在不同的数据集上运行经过训练的机器学习模型

我是机器学习的新手，正在尝试在另一个相同格式的数据集上运行一个简单的分类模型，该模型是我使用 pickle 训练和保存的。我有以下 python 代码。

Code

#Training set
features = pd.read_csv('../Data/Train_sop_Computed.csv')
#Testing set
testFeatures = pd.read_csv('../Data/Test_sop_Computed.csv')

print(colored('\nThe shape of our features is:','green'), features.shape)
print(colored('\nThe shape of our Test features is:','green'), testFeatures.shape)

features = pd.get_dummies(features)
testFeatures = pd.get_dummies(testFeatures)

features.iloc[:,5:].head(5)
testFeatures.iloc[:,5].head(5)

labels = np.array(features['Truth'])
testlabels = np.array(testFeatures['Truth'])

features= features.drop('Truth', axis = 1)
testFeatures = testFeatures.drop('Truth', axis = 1)

feature_list = list(features.columns)
testFeature_list = list(testFeatures.columns)

def add_missing_dummy_columns(d, columns):
    missing_cols = set(columns) - set(d.columns)
    for c in missing_cols:
        d[c] = 0


def fix_columns(d, columns):
    add_missing_dummy_columns(d, columns)

    # make sure we have all the columns we need
    assert (set(columns) - set(d.columns) == set())

    extra_cols = set(d.columns) - set(columns)
    if extra_cols: print("extra columns:", extra_cols)

    d = d[columns]
    return d


testFeatures = fix_columns(testFeatures, features.columns)

features = np.array(features)
testFeatures = np.array(testFeatures)

train_samples = 100

X_train, X_test, y_train, y_test = model_selection.train_test_split(features, labels, test_size = 0.25, random_state = 42)
testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

print(colored('\n        TRAINING SET','yellow'))
print(colored('\nTraining Features Shape:','magenta'), X_train.shape)
print(colored('Training Labels Shape:','magenta'), X_test.shape)
print(colored('Testing Features Shape:','magenta'), y_train.shape)
print(colored('Testing Labels Shape:','magenta'), y_test.shape)

print(colored('\n        TESTING SETS','yellow'))
print(colored('\nTraining Features Shape:','magenta'), testX_train.shape)
print(colored('Training Labels Shape:','magenta'), textX_test.shape)
print(colored('Testing Features Shape:','magenta'), testy_train.shape)
print(colored('Testing Labels Shape:','magenta'), testy_test.shape)

from sklearn.metrics import precision_recall_fscore_support

import pickle

loaded_model_RFC = pickle.load(open('../other/SOPmodel_RFC', 'rb'))
result_RFC = loaded_model_RFC.score(textX_test, testy_test)
print(colored('Random Forest Classifier: ','magenta'),result_RFC)

loaded_model_SVC = pickle.load(open('../other/SOPmodel_SVC', 'rb'))
result_SVC = loaded_model_SVC.score(textX_test, testy_test)
print(colored('Support Vector Classifier: ','magenta'),result_SVC)

loaded_model_GPC = pickle.load(open('../other/SOPmodel_Gaussian', 'rb'))
result_GPC = loaded_model_GPC.score(textX_test, testy_test)
print(colored('Gaussian Process Classifier: ','magenta'),result_GPC)

loaded_model_SGD = pickle.load(open('../other/SOPmodel_SGD', 'rb'))
result_SGD = loaded_model_SGD.score(textX_test, testy_test)
print(colored('Stocastic Gradient Descent: ','magenta'),result_SGD)

我能够获得测试集的结果。

但我面临的问题是我需要在整个系统上运行模型Test_sop_Computed.csv数据集。但它仅在我分割的测试数据集上运行。 如果有人能够就如何在整个数据集上运行加载的模型提供任何建议，我将不胜感激。我知道下面这行代码出错了。

testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

训练数据集和测试数据集都有Subject, Predicate, Object, Computed and Truth以及具有的功能Truth是预测的类别。测试数据集具有实际值Truth列，我使用它testFeatures = testFeatures.drop('Truth', axis = 1)并打算使用分类器的各种加载模型来预测这一点Truth as 0 or 1对于整个数据集，然后将预测作为数组获取。

到目前为止我已经做到了。但我认为我也在分割我的测试数据集。有没有办法通过整个测试数据集，即使它在另一个文件中？

该测试数据集的格式与训练集相同。我检查了两者的形状，得到以下结果。

确认特征和形状

Shape of the Train features is: (1860, 5)
Shape of the Test features is: (1386, 5)

         TRAINING SET

Training Features Shape: (1395, 1045)
Training Labels Shape: (465, 1045)
Testing Features Shape: (1395,)
Testing Labels Shape: (465,)

          TEST SETS

Training Features Shape: (1039, 1045)
Training Labels Shape: (347, 1045)
Testing Features Shape: (1039,)
Testing Labels Shape: (347,)

任何这方面的建议都将受到高度赞赏。

你的问题有点不清楚，但据我了解，你想在上面运行你的模型测试X_train and on 测试X_测试（这只是测试功能分为两个子数据集）。

所以，你可以在上面运行你的模型测试X_train就像你做的一样测试X_测试, e.g. :

result_RFC_train = loaded_model_RFC.score(textX_train, testy_train)

或者您可以删除以下行：

testX_train, textX_test, testy_train, testy_test = model_selection.train_test_split(testFeatures, testlabels, test_size= 0.25, random_state = 42)

因此，您只需不要拆分数据并在完整数据集上运行它：

result_RFC_train = loaded_model_RFC.score(testFeatures, testlabels)