اذهب إلى المحتوى

Meezo ML

الأعضاء
 عرض الملف الشخصي  تصفح المحتوى

  • المساهمات

    197

  • تاريخ الانضمام

  • تاريخ آخر زيارة

 نوع المحتوى 

ريادة الأعمال

البرمجة

التصميم

DevOps

التسويق والمبيعات

العمل الحر

البرامج والتطبيقات

آخر التحديثات

قصص نجاح

أسئلة وأجوبة

كتب

دورات

أجوبة بواسطة Meezo ML

  3. ظهور الخطأ التالي أثناء قياس كفاءة النموذج ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

    في بايثون

    نشر · تم التعديل في بواسطة Meezo ML

    قمت ببناء مودل LogisticRegression لكن عند محاولة قياس الكفاءة يظهر لي الخطأالتالي: 

    import numpy as np
from tensorflow.keras.datasets import mnist
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import f1_score,precision_score,recall_score,accuracy_score,log_loss
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train.shape
image_size = x_train.shape[1]
input_size = image_size * image_size
x_train = np.reshape(x_train, [-1, input_size])/ 255
x_test = np.reshape(x_test, [-1, input_size]) / 255
#y_train = to_categorical(y_train)
#y_test = to_categorical(y_test)
t =LogisticRegression()
t.fit(x_train, y_train)
f1_score(y_test,t.predict(x_test))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-bbb2bd2287ff> in <module>
     15 t =LogisticRegression()
     16 t.fit(x_train, y_train)
---> 17 f1_score(y_test,t.predict(x_test))
     18 
     19 #ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
   1097                        pos_label=pos_label, average=average,
   1098                        sample_weight=sample_weight,
-> 1099                        zero_division=zero_division)
   1100 
   1101 

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)
   1224                                                  warn_for=('f-score',),
   1225                                                  sample_weight=sample_weight,
-> 1226                                                  zero_division=zero_division)
   1227     return f
   1228 

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
   1482         raise ValueError("beta should be >=0 in the F-beta score")
   1483     labels = _check_set_wise_labels(y_true, y_pred, average, labels,
-> 1484                                     pos_label)
   1485 
   1486     # Calculate tp_sum, pred_sum, true_sum ###

~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
   1314             raise ValueError("Target is %s but average='binary'. Please "
   1315                              "choose another average setting, one of %r."
-> 1316                              % (y_type, average_options))
   1317     elif pos_label not in (None, 1):
   1318         warnings.warn("Note that pos_label (set to %r) is ignored when "

ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

    ما الحل؟

  4. ظهور الخطأ التالي  ValueError: bad input shape  عند محاولة تدريب نموذج باستخدام خوارزمية MultinomialNB

    في علوم الحاسوب

    نشر

    ظهور الخطأ التالي  ValueError: bad input shape  عند محاولة تدريب نموذج باستخدام خوارزمية MultinomialNB.
    الكود مع الخطأ: 

    import numpy as np
from sklearn.naive_bayes import MultinomialNB
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.datasets import mnist
from sklearn.metrics import accuracy_score
(X, Y),(Xtest, Ytest) = mnist.load_data()
Y = to_categorical(Y)
Ytest = to_categorical(Ytest)
X = np.reshape(X, [-1, X.shape[1]*X.shape[1]])
Xtest = np.reshape(Xtest, [-1, input_size])
M =MultinomialNB()
M.fit(X, Y)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-19-161d6ac448e8> in <module>
     10 Xtest = np.reshape(Xtest, [-1, input_size])
     11 t =MultinomialNB()
---> 12 t.fit(X, Y)
     13 accuracy_score(Ytest,t.predict(Xtest)) # 0.8357

~\anaconda3\lib\site-packages\sklearn\naive_bayes.py in fit(self, X, y, sample_weight)
    607         self : object
    608         """
--> 609         X, y = self._check_X_y(X, y)
    610         _, n_features = X.shape
    611         self.n_features_ = n_features

~\anaconda3\lib\site-packages\sklearn\naive_bayes.py in _check_X_y(self, X, y)
    473 
    474     def _check_X_y(self, X, y):
--> 475         return check_X_y(X, y, accept_sparse='csr')
    476 
    477     def _update_class_log_prior(self, class_prior=None):

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_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)
    758                         dtype=None)
    759     else:
--> 760         y = column_or_1d(y, warn=True)
    761         _assert_all_finite(y)
    762     if y_numeric and y.dtype.kind == 'O':

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in column_or_1d(y, warn)
    795         return np.ravel(y)
    796 
--> 797     raise ValueError("bad input shape {0}".format(shape))
    798 
    799 

ValueError: bad input shape (60000, 10)

     

  8. ظهور الخطأ ValueError: np.nan is an invalid document, expected byte or unicode string عند محاولة ترميز النص باستخدام TfidfVectorizer

    في بايثون

    نشر

    أحاول أن أقوم بترميز النص باستخدام ال TF-IDF لكن يظهر لي الخطأ  ValueError: np.nan is an invalid document, expected byte or unicode string. في هذا المقطع البرمجي التالي: 

    from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
data = pd.read_csv("train.csv")
tfidf = TfidfVectorizer(encoding='utf-8',decode_error='replace')
enc = tfidf.fit_transform(data['tweets'])

    ما السبب؟
    ظهور الخطأ ValueError: np.nan is an invalid document, expected byte or unicode string عند محاولة ترميز النص باستخدام TfidfVectorizer

  12. ظهور الخطأ التالي ValueError: Found array with dim 3. Estimator expected <= 2 عند محاولة تدريب نموذج

    في بايثون

    نشر · تم التعديل في بواسطة Meezo ML

    ظهور الخطأ التالي ValueError: Found array with dim 3. Estimator expected <= 2 عند محاولة تدريب نموذج باستخدام خوارزمية  MultinomialNB؟ 

    import numpy as np
from tensorflow.keras.datasets import mnist
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from tensorflow.keras.utils import to_categorical
from sklearn.metrics import f1_score,precision_score,recall_score,accuracy_score,log_loss
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train.shape
t = Pipeline([('clf',MultinomialNB())])
t = text_clf.fit(x_train, y_train)
accuracy_score(y_test,t.predict(x_test))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-25-434bf8f46433> in <module>
     10 # تعريف pipline
     11 t = Pipeline([('clf',MultinomialNB())])
---> 12 t = text_clf.fit(x_train, y_train)
     13 accuracy_score(y_test,t.predict(x_test))

~\anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
    352                                  self._log_message(len(self.steps) - 1)):
    353             if self._final_estimator != 'passthrough':
--> 354                 self._final_estimator.fit(Xt, y, **fit_params)
    355         return self
    356 

~\anaconda3\lib\site-packages\sklearn\naive_bayes.py in fit(self, X, y, sample_weight)
    607         self : object
    608         """
--> 609         X, y = self._check_X_y(X, y)
    610         _, n_features = X.shape
    611         self.n_features_ = n_features

~\anaconda3\lib\site-packages\sklearn\naive_bayes.py in _check_X_y(self, X, y)
    473 
    474     def _check_X_y(self, X, y):
--> 475         return check_X_y(X, y, accept_sparse='csr')
    476 
    477     def _update_class_log_prior(self, class_prior=None):

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_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)
    753                     ensure_min_features=ensure_min_features,
    754                     warn_on_dtype=warn_on_dtype,
--> 755                     estimator=estimator)
    756     if multi_output:
    757         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    572         if not allow_nd and array.ndim >= 3:
    573             raise ValueError("Found array with dim %d. %s expected <= 2."
--> 574                              % (array.ndim, estimator_name))
    575 
    576         if force_all_finite:

ValueError: Found array with dim 3. Estimator expected <= 2.

     

  18. ظهور الخطأ التالي Expected 2D array, got 1D array instead عند محاولة تدريب نموذج في مكتبة Sklearn

    في بايثون

    نشر

    عند محاولة تدريب model ( نموذج ) يظهر لي الخطأ التالي Expected 2D array, got 1D array instead  الكود: 

    from sklearn.preprocessing  import LabelEncoder
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# إنشاءداتا 
data = {'size': [100, 30, 50, 200, 2],
        'class': ['big', 'small', 'medium', 'verybig', 'verysmall']}
df = pd.DataFrame(data)
le = LabelEncoder()
le.fit(df['class'])
df['class'] = le.transform(df['class'])
X_train, X_test, y_train, y_test = train_test_split(df['size'], df['class'], random_state = 42, test_size = 0.33)
linreg = LinearRegression()
linreg.fit(X_train, y_train)
linreg.score(x_test,y_test)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-f740a0daa981> in <module>
     12 X_train, X_test, y_train, y_test = train_test_split(df['size'], df['class'], random_state = 42, test_size = 0.33)
     13 linreg = LinearRegression()
---> 14 linreg.fit(X_train, y_train)
     15 linreg.score(x_test,y_test)

~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py in fit(self, X, y, sample_weight)
    490         n_jobs_ = self.n_jobs
    491         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
--> 492                          y_numeric=True, multi_output=True)
    493 
    494         if sample_weight is not None:

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_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)
    753                     ensure_min_features=ensure_min_features,
    754                     warn_on_dtype=warn_on_dtype,
--> 755                     estimator=estimator)
    756     if multi_output:
    757         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    554                     "Reshape your data either using array.reshape(-1, 1) if "
    555                     "your data has a single feature or array.reshape(1, -1) "
--> 556                     "if it contains a single sample.".format(array))
    557 
    558         # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
array=[ 50 100 200].

     

  20. لماذا cross_val_score لا تعمل مع roc_auc ويظهر لي هذا الخطأ: ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.

    في أسئلة البرمجة

    نشر

    لدي الكود التالي: 

    from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
dataset = load_iris()
Xdata = pd.DataFrame(data=dataset.data, columns=dataset.feature_names)
encoder = OneHotEncoder()
ydata = encoder.fit_transform(pd.DataFrame(dataset.target)).toarray()
model = DecisionTreeClassifier(max_depth=1)
cross_val_score(model, Xdata, ydata, cv=4, scoring="roc_auc")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-6be5be8289ab> in <module>
     11 model = DecisionTreeClassifier(max_depth=1)
     12 
---> 13 cross_val_score(model, Xdata, ydata, cv=4, scoring="roc_auc")

~\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, error_score)
    388                                 fit_params=fit_params,
    389                                 pre_dispatch=pre_dispatch,
--> 390                                 error_score=error_score)
    391     return cv_results['test_score']
    392 

~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
    234             return_times=True, return_estimator=return_estimator,
    235             error_score=error_score)
--> 236         for train, test in cv.split(X, y, groups))
    237 
    238     zipped_scores = list(zip(*scores))

~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
   1002             # remaining jobs.
   1003             self._iterating = False
-> 1004             if self.dispatch_one_batch(iterator):
   1005                 self._iterating = self._original_iterator is not None
   1006 

~\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    833                 return False
    834             else:
--> 835                 self._dispatch(tasks)
    836                 return True
    837 

~\anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    752         with self._lock:
    753             job_idx = len(self._jobs)
--> 754             job = self._backend.apply_async(batch, callback=cb)
    755             # A job can complete so quickly than its callback is
    756             # called before we get here, causing self._jobs to

~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    207     def apply_async(self, func, callback=None):
    208         """Schedule a func to be run"""
--> 209         result = ImmediateResult(func)
    210         if callback:
    211             callback(result)

~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    588         # Don't delay the application, to avoid keeping the input
    589         # arguments in memory
--> 590         self.results = batch()
    591 
    592     def get(self):

~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~\anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~\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, return_estimator, error_score)
    542     else:
    543         fit_time = time.time() - start_time
--> 544         test_scores = _score(estimator, X_test, y_test, scorer)
    545         score_time = time.time() - start_time - fit_time
    546         if return_train_score:

~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _score(estimator, X_test, y_test, scorer)
    589         scores = scorer(estimator, X_test)
    590     else:
--> 591         scores = scorer(estimator, X_test, y_test)
    592 
    593     error_msg = ("scoring must return a number, got %s (%s) "

~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in __call__(self, estimator, *args, **kwargs)
     85             if isinstance(scorer, _BaseScorer):
     86                 score = scorer._score(cached_call, estimator,
---> 87                                       *args, **kwargs)
     88             else:
     89                 score = scorer(estimator, *args, **kwargs)

~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in _score(self, method_caller, clf, X, y, sample_weight)
    330                                                  **self._kwargs)
    331         else:
--> 332             return self._sign * self._score_func(y, y_pred, **self._kwargs)
    333 
    334     def _factory_args(self):

~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in roc_auc_score(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)
    393                                              max_fpr=max_fpr),
    394                                      y_true, y_score, average,
--> 395                                      sample_weight=sample_weight)
    396 
    397 

~\anaconda3\lib\site-packages\sklearn\metrics\_base.py in _average_binary_score(binary_metric, y_true, y_score, average, sample_weight)
    118         y_score_c = y_score.take([c], axis=not_average_axis).ravel()
    119         score[c] = binary_metric(y_true_c, y_score_c,
--> 120                                  sample_weight=score_weight)
    121 
    122     # Average the results

~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in _binary_roc_auc_score(y_true, y_score, sample_weight, max_fpr)
    219     """Binary roc auc score"""
    220     if len(np.unique(y_true)) != 2:
--> 221         raise ValueError("Only one class present in y_true. ROC AUC score "
    222                          "is not defined in that case.")
    223 

ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.

     

  25. ظهور الخطأ التالي أثناء تدريب ال ValueError: Unknown label type: 'continuous-multioutput'   Model

    في أسئلة البرمجة

    نشر · تم التعديل في بواسطة Meezo ML

    قمت ببناء نموذج، لكن يظهر لي دوماً الخطأ التالي: 

    #استيراد المكتبات
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
#تحميل البيانات
data = load_breast_cancer().data
labels = load_breast_cancer().target
# تقسيم البيانات
X_train, y_train,X_test, y_test = train_test_split(data, labels, test_size=0.2, random_state=2021, shuffle =True)
c1 = LogisticRegression(multi_class='multinomial', random_state=1)
c2 = RandomForestClassifier(n_estimators=50, random_state=1)
c3 = GaussianNB()
ec1 = VotingClassifier(estimators=[ ('lr', c1), ('rf', c2), ('gnb', c3)], voting='hard')
ec1 = ec1.fit(X_train, y_train)
print(ec1.score(X_test,y_test))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-16-46caf1469fab> in <module>
     17 
     18 ec1 = VotingClassifier(estimators=[ ('lr', c1), ('rf', c2), ('gnb', c3)], voting='hard')
---> 19 ec1 = ec1.fit(X_train, y_train)
     20 print(ec1.score(X_test,y_test))#0.9385964912280702

~\anaconda3\lib\site-packages\sklearn\ensemble\_voting.py in fit(self, X, y, sample_weight)
    207 
    208         """
--> 209         check_classification_targets(y)
    210         if isinstance(y, np.ndarray) and len(y.shape) > 1 and y.shape[1] > 1:
    211             raise NotImplementedError('Multilabel and multi-output'

~\anaconda3\lib\site-packages\sklearn\utils\multiclass.py in check_classification_targets(y)
    167     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
    168                       'multilabel-indicator', 'multilabel-sequences']:
--> 169         raise ValueError("Unknown label type: %r" % y_type)
    170 
    171 

ValueError: Unknown label type: 'continuous-multioutput'

     

×
×
  • أضف...