I have tried the following code and this error has been occuring to me
Link for DataSet is in link bellow
ValueError
---> line 18 ds1_model.fit(X, y)
ValueError: could not convert string to float: 'Iris-setosa'
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv'
ds1 = pd.read_csv(url)
ds1.columns = (['SepalLength' , 'SepalWidth' , 'PetalLength' , 'PetalWidth' , 'ClassLabel'])
ds1_filtered=ds1.dropna(axis=0)
y = ds1_filtered.ClassLabel
ds1_features = ['SepalLength' , 'SepalWidth' , 'PetalLength' , 'PetalWidth']
X = ds1_filtered[ds1_features]
ds1_model = DecisionTreeRegressor()
ds1_model.fit(X, y)
PredictedClassLabel = ds1_model.predict(X)
mean_absolute_error(y, PredictedClassLabel)
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)
ds1_model = DecisionTreeRegressor()
ds1_model.fit(train_X, train_y)
predicitions = ds1_model.predict(val_X)
print(mean_absolute_error(val_y, predictions))
can you please help to suggest or explain how to fix this?
DataSet Link
As the name ClassLabel implies, the iris dataset is a classification and not a regression one; hence, neither DecisionTreeRegressor is the correct model to use nor mean_absolute_error is the correct metric.
You should use a DecisionTreeClassifier and accuracy_score instead:
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = load_iris()
clf = DecisionTreeClassifier()
train_X, val_X, train_y, val_y = train_test_split(iris.data, iris.label, random_state = 0)
clf.fit(train_X, train_Y)
pred = clf.predict(val_X)
print(accuracy_score(val_y, pred))
The scikit-learn decision tree classification tutorial using the said dataset can give you more ideas.
Related
I have build a random forest classifier using scikit learn and python, and I am having trouble actually feeding data in to see the prediction. I want to see the format of the output, and to convert this to a json file. I have attached the code for the random forest and what the data looks like. I believe I need to use 'y_pred', but I am not sure what format the input data needs to be.
X = dataset.iloc[:, 2:4].values
y = dataset["pages"]
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=20, random_state = 0)
classifier = classifier.fit(X_train,y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
You can simply concatenate the predicted values with the matrix of features.
Also note that the pipeline is exactly for this purpose, when you first want to transform the data and then apply some classifier.
This should work for you:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
X = dataset.iloc[:, 2:4].values
y = dataset["pages"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
classifier = make_pipeline(StandardScaler(), RandomForestClassifier(n_estimators=20, random_state=0))
classifier = classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
pred = pd.concat([X_test, pd.Series(y_pred, name="pages")], axis=1)
I have the following code, where i predict a value from 4 input values:
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
data = np.loadtxt('C:/Users/hedeg/Desktop/RulaSoftEdgePrediction.txt')
X_train = np.array(data[0:3500,0:4])
y_train = np.array(data[0:3500,4])
X_test = np.array(data[3500::,0:4])
y_test = np.array(data[3500::,4])
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(X_train, y_train)
I get this error msg:
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([1. , 1.1, 1.2, ..., 3. , 3. , 3. ]),)
How can i solve this problem?
Try to use this one:
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
# generate 2d classification dataset
X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
# fit final model
model = LogisticRegression()
model.fit(X, y)
# example of training a final classification model
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
# generate 2d classification dataset
X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
# fit final model
model = LogisticRegression()
model.fit(X, y)
I'm trying to make a text classifier
import pandas as pd
import pandas
from sklearn import svm
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsOneClassifier
from sklearn.svm import SVC
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
dataset = pd.read_csv('data.csv', encoding = 'utf-8')
data = dataset['text']
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 = 84))
train_counts = count_vector.fit_transform(X_train)
train_tfidf = tfidf.fit_transform(train_counts)
classifier.fit(train_tfidf, y_train)
test_counts = count_vector.transform(X_test)
test_tfidf = tfidf.transform(test_counts)
classifier.predict(test_tfidf)
fit_classifier(X_train, y_train)
predicted = predict(X_test)
print("confusion matrix")
print(confusion_matrix(X_test, predicted, labels = labels))
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))
But I have the following error and I can not understand.
Traceback (most recent call last):
File "classificacao.py", line 37, in
fit_classifier(X_train, y_train)
NameError: name 'fit_classifier' is not defined
But fit is not always defined by default?
you are calling a non existing function:
fit_classifier(X_train, y_train)
to fit your classifier you would use
classifier.fit(X_train, y_train)
instead.
You'll get the same error when trying to predict your test data.
You need to change
predicted = predict(X_test)
to
predicted = classifier.predict(X_test)
Your Confusionmatrix should get your labels, not your test data:
print(confusion_matrix(y_test, predicted, labels = labels))
I am trying to calculate roc_auc for hard votingclassifier that i build . i present the code with reprodcible example. now i want to calculate the roc_auc score and plot ROC curver but unfortunately i got the following error predict_proba is not available when voting='hard'
# Voting Ensemble for Classification
import pandas
from sklearn import datasets
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
from sklearn.metrics import make_scorer,confusion_matrix, f1_score, precision_score, recall_score, cohen_kappa_score,accuracy_score,roc_curve
import numpy as np
np.random.seed(42)
iris = datasets.load_iris()
X = iris.data[:, :4] # we only take the first two features.
Y = iris.target
print(Y)
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
# create the sub models
estimators = []
model1 = LogisticRegression()
estimators.append(('logistic', model1))
model2 = RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0)
estimators.append(('RandomForest', model2))
model3 = MultinomialNB()
estimators.append(('NaiveBayes', model3))
model4=SVC(probability=True)
estimators.append(('svm', model4))
model5=DecisionTreeClassifier()
estimators.append(('Cart', model5))
# create the ensemble model
print('Majority Class Labels (Majority/Hard Voting)')
ensemble = VotingClassifier(estimators,voting='hard')
#accuracy
results = model_selection.cross_val_score(ensemble, X, Y, cv=kfold,scoring='accuracy')
y_pred = cross_val_predict(ensemble, X ,Y, cv=10)
print("Accuracy ensemble model : %0.2f (+/- %0.2f) " % (results.mean(), results.std() ))
print(results.mean())
#recall
recall_scorer = make_scorer(recall_score, pos_label=1)
recall = cross_val_score(ensemble, X, Y, cv=kfold, scoring=recall_scorer)
print('Recall', np.mean(recall), recall)
# Precision
precision_scorer = make_scorer(precision_score, pos_label=1)
precision = cross_val_score(ensemble, X, Y, cv=kfold, scoring=precision_scorer)
print('Precision', np.mean(precision), precision)
#f1_score
f1_scorer = make_scorer(f1_score, pos_label=1)
f1_score = cross_val_score(ensemble, X, Y, cv=kfold, scoring=f1_scorer)
print('f1_score ', np.mean(f1_score ),f1_score )
#roc_auc_score
roc_auc_score = cross_val_score(ensemble, X, Y, cv=kfold, scoring='roc_auc')
print('roc_auc_score ', np.mean(roc_auc_score ),roc_auc_score )
To calculate the roc_aucmetric you first need to
Replace: ensemble = VotingClassifier(estimators,voting='hard')
with: ensemble = VotingClassifier(estimators,voting='soft').
Next, the last 2 lines of code will throw an error:
roc_auc_score = cross_val_score(ensemble, X, Y, cv=3, scoring='roc_auc')
print('roc_auc_score ', np.mean(roc_auc_score ),roc_auc_score )
ValueError: multiclass format is not supported
This is normal since in Y you have 3 classes (np.unique(Y) == array([0, 1, 2])).
You can't use roc_auc as a single summary metric for multiclass models. If you want, you could calculate **per-class roc_auc.**
How to solve this:
1) Use only two classes to calculate the roc_auc_score
2) use label binarization in advance vefore calling roc_auc_score
I am running the [code] of multi-label classification1.how to fix the NameError that the "X_train" is not defined.the python code is given below.
import scipy
from scipy.io import arff
data, meta = scipy.io.arff.loadarff('./yeast/yeast-train.arff')
from sklearn.datasets import make_multilabel_classification
# this will generate a random multi-label dataset
X, y = make_multilabel_classification(sparse = True, n_labels = 20,
return_indicator = 'sparse', allow_unlabeled = False)
# using binary relevance
from skmultilearn.problem_transform import BinaryRelevance
from sklearn.naive_bayes import GaussianNB
# initialize binary relevance multi-label classifier
# with a gaussian naive bayes base classifier
classifier = BinaryRelevance(GaussianNB())
# train
classifier.fit(X_train, y_train)
# predict
predictions = classifier.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test,predictions)
You forgot to split the dataset into train and test sets.
Import the library
from sklearn.model_selection import train_test_split
Add this line before classifier.fit()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train does not exist, you have to split between train and test :
from sklearn.preprocessing import StandardScaler
s =StandardScaler()
X_train = s.fit_transform(X_train)
X_test = s.fit_transform(X_test)