Question

我有一个csv文件，其中包含位置，纬度，经度，犯罪类型等详细信息。我想制作一个分类器，以便在给定其他属性的情况下预测犯罪类型。现在，我想使用sklearn训练它，但sklearn需要浮动值。

    import os
    import glob
    import pandas as pd
    from sklearn.cross_validation import train_test_split
    from sklearn import svm
    from sklearn import preprocessing
    from sklearn.feature_extraction.text import TfidfTransformer

    input_file_name = []
    input_file = []
    frame = pd.DataFrame()
    fields = ['Reported by', 'Falls within', 'Longitude', 'Latitude', 'Location', 'LSOA code', 'LSOA name', 'Crime type']
    to_drop = ['No Location']

    for filename in glob.glob(os.path.join('/home/prakhar/Programming/Minor/2017-01', '*.csv')):
            input_file_name.append(filename)

    for ifn in input_file_name:
        input_file.append(pd.read_csv(ifn, error_bad_lines=False, skipinitialspace=True, usecols=fields));

    frame = pd.concat(input_file, ignore_index=True)    
    frame = frame[~frame['Location'].isin(to_drop)]

    x, y = frame.iloc[:,:-1], frame.iloc[:, -1]
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)

frame.head()

的输出

         Reported by          Falls within  Longitude   Latitude  \
0  Thames Valley Police  Thames Valley Police  -0.972323  51.991460   
1  Thames Valley Police  Thames Valley Police  -0.981511  51.997312   
2  Thames Valley Police  Thames Valley Police  -0.970516  51.992128   
3  Thames Valley Police  Thames Valley Police  -0.973412  51.992225   
4  Thames Valley Police  Thames Valley Police  -0.984120  51.997263   

                     Location  LSOA code            LSOA name  \
0      On or near Osprey Walk  E01017648  Aylesbury Vale 001A   
1  On or near Portfield Close  E01017648  Aylesbury Vale 001A   
2      On or near Lime Avenue  E01017648  Aylesbury Vale 001A   
3     On or near Martin Close  E01017648  Aylesbury Vale 001A   
4   On or near Mckenzie Close  E01017649  Aylesbury Vale 001B   

                  Crime type  
0      Anti-social behaviour  
1      Anti-social behaviour  
2                Other theft  
3              Vehicle crime  
4  Criminal damage and arson

Answer 1

尝试以下方法：

from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler

y = frame['Crime type'].values
# convert the labels from strings to numbers (0,1,2....)
y = LabelEncoder().fit_transform(y)

frame = frame.drop(['Crime type'], axis=1)

for f in frame.columns:
    if frame[f].dtype == 'object':
        lbl_enc = LabelEncoder()
        # same as above encoding. it takes every object dtype from 
        # pandas dataframe and converts to numerical labels
        frame[f] = lbl_enc.fit_transform(frame[f].values)

X = frame.values
# binarize the encoded columns. this is not needed if you are using a tree based algorithm
ohe = OneHotEncoder(categorical_features=[0, 1, 4, 5, 6])
X = ohe.fit_transform(X)

# use the following for SVMs (with_mean=False for sparse data)
scl = StandardScaler(with_mean=False)
X = scl.fit_transform(X)
# fit model here: model.fit(X, y)

如何使用包含字符串作为目标（y）和特征（X）值的pandas数据帧来训练svm？

1 个答案: