Pythonマシンの学習エラー...どのように解決するには？

Question

タイトルをどのように言葉にするかわからないので、私の謝罪...

私はPython Machine Learningを初めて学び、まだ自分自身を学んでいます。私は、このデータセット（ML_TEST）があります。

Sale ID Amount in $ Region  Product Salesperson Win_Lose 
    1   500  North  ink    Jon  1 
    2   250  North  ink    Jon  0 
    3   250  North  ink    Jon  0 
    4   750  North  paper   Jon  0 
    5   800  North  ink    Bill 0 
    6   250  North  paper   Bill 1 
    7   750  North  paper   Jon  1 
    8   250  North  ink    Bill 1 
    9   250  North  paper   Dave 0 
    10   800  North  desk chair  Bill 1 
    11   750  South  paper   Dave 0 
    12   500  South  desk chair  Dave 1 
    13   500  South  ink    Bill 1 
    14   500  South  ink    Bill 0 
    15   400  South  paper   Jon  0 
    16   250  South  paper   Jon  0 
    17   250  South  ink    Jon  1 
    18   250  East  ink    Dave 1 
    19   250  East  ink    Bill 1 
    20   400  East  ink    Jon  0 
    21   400  East  paper   Dave 1 
    22   500  West  desk chair  Bill 0 
    23   750  West  desk chair  Jon  1 
    24   800  West  desk chair  Jon  0 
    25   450  West  paper   Jon  0 
    26   250  West  ink    Dave 1 
    27   250  West  paper   Dave 1 
    28   250  West  paper   Bill 1 
    29   250  West  paper   Bill 0 
    30   400  West  ink    Bill 1 

をそして私は私がこれを実行したときに私が手にエラーを理解しようとしています：

ValueError        Traceback (most recent call last) 
<ipython-input-119-86bed78dded1> in <module>() 
    12 for name, model in models: 
    13   kfold = model_selection.KFold(n_splits=12, random_state=seed) 
---> 14   cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) 
    15   results.append(cv_results) 
    16   names.append(name) 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch) 
    138            train, test, verbose, None, 
    139            fit_params) 
--> 140      for train, test in cv_iter) 
    141  return np.array(scores)[:, 0] 
    142 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable) 
    756    # was dispatched. In particular this covers the edge 
    757    # case of Parallel used with an exhausted iterator. 
--> 758    while self.dispatch_one_batch(iterator): 
    759     self._iterating = True 
    760    else: 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator) 
    606     return False 
    607    else: 
--> 608     self._dispatch(tasks) 
    609     return True 
    610 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch) 
    569   dispatch_timestamp = time.time() 
    570   cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self) 
--> 571   job = self._backend.apply_async(batch, callback=cb) 
    572   self._jobs.append(job) 
    573 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback) 
    107  def apply_async(self, func, callback=None): 
    108   """Schedule a func to be run""" 
--> 109   result = ImmediateResult(func) 
    110   if callback: 
    111    callback(result) 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch) 
    324   # Don't delay the application, to avoid keeping the input 
    325   # arguments in memory 
--> 326   self.results = batch() 
    327 
    328  def get(self): 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0) 
    129 
    130  def __call__(self): 
--> 131   return [func(*args, **kwargs) for func, args, kwargs in self.items] 
    132 
    133  def __len__(self): 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score) 
    236    estimator.fit(X_train, **fit_params) 
    237   else: 
--> 238    estimator.fit(X_train, y_train, **fit_params) 
    239 
    240  except Exception as e: 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight) 
    1171 
    1172   X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, 
-> 1173       order="C") 
    1174   check_classification_targets(y) 
    1175   self.classes_ = np.unique(y) 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator) 
    519  X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite, 
    520      ensure_2d, allow_nd, ensure_min_samples, 
--> 521      ensure_min_features, warn_on_dtype, estimator) 
    522  if multi_output: 
    523   y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False, 

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator) 
    380          force_all_finite) 
    381  else: 
--> 382   array = np.array(array, dtype=dtype, order=order, copy=copy) 
    383 
    384   if ensure_2d: 

ValueError: could not convert string to float: 'Jon' 

：

#Load Libraries 
import pandas 
from pandas.tools.plotting import scatter_matrix 
import matplotlib.pyplot as plt 
from sklearn import model_selection 
from sklearn.metrics import classification_report 
from sklearn.metrics import confusion_matrix 
from sklearn.metrics import accuracy_score 
from sklearn.linear_model import LogisticRegression 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis 
from sklearn.naive_bayes import GaussianNB 
from sklearn.svm import SVC 
import pyodbc 
conn = pyodbc.connect('') 

sql = "Select * from TMP.ML_TEST" 
dataset = pd.read_sql(sql, conn) 

array = dataset.values 
X = array[:,0:5] 
Y = array[:,5] 
validation_size = 0.20 
seed = 7 
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed) 
print(Y) 

seed = 7 
scoring = 'accuracy' 


models = [] 
models.append(('LR', LogisticRegression())) 
models.append(('LDA', LinearDiscriminantAnalysis())) 
models.append(('KNN', KNeighborsClassifier())) 
models.append(('CART', DecisionTreeClassifier())) 
models.append(('NB', GaussianNB())) 
models.append(('SVM', SVC())) 
# evaluate each model in turn 
results = [] 
names = [] 
for name, model in models: 
    kfold = model_selection.KFold(n_splits=12, random_state=seed) 
    cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) 
    results.append(cv_results) 
    names.append(name) 
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) 
    print(msg) 

、ここでは、私が取得エラーです

多くの機能がテキストであるため、Naive-Bayesモデルを使用したいと思っていますが、このエラーを回避することはできません:(

私は、売り上げがこれらの機能に基づいて勝ち負けるかどうかを予測するモデルを構築しようとしています。

Answer 1

stringsからなるフィーチャベクタにMLテクニックを適用しようとしています。数学的演算はdouble/floatの値に対してのみ定義されるため、これは不可能です。このエラーを解決するには、この文字列ラベルを数値表現に変換する必要があります。これを行うには、sklearn.preprocessing.LabelEncoderを使用します。

>>> le = preprocessing.LabelEncoder() 
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) 
LabelEncoder() 
>>> list(le.classes_) 
['amsterdam', 'paris', 'tokyo'] 
>>> le.transform(["tokyo", "tokyo", "paris"]) 
array([2, 2, 1]...) 
>>> list(le.inverse_transform([2, 2, 1])) 
['tokyo', 'tokyo', 'paris'] 

さらに詳しい情報はdocumentationにあります。