learn model file
  pickle.dump(model, open(modelFile, 'wb')) #save model
  print('Trained model saved as %s\n' %modelFile)

  #evaluate prdeiction accuracy on test examples
  acc = evaluatePredictions(m, y_test, predicted) # evaluate prediction accuracy

  result.append([m, acc, tTrain, tTest]) #record results
  predictedTest[m] = predicted #save predicted class
  print(60*'='+'\n') #end training and testing for model

提前谢谢你。

【问题讨论】：

您能展示一下您的训练 csv (q2train.csv) 的样子吗？哪些特征进入特征矩阵“X”，哪些特征（目标）应该是预测“y”的向量？

【参考方案1】：

我不确定我是否正确： 当你从预测有效得到答案时 您可以将其传输到 pandadata 框架，然后将其转换为 csv 文件，如下所示：

y_csv='answer':predict
y_csv=pd.DataFrame(data=y_csv)
y_csv.to_csv('Filename',index=False)