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Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead
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Closed 4 months ago.
I am trying to follow a machine-learning tutorial listed here: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/, but I am encountering an issue. I was able to run the following code on my Macbook air, however, it did not work on my Windows machine. I checked other questions with similar titles, none of which seem to fit my problem.
Why is this happening? How can it be fixed?
My entire code:
# Python version
import sys
print('Python: {}'.format(sys.version))
# scipy
import scipy
print('scipy: {}'.format(scipy.__version__))
# numpy
import numpy
print('numpy: {}'.format(numpy.__version__))
# matplotlib
import matplotlib
print('matplotlib: {}'.format(matplotlib.__version__))
# pandas
import pandas
print('pandas: {}'.format(pandas.__version__))
# scikit-learn
import sklearn
print('sklearn: {}'.format(sklearn.__version__))
# compare algorithms
from pandas import read_csv
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
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
# Load dataset
url = "energyFormatted.csv"
names = ['TOTAL', 'PURCHASED', 'NUCLEAR', 'SOLAR', 'WIND', 'NATURAL_GAS', 'COAL', 'OIL']
dataset = read_csv(url, names=names)
print(dataset.shape)
# Split-out validation dataset
array = dataset.values
X = array[:, 0:4]
y = array[:, 4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1, shuffle=True)
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = StratifiedKFold(n_splits=10, random_state=1, shuffle=True)
cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
results.append(cv_results)
names.append(name)
print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))
The line that's giving me an error:
cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
The error itself:
Traceback (most recent call last):
File "D:\Applications\pythonProject\venv\lib\site-packages\joblib\parallel.py", line 862, in dispatch_one_batch
tasks = self._ready_batches.get(block=False)
File "C:\Users\danie\AppData\Local\Programs\Python\Python39\lib\queue.py", line 168, in get
raise Empty
_queue.Empty
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\danie\AppData\Roaming\JetBrains\PyCharmCE2022.2\scratches\FY23 SCI FAIR\main.py", line 63, in <module>
cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=None)
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_validation.py", line 515, in cross_val_score
cv_results = cross_validate(
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_validation.py", line 266, in cross_validate
results = parallel(
File "D:\Applications\pythonProject\venv\lib\site-packages\joblib\parallel.py", line 1085, in __call__
if self.dispatch_one_batch(iterator):
File "D:\Applications\pythonProject\venv\lib\site-packages\joblib\parallel.py", line 873, in dispatch_one_batch
islice = list(itertools.islice(iterator, big_batch_size))
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_validation.py", line 266, in <genexpr>
results = parallel(
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_split.py", line 340, in split
for train, test in super().split(X, y, groups):
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_split.py", line 86, in split
for test_index in self._iter_test_masks(X, y, groups):
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_split.py", line 717, in _iter_test_masks
test_folds = self._make_test_folds(X, y)
File "D:\Applications\pythonProject\venv\lib\site-packages\sklearn\model_selection\_split.py", line 660, in _make_test_folds
raise ValueError(
ValueError: Supported target types are: ('binary', 'multiclass'). Got 'continuous' instead.
CSV:
28564,0,6284.08,1713.84,19.9948,19994.8,19.9948,19.9948
28411,0,6250.42,852.33,0,20740.03,568.22,0
27515,0,6053.3,550.3,0,20361.1,550.3,0
24586,491.72,5408.92,245.86,0,17947.78,491.72,0
26653,533.06,6130.19,0,0,18923.63,1066.12,0
26836,805.08,6172.28,0,0,18785.2,1073.44,0
26073,1303.65,5736.06,0,0,17990.37,1042.92,0
27055,1352.75,6222.65,0,0,18397.4,1082.2,0
26236,1311.8,6034.28,0,0,17578.12,1311.8,0
26020,1821.4,3903,0,0,18994.6,1040.8,260.2
26538,0,4246.08,265.38,13799.76,6369.12,0,1326.9
25800,3354,5160,0,0,14964,1290,1032
26682,3468.66,5603.22,0,0,14941.92,1600.92,1067.28
24997,3499.58,5499.34,0,0,13248.41,1499.82,1249.85
25100,3765,4769,0,0,13052,1506,2008
24651,4190.67,4930.2,0,0,12325.5,1232.55,1972.08
12053,0,1084.77,0,3133.78,6508.62,0,723.18
11500,2070,2415,0,0,4255,690,2070
Accuracy doesn't make sense for scoring a continuous variable. The error indicates that your y values are of type float (or are otherwise continuous). You could try something like sklearn.metrics.mean_squared_error instead of accuracy.
I'm trying to calculate the accuracy score, of a SVM using Laplacian kernel (as a pre-computed kernel). However, I'm getting the error as below when I try to calculate the accuracy score.
My code :
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.metrics.pairwise import laplacian_kernel
#Load the iris data
iris_data = load_iris()
#Split the data and target
X = iris_data.data
y = iris_data.target
#Convert X and y to a numpy array
X = np.array(X)
y = np.array(y)
#Perform train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42, shuffle=True)
#Using Laplacian kernel - https://scikit-learn.org/stable/modules/metrics.html#laplacian-kernel
K = np.array(laplacian_kernel(X_train, gamma=.5))
svm = SVC(kernel='precomputed').fit(K, np.ravel(y_train))
pred_y = svm.predict(K)
#Print accuracy score - here is where the error is happening.
print(accuracy_score(y_test, pred_y))
When I run this code, I'm getting error as shown below :
Traceback (most recent call last):
File "/Users/user/Desktop/Research/Src/Laplace.py", line 36, in <module>
print(accuracy_score(y_test, pred_y))
File "/Users/user/miniforge3/envs/user_venv/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "/Users/user/miniforge3/envs/user/lib/python3.8/site-packages/sklearn/metrics/_classification.py", line 202, in accuracy_score
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "/Users/user/miniforge3/envs/user/lib/python3.8/site-packages/sklearn/metrics/_classification.py", line 83, in _check_targets
check_consistent_length(y_true, y_pred)
File "/Users/user/miniforge3/envs/user/lib/python3.8/site-packages/sklearn/utils/validation.py", line 262, in check_consistent_length
raise ValueError("Found input variables with inconsistent numbers of"
ValueError: Found input variables with inconsistent numbers of samples: [45, 105]
So how can I resolve this error?
You calculated pred_y using your train inputs which has 105 elements and y_test has 45 elements.
You need to add a step:
#user3046211's code
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.metrics.pairwise import laplacian_kernel
#Load the iris data
iris_data = load_iris()
#Split the data and target
X = iris_data.data
y = iris_data.target
#Convert X and y to a numpy array
X = np.array(X)
y = np.array(y)
#Perform train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42, shuffle=True)
#Using Laplacian kernel - https://scikit-learn.org/stable/modules/metrics.html#laplacian-kernel
K = np.array(laplacian_kernel(X_train, gamma=.5))
svm = SVC(kernel='precomputed').fit(K, np.ravel(y_train))
pred_y = svm.predict(K)
#Print accuracy score - here is where the error is happening.
print(accuracy_score(y_test, pred_y))
# NEW CODE STARTS HERE
K_test = np.array(laplacian_kernel(X=X_test,Y=X_train, gamma=.5))
pred_y_test = svm.predict(K_test)
print(accuracy_score(y_test, pred_y_test))
I am new to this KNN I want to ask a simple question I have written a code in python of KNN. when I used fingerprints.csv that contains decimals number my code gives me an error. I assume that it doesn't predict float values. so I used another CSV that has similar data but no decimal value my code worked well.
what changes I should make so my code will be able to predict floats.
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
import pickle
import glob
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import f1_score
from sklearn.metrics import accuracy_score
from sklearn.multioutput import MultiOutputClassifier
# training/validation set
train_set = pd.read_csv("1.csv")
# test set
test_set = pd.read_csv("testing data.csv")
X = train_set.iloc[:,0:3].values #RSSI
Y = train_set.iloc[:,3:5].values #X,Y (OUTCOME)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)
#print(X_train.shape)
#print(X_test)
#print(Y_train.shape)
#print(Y_test)
sc = StandardScaler() #feature scalin
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
#print(Y_train)
aa =(X_train[:,0]+X_train[:,1]+X_train[:,2])/3
print(aa)
#import math
#print(math.sqrt(len(Y_test)))
knn = KNeighborsClassifier(n_neighbors=1, metric='euclidean')
classifier = MultiOutputClassifier(knn, n_jobs=-2)
classifier.fit(X_train, Y_train)
# Save the trained model as a pickle string.
saved_model = pickle.dumps (classifier)
# Load the pickled model
classifier_from_pickle = pickle.loads(saved_model)
# Use the loaded pickled model to make predictions
classifier_from_pickle.predict(X_test)
Y_pred = classifier.predict(X_test)
print(Y_pred)
a = Y_test[:,0] # actual labels
b = Y_pred[:,0] # predicted labels
acc = len([a[i] for i in range(0, len(a)) if a[i] == b[i]]) / len(a)
a = Y_test[:,1] # actual labels
b = Y_pred[:,1] # predicted labels
accu = len([a[i] for i in range(0, len(a)) if a[i] == b[i]]) / len(a)
accuracy=acc+accu
print("Accuracy: ",accuracy)
#Model Validation on Validation.csv
X = test_set.iloc[:,0:3].values #RSSI
#print(X)
X_test = sc.transform(X)
#print(X_test)
aa =(X_test[:,0]+X_test[:,1]+X_test[:,2])/3
print(aa)
# Use the loaded pickled model to make predictions on Validate Dataset
classifier_from_pickle.predict(X_test)
Y_pred = classifier.predict(X_test)
print(Y_pred)
the error
Traceback (most recent call last):
File "d:/knnn code/knn2.py", line 53, in <module>
classifier.fit(X_train, Y_train)
File "C:\Users\92316\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\multioutput.py", line 359, in fit
super().fit(X, Y, sample_weight)
File "C:\Users\92316\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\multioutput.py", line 156, in fit
check_classification_targets(y)
File "C:\Users\92316\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\utils\multiclass.py", line 169, in check_classification_targets
raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'continuous-multioutput'
thanks in advance for your time and help.
I am trying to implement the Random Forest Classifier in the python but it shows a value error. The example code is:
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv("0.5-1.csv")
df.head()
X = df[['wavelength', 'phase velocity']]
y = df['shear wave velocity']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
print (len(X_train),len(X_test),len(y_train),len(y_test))
rf = RandomForestClassifier(n_estimators=40)
rf.fit(X_train, y_train)
print (rf.score(X_test, y_test))
The error is:
Traceback (most recent call last):
File "G:\My Drive\ANN\test\0.5-1\0.5-1_tunecode.py", line 23, in <module>
rf.fit(X_train, y_train)
File "C:\Users\sadia\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\ensemble\forest.py", line 275, in fit
y, expanded_class_weight = self._validate_y_class_weight(y)
File "C:\Users\sadia\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\ensemble\forest.py", line 478, in _validate_y_class_weight
check_classification_targets(y)
File "C:\Users\sadia\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\utils\multiclass.py", line 169, in check_classification_targets
raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'continuous'
The failure occurs on:
rf.fit(X_train, y_train)
Any help will be greatly appreciated.
This error occurs because you are passing the float value to your classifier which expects categorical values as target vector.Try using the regressor algorithms.
i.e instead of RandomForestClassifier you should use RandomForestRegressor for continuous target vector.
Hope this helps!
I'm trying to run my classifier but I get this error
import pandas
import numpy as np
import pandas as pd
from sklearn import svm
from sklearn.svm import SVC
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
from sklearn.multiclass import OneVsOneClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
dataset = pd.read_csv('all_topics_limpo.csv', encoding = 'utf-8')
data = pandas.get_dummies(dataset['verbatim_corrige'])
labels = dataset['label']
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size = 0.2, random_state = 0)
count_vector = CountVectorizer()
tfidf = TfidfTransformer()
classifier = OneVsOneClassifier(SVC(kernel = 'linear', random_state = 100))
#classifier = LogisticRegression()
train_counts = count_vector.fit_transform(X_train)
train_tfidf = tfidf.fit_transform(train_counts)
classifier.fit(X_train, y_train)
test_counts = count_vector.transform(X_test)
test_tfidf = tfidf.transform(test_counts)
predicted = classifier.predict(test_tfidf)
predicted = classifier.predict(X_test)
print("confusion matrix")
print(confusion_matrix(y_test, predicted, labels = labels))
print("F-score")
print(f1_score(y_test, predicted))
print(precision_score(y_test, predicted))
print(recall_score(y_test, predicted))
print("cross validation")
test_counts = count_vector.fit_transform(data)
test_tfidf = tfidf.fit_transform(test_counts)
scores = cross_validation.cross_val_score(classifier, test_tfidf, labels, cv = 10)
print(scores)
print("Accuracy: {} +/- {}".format(scores.mean(), scores.std() * 2))
My output error:
ValueError: cannot use sparse input in 'SVC' trained on dense data
I can not execute my code because of this problem and I am not understanding anything of what is happening.
all output error
Traceback (most recent call last):
File "classification.py", line 42, in
predicted = classifier.predict(test_tfidf)
File "/usr/lib/python3/dist-packages/sklearn/multiclass.py", line 584, in predict
Y = self.decision_function(X)
File "/usr/lib/python3/dist-packages/sklearn/multiclass.py", line 614, in decision_function
for est, Xi in zip(self.estimators_, Xs)]).T
File "/usr/lib/python3/dist-packages/sklearn/multiclass.py", line 614, in
for est, Xi in zip(self.estimators_, Xs)]).T
File "/usr/lib/python3/dist-packages/sklearn/svm/base.py", line 548, in predict
y = super(BaseSVC, self).predict(X)
File "/usr/lib/python3/dist-packages/sklearn/svm/base.py", line 308, in predict
X = self._validate_for_predict(X)
File "/usr/lib/python3/dist-packages/sklearn/svm/base.py", line 448, in _validate_for_predict
% type(self).name)
ValueError: cannot use sparse input in 'SVC' trained on dense data
You get this error because your training & test data are not of the same kind: while you train in your initial X_train set:
classifier.fit(X_train, y_train)
you are trying to get predictions from a dataset which has undergone count vectorization & tf-idf transormations first:
predicted = classifier.predict(test_tfidf)
It is puzzling why you choose to do so, why you nevertheless compute train_counts and train_tfidf (you don't seem to actually use them anywhere), and why you are also trying to redefine predicted as classifier.predict(X_test) immediately afterwards. Normally, changing your training line to
classifier.fit(train_tfidf, y_train)
and getting rid of your second predicted definition should work OK...
you can use this code :
test_tfidf = tfidf.transform(test_counts).toarray()
befor you want to predict your model and after :
predicted = classifier.predict(test_tfidf)
just do this simple code
nice job