Индекс за пределами: установка SSVM с использованием Pystruct

Я пытаюсь установить SSVM, как показано на странице примеров: https://github.com/pystruct/pystruct/blob/master/examples/multi_class_svm.py

Код работает отлично с данным примером, но не с моими собственными данными.

Входной набор данных представляет собой файл csv со следующими столбцами и строками, например:

user_name facility start_date day_of_week monthweek month TestUserA FacilityA 2/1/2015 1 1 2 ... ... ... ... ... ... 

из вышеперечисленных столбцов, единственными предикторами, которые я использую, являются «объект» и day_of_week '

и я генерирую ярлыки, объединяя 3-значную строку dayOfWeek со средством:

 for example MONFacilityA 

Структура моих данных следующая:

 ('Predictors shape: ', (518, 2)) ('Labels shape: ', (518,)) ('X_train type: ', <type 'numpy.ndarray'>) ('X_train shape: ', (440, 2)) ('X_test shape: ', (78, 2)) ('y_train type: ', <type 'numpy.ndarray'>) ('y_train shape: ', (440,)) ('y_test shape: ', (78,)) ('X_train example 1st row element type: ', <type 'numpy.int64'>) ('y_train example 1st row element type: ', <type 'numpy.int64'>) ('Unique labels: len(np.unique(y_train)) ', 20) 

.. и мои метки начинаются с 0 … до … n, как указано в этом сообщении: IndexError при установке SSVM-модели в PyStruct

Но я получаю эту ошибку:

  Traceback (most recent call last): File "userSchedulePredictor_MultiClassSVM.py", line 151, in <module> one_slack_svm.fit(X_train_bias, y_train) File "/usr/local/lib/python2.7/dist-packages/pystruct/learners/one_slack_ssvm. py", line 455, in fit X, Y, joint_feature_gt, constraints) File "/usr/local/lib/python2.7/dist-packages/pystruct/learners/one_slack_ssvm. py", line 355, in _find_new_constraint X, Y, self.w, relaxed=True) File "/usr/local/lib/python2.7/dist-packages/pystruct/models/unstructured_svm. py", line 323, in batch_loss_augmented_inference scores[other_classes] += np.repeat(self.class_weight[Y], IndexError: index 20 is out of bounds for size 20 *** Error in `python': double free or corruption (!prev): 0x000000000228af90 *** Aborted (core dumped) 

Это мой код:

 # Imports import os import numpy as np import pandas as pd # Fetch paths workingDir = os.getcwd() print("Current working directory: ", workingDir) # Get the raw csv data rawData = pd.read_csv(workingDir + "/data/data2.csv") # print('Dataset columns names: ', list(rawData.columns.values)) # print('Unique Days_of_week from dataset: ', np.unique(rawData['day_of_week'])) # print('Unique facility names from dataset: ', np.unique(rawData['facility'])) # Keep only the columns that are needed for prediction data = pd.DataFrame({'facility' : rawData['facility'], 'day_of_week': rawData['day_of_week'], 'currentState': '', 'nextState': ''}) weekDays = { 1 : 'SUN', 2 : 'MON', 3 : 'TUE', 4 : 'WED', 5 : 'THU', 6 : 'FRI', 7 : 'SAT' } # Fill currentState column for i, row in data.iterrows(): facility = row['facility'] weekDayStr = weekDays[row['day_of_week']] nextStateStr = weekDayStr + row['facility'] data.ix[i, 'currentState'] = nextStateStr #data.ix[i, 'day_of_week'] = weekDayStr # Fill next state column for i, row in data.iterrows(): if i - 1 and (i-1) > 0: data.ix[i - 1, 'nextState'] = row['currentState'] # Remove unwanted columns del data['currentState'] #Keep only the rows which have nextState values data = data[data.nextState != ''] predictors = pd.DataFrame({'facility' : data['facility'], 'day_of_week': data['day_of_week']}) #labels = pd.DataFrame({'nextState' : data['nextState']}) #labels = np.array(pd.Series(data['nextState'])) tempLabels = pd.DataFrame({'nextState' : data['nextState']}) labels = pd.Series(data['nextState']) #Convert labels to INT values uniqueLabels = labels.unique() strLabelToInt = {} for i in range(len(uniqueLabels)): strLabelToInt[uniqueLabels[i]] = i print('Label lookup dictionary: ', strLabelToInt) #Convert labels to INT values # uniqueLabels = pd.unique(labels['nextState']) # strLabelToInt = {} # for i in range(len(uniqueLabels)): # strLabelToInt[uniqueLabels[i]] = i #print('Label lookup dictionary: ', strLabelToInt) # for i, row in labels.iterrows(): # labels.ix[i, 'nextState'] = int(strLabelToInt[row['nextState']]) for i, row in tempLabels.iterrows(): tempLabels.ix[i, 'nextState'] = int(strLabelToInt[row['nextState']]) labels.update(tempLabels['nextState']) labels = np.array(labels, dtype='int64') # Convert labels to numpy array # labels = labels.values #Perform Imputation on data predictors = predictors.dropna() # Perform One-hot encoding on Categorical variables in the dataset #predictors = pd.get_dummies(predictors) #Convert predictors to INT values uniqueFacilityNames = pd.unique(predictors['facility']) strFacilityToInt = {} for i in range(len(uniqueFacilityNames)): strFacilityToInt[uniqueFacilityNames[i]] = i + 1 print('Facility lookup dictionary: ', strFacilityToInt) tempFacilityLabels = pd.DataFrame({'facility' : predictors['facility']}) for i, row in tempFacilityLabels.iterrows(): tempFacilityLabels.ix[i, 'facility'] = int(strFacilityToInt[row['facility']]) predictors['facility'].update(tempFacilityLabels['facility']) predictors = np.array(predictors, dtype='int64') #Create train/test split from sklearn.cross_validation import train_test_split print('Predictors shape: ',predictors.shape) print('Labels shape: ', labels.shape) X_train, X_test, y_train, y_test = train_test_split(predictors, labels, test_size=0.15, random_state=0) # convert train/test data to numpy arrays (Not sure if PyStruct supports pandas df's) #X_train = X_train.values #X_test = X_test.values print('X_train type: ', type(X_train)) print('X_train shape: ', X_train.shape) print('X_test shape: ', X_test.shape) print('y_train type: ', type(y_train)) print('y_train shape: ', y_train.shape) print('y_test shape: ', y_test.shape) print('X_train example 1st row element type: ', type(X_train[0][0])) print('y_train example 1st row element type: ', type(y_train[0])) X_train_bias = np.hstack([X_train, np.ones((X_train.shape[0], 1))]) X_test_bias = np.hstack([X_test, np.ones((X_test.shape[0], 1))]) from time import time from pystruct.models import MultiClassClf from pystruct.learners import (NSlackSSVM, OneSlackSSVM,SubgradientSSVM, FrankWolfeSSVM) target_class_count = len(np.unique(y_train)) print('Unique labels: ', len(uniqueLabels)) model = MultiClassClf(n_features=X_train_bias.shape[1], n_classes=target_class_count) n_slack_svm = NSlackSSVM(model, verbose=2, check_constraints=False, C=0.1, batch_size=100, tol=1e-2) one_slack_svm = OneSlackSSVM(model, verbose=2, C=.10, tol=.001) subgradient_svm = SubgradientSSVM(model, C=0.1, learning_rate=0.000001, max_iter=1000, verbose=0) fw_bc_svm = FrankWolfeSSVM(model, C=.1, max_iter=50) fw_batch_svm = FrankWolfeSSVM(model, C=.1, max_iter=50, batch_mode=True) # n-slack cutting plane ssvm #start = time() #n_slack_svm.fit(X_train_bias, y_train) #time_n_slack_svm = time() - start #y_pred = np.hstack(n_slack_svm.predict(X_test_bias)) #print("Score with pystruct n-slack ssvm: %f (took %f seconds)" # % (np.mean(y_pred == y_test), time_n_slack_svm)) ## 1-slack cutting plane ssvm start = time() one_slack_svm.fit(X_train_bias, y_train) time_one_slack_svm = time() - start y_pred = np.hstack(one_slack_svm.predict(X_test_bias)) print("Score with pystruct 1-slack ssvm: %f (took %f seconds)" % (np.mean(y_pred == y_test), time_one_slack_svm)) #online subgradient ssvm start = time() subgradient_svm.fit(X_train_bias, y_train) time_subgradient_svm = time() - start y_pred = np.hstack(subgradient_svm.predict(X_test_bias)) print("Score with pystruct subgradient ssvm: %f (took %f seconds)" % (np.mean(y_pred == y_test), time_subgradient_svm)) # the standard one-vs-rest multi-class would probably be as good and faster # but solving a different model libsvm = LinearSVC(multi_class='crammer_singer', C=.1) start = time() libsvm.fit(X_train, y_train) time_libsvm = time() - start print("Score with sklearn and libsvm: %f (took %f seconds)" % (libsvm.score(X_test, y_test), time_libsvm)) start = time() fw_bc_svm.fit(X_train_bias, y_train) y_pred = np.hstack(fw_bc_svm.predict(X_test_bias)) time_fw_bc_svm = time() - start print("Score with pystruct frankwolfe block coordinate ssvm: %f (took %f seconds)" % (np.mean(y_pred == y_test), time_fw_bc_svm)) start = time() fw_batch_svm.fit(X_train_bias, y_train) y_pred = np.hstack(fw_batch_svm.predict(X_test_bias)) time_fw_batch_svm = time() - start print("Score with pystruct frankwolfe batch ssvm: %f (took %f seconds)" % (np.mean(y_pred == y_test), time_fw_batch_svm)) 

Я попытался использовать 1-горячее представление моих данных (int numpy массивов), но напрасно … Связано ли это с совместным_файлом (x, y), описанным в wiki? https://pystruct.github.io/user_guide.html

Если да, может кто-то пожалуйста пролить свет на то, что я здесь отсутствует? Я не уверен, что понимаю, что X, Y должны иметь определенную форму и т. Д., Чтобы это хорошо работало …

Пожалуйста, порекомендуйте,

Благодаря!

• Слияние с массивами numpy с различными размерами столбцов
• Как использовать Tensorflow для обработки сигналов?
• Подвыборки + классификация с использованием scikit-learn
• sklearn.ensemble.AdaBoostClassifier не может использовать SVM как base_estimator?
• Признание мниста с использованием кера