本文整理汇总了Python中sklearn.utils.extmath.density函数的典型用法代码示例。如果您正苦于以下问题：Python density函数的具体用法？Python density怎么用？Python density使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了density函数的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_density
def test_density():
rng = np.random.RandomState(0)
X = rng.randint(10, size=(10, 5))
X[1, 2] = 0
X[5, 3] = 0
X_csr = sparse.csr_matrix(X)
X_csc = sparse.csc_matrix(X)
X_coo = sparse.coo_matrix(X)
X_lil = sparse.lil_matrix(X)
for X_ in (X_csr, X_csc, X_coo, X_lil):
assert_equal(density(X_), density(X))
开发者ID:93sam，项目名称:scikit-learn，代码行数:12，代码来源:test_extmath.py
示例2: getReport
def getReport(self,save = 1, get_top_words = 0): # returns report
report = ""
if get_top_words == 1:
if hasattr(self.mlModel, 'coef_'):
report += "Dimensionality: " + str(self.mlModel.coef_.shape[1])
report += "\nDensity: " + str(density(self.mlModel.coef_))
rank = np.argsort(self.mlModel.coef_[0])
top10 = rank[-20:]
bottom10 = rank[:20]
report += "\n\nTop 10 keywords: "
report += "\nPositive: " + (" ".join(self.feature_names[top10]))
report += "\nNegative: " + (" ".join(self.feature_names[bottom10]))
score = metrics.accuracy_score(self.y_test, self.y_pred)
report += "\n\nAccuracy: " + str(score)
report += "\nClassification report: "
report += "\n\n" + str(metrics.classification_report(self.y_test, self.y_pred,target_names=["Negative","Positive"]))
report += "\nConfusion matrix: "
report += "\n\n" + str(metrics.confusion_matrix(self.y_test, self.y_pred)) + "\n\n"
if save == 1:
with open(self.model_path + "report.txt", "w") as text_file:
text_file.write(report)
return report
开发者ID:tpsatish95，项目名称:Universal-MultiDomain-Sentiment-Classifier，代码行数:26，代码来源:learner.py
示例3: _classify
def _classify(clf, cluster_data, X_train, y_train, X_test, feature_names,
categories, c_params):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
cluster_data.cluster_of_posts = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if feature_names is not None:
print("top 10 keywords per class:")
for i, category in enumerate(categories):
top10 = np.argsort(clf.coef_[i])[-10:]
print(trim("%s: %s"
% (category, " ".join(feature_names[top10]))))
print()
if c_params.is_report_printed:
print("classification report:")
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, train_time, test_time
开发者ID:lpritchett，项目名称:team-amzn-1，代码行数:33，代码来源:classification.py
示例4: report_accuracy
def report_accuracy(model, categories, test_target, predicted):
score = metrics.f1_score(test_target, predicted)
print "f1-score: {:.3f}".format(score)
clf = model.named_steps['clf']
if hasattr(clf, 'coef_'):
coef = model.named_steps['clf'].coef_
print "dimensionality: {}".format(coef.shape[1])
print "density: {}".format(density(coef))
print "top 15 keywords per class:"
feature_names = np.asarray(model.named_steps['vect'].get_feature_names())
for i, category in enumerate(categories):
topkw = np.argsort(coef[i])[-15:]
keywords = '\n\t'.join(textwrap.wrap(
", ".join(feature_names[topkw])
))
print "{}: {}".format(category, keywords)
print
print "classification report:"
print metrics.classification_report(test_target, predicted,
target_names=categories)
print "confusion matrix:"
print metrics.confusion_matrix(test_target, predicted)
print
开发者ID:rolando-archive，项目名称:yatiri，代码行数:27，代码来源:run_classifier.py
示例5: benchmark_features_selection
def benchmark_features_selection(clf,name):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(y_train, 2),
scoring='accuracy')
rfecv.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
print(name+"Optimal number of features : %d" % rfecv.n_features_)
# Plot number of features VS. cross-validation scores
plt.figure()
plt.xlabel("Number of features selected")
plt.ylabel("Cross validation score (nb of correct classifications)")
plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
plt.show()
t0 = time()
pred = rfecv.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
print("Saving data to database:")
save_results_data(cursor, name, testing_identifiant_produit_list, pred)
print()
clf_descr = str(clf).split('(')[0]
return clf_descr,train_time,test_time
开发者ID:sduprey，项目名称:PYTHON_WEB，代码行数:35，代码来源:launching_recursive_feature_selection_production_sparse_on_uniformly_restrained_data.py
示例6: benchmark
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.f1_score(y_test, pred)
print("f1-score: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=categories))
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:afshinrahimi，项目名称:textylon，代码行数:35，代码来源:Copy+of+dsm.py
示例7: benchmark
def benchmark(clf):
print 80 * '_'
print "Training: "
print clf
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print "train time: %0.3fs" % train_time
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print "test time: %0.3fs" % test_time
score = metric(y_test, pred)
print "MAE: %0.3f" % score
if hasattr(clf, 'alpha_'):
print "Alpha", clf.alpha_
try:
if hasattr(clf, 'coef_'):
print "density: %f" % density(clf.coef_)
print "dimensionality: %d" % clf.coef_.shape[0]
print
except Exception as ex:
print ex
print
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:ageek，项目名称:kaggle-job-salary，代码行数:33，代码来源:test_notext.py
示例8: benchmark
def benchmark(clf, clf_name):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(x_train_std, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(x_test_std)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=["not helpful", "helpful"]))
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
save_confusion_matrix(confusion_matrix(y_test, pred), pred, clf_name)
return clf_descr, score, train_time, test_time
开发者ID:jacobmbr，项目名称:psam-5600-machine-learning，代码行数:31，代码来源:helpfulness.py
示例9: benchmark
def benchmark(self, clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time.time()
clf.fit(self.X_train, self.y_train)
train_time = time.time() - t0
print("train time: %0.3fs" % train_time)
t0 = time.time()
pred = clf.predict(self.X_test)
test_time = time.time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(self.y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
print("top 10 keywords per class:")
for i, label in enumerate(self.target_names):
top10 = np.argsort(clf.coef_[i])[-10:]
print(self.trim("%s: %s" % (label, " ".join(self.feature_names[top10]))))
print()
print("confusion matrix:")
print(metrics.confusion_matrix(self.y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:vinidixit，项目名称:codes，代码行数:34，代码来源:ClassifierBenchmarker.py
示例10: benchmark
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.f1_score(y_test, pred)
print("f1-score: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if feature_names is not None:
print("top 10 keywords per class:")
print()
if True:
print("confusion matrix:")
cm = metrics.confusion_matrix(y_test, pred)
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:samijaber，项目名称:scenic-routing，代码行数:33，代码来源:classifier.py
示例11: benchmark
def benchmark(clf,X_train,y_train,X_test,y_test):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
print()
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:LopezGG，项目名称:SwitchBoardDialogClassifcation，代码行数:28，代码来源:NgramsBasedClassification.py
示例12: benchmark
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.f1_score(y_test, pred)
accscore = metrics.accuracy_score(y_test, pred)
print ("pred count is %d" %len(pred))
print ('accuracy score: %0.3f' % accscore)
print("f1-score: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=categories))
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print("confidence for unlabeled data:")
#compute absolute confidence for each unlabeled sample in each class
confidences = np.abs(clf.decision_function(X_unlabeled))
#average abs(confidence) over all classes for each unlabeled sample (if there is more than 2 classes)
if(len(categories) > 2):
confidences = np.average(confidences, axix=1)
print confidences
sorted_confidences = np.argsort(confidences)
question_samples = []
#select top k low confidence unlabeled samples
low_confidence_samples = sorted_confidences[0:NUM_QUESTIONS]
#select top k high confidence unlabeled samples
high_confidence_samples = sorted_confidences[-NUM_QUESTIONS:]
question_samples.extend(low_confidence_samples.tolist())
question_samples.extend(high_confidence_samples.tolist())
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time, question_samples
开发者ID:afshinrahimi，项目名称:activelearning，代码行数:57，代码来源:activelearner.py
示例13: benchmark
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
predictions = clf.predict_proba(X_test)
fin_predict = []
for i in xrange(0,len(predictions)):
x = np.argpartition(predictions[i],-5)[-5:]
x = clf.classes_[x]
fin_predict.append([bunch.target_names[e] for e in x])
our_accuracies.append(final_accuracy(fin_predict))
print(our_accuracies[-1])
# print("------------predictions------------")
# print(pred)
# print("-------------------------")
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("top 10 keywords per class:")
for i, category in enumerate(categories):
top10 = np.argsort(clf.coef_[i])[-10:]
print(trim("%s: %s"
% (category, " ".join(feature_names[top10]))))
print()
if opts.print_report:
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=categories))
if opts.print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:rohitsakala，项目名称:SMAI_Project，代码行数:53，代码来源:TED-Supervised.py
示例14: benchmark
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
# FIXME: use X_train.toarray() instead. if it didn't work use y_train.toarray() too :D
#y_train.toarray()
#X_train.toarray()
#clf.fit(X_train.toarray(), y_train)
#clf.fit(X_train, y_train.toarray())
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
score = metrics.precision_score(y_test, pred, average='weighted', pos_label=None)
print("precision: %0.3f" % score)
score = metrics.recall_score(y_test, pred, average='weighted', pos_label=None)
print("recall: %0.3f" % score)
score = metrics.f1_score(y_test, pred, average='weighted', pos_label=None)
print("f1: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("top 10 keywords per class:")
#for i, category in enumerate(categories):
# top10 = np.argsort(clf.coef_[i])[-10:]
# print(trim("%s: %s"
# % (category, " ".join(feature_names[top10]))))
print()
if opts.print_report:
print("classification report:")
#print(metrics.classification_report(y_test, pred,
# target_names=categories))
if opts.print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:Mitra00，项目名称:scikit_learn_BinaryClass，代码行数:52，代码来源:document_classification_20newsgroups.py
示例15: benchmark
def benchmark(clf):
print 80 * '_'
print "Training: "
print clf
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print "train time: %0.3fs" % train_time
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print "test time: %0.3fs" % test_time
score = metrics.f1_score(y_test, pred)
print "f1-score: %0.3f" % score
if hasattr(clf, 'coef_'):
print "dimensionality: %d" % max(clf.coef_.shape)
print "density: %f" % density(clf.coef_)
if opts.print_top10:
print "top 10 keywords per class:"
for i, category in enumerate(categories):
import pdb;pdb.set_trace()
if len(clf.coef_.shape) == 1:
top10 = np.argsort(clf.coef_[i])[-10:]
else:
top10 = np.argsort(clf.coef_[0][i])[-10:]
print trim("%s: %s" % (
category, " ".join(np.array(feature_names)[top10])))
print
pos_hits = []
for i in range(len(pred)):
if pred[i] == 1:
pos_hits.append(y_test[i])
#print float(sum(pos_hits))/len(pos_hits)
#print len(pos_hits)
if opts.print_report:
print "classification report:"
print metrics.classification_report(y_test, pred,
target_names=map(str,categories))
if opts.print_cm:
print "confusion matrix:"
print metrics.confusion_matrix(y_test, pred)
print
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:CTCL，项目名称:candidate_classifier，代码行数:50，代码来源:classify_ex_fb.py
示例16: benchmark
def benchmark(self, clf):
print_topX = self.print_topX
print_report = self.print_report
print_cm = self.print_cm
X_train = self.X_train
y_train = self.y_train
X_test = self.X_test
y_test = self.y_test
feature_names = self.feature_names
categories = ["1"]
print("_" * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.f1_score(y_test, pred)
print("f1-score: %0.3f" % score)
if hasattr(clf, "coef_"):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if print_topX:
print("top 10 keywords per class:")
for i, category in enumerate(categories):
topX = np.argsort(clf.coef_[i])[-print_topX:]
print(trim("%s: %s" % (category, " ".join(feature_names[topX]))))
print()
if print_report:
print("classification report:")
print(classification_report(y_test, pred))
# target_names=categories))
if print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split("(")[0]
return clf_descr, score, train_time, test_time, clf, pred
开发者ID:jacobboy，项目名称:spring14，代码行数:50，代码来源:classes.py
示例17: benchmark
def benchmark(clf):
needsDense=[RandomForestClassifier, AdaBoostClassifier, Pipeline]
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
if type(clf) in needsDense:
clf.fit(X_train.todense(), y_train)
else:
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
if type(clf) in needsDense:
pred = clf.predict(X_test.todense())
else:
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.f1_score(y_test, pred)
print("f1-score: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if print_topX:
print("top feature per class:")
for i, category in enumerate(categories):
# topX = np.min(clf.coef_.shape[1], print_topX)
topX = np.argsort(clf.coef_[i])[-print_topX:][::-1]
print(trim("%s: %s"
% (category, " | ".join(feature_names[topX]))))
print()
if print_report:
print("classification report:")
print(metrics.classification_report(y_test, pred))
# target_names=categories))
if print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time, clf, pred
开发者ID:jacobboy，项目名称:spring14，代码行数:49，代码来源:feat_select.py
示例18: benchmark
def benchmark(clf):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
try:
clf.fit(X_train, y_train)
except:
clf.fit(X_train.toarray(), y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
try:
pred = clf.predict(X_test)
except:
pred = clf.predict(X_test.toarray())
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.f1_score(y_test, pred)
print("f1-score: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("top 10 keywords per class:")
for i, category in enumerate(categories):
top10 = np.argsort(clf.coef_[i])[-10:]
print(trim("%s: %s"
% (category, " ".join(feature_names[top10]))))
print()
if opts.print_report:
print("classification report:")
print(metrics.classification_report(y_test, pred,
target_names=categories))
if opts.print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:osh，项目名称:newsgroup-classifier，代码行数:47，代码来源:document_classification_20newsgroups.py
示例19: benchmark
def benchmark(clf, clf_descr, X_train, X_test, y_train, y_test, feature_names, categories, silent, print_top10):
"""
Benchmark a classifier.
"""
if not silent:
print('_' * 80)
print("Training: ")
if (not silent) or print_top10:
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
if not silent:
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
if not silent:
print("test time: %0.3fs" % test_time)
#score = metrics.f1_score(y_test, pred)
score = np.mean(pred == y_test)
if not silent:
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
if not silent:
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if print_top10 and feature_names is not None:
print("top 10 keywords per class:")
#print(categories)
if len(categories) > 2: # multi-class
for i, category in enumerate(categories):
top10 = np.argsort(clf.coef_[i])[-10:]
print("%s: %s" % (category, " ".join(feature_names[top10])))
else: # binary
top10 = np.argsort(clf.coef_[0])[-10:]
print("%s" % (" ".join(feature_names[top10])))
print()
#clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time, pred
开发者ID:tiefling-cat，项目名称:guessing-aspect，代码行数:45，代码来源:aspect_tools.py
示例20: benchmark
def benchmark(clf,name):
print('_' * 80)
print("Training: ")
print(clf)
t0 = time()
clf.fit(X_train, y_train)
train_time = time() - t0
print("train time: %0.3fs" % train_time)
t0 = time()
pred = clf.predict(X_test)
test_time = time() - t0
print("test time: %0.3fs" % test_time)
score = metrics.accuracy_score(y_test, pred)
print("accuracy: %0.3f" % score)
if hasattr(clf, 'coef_'):
print("dimensionality: %d" % clf.coef_.shape[1])
print("density: %f" % density(clf.coef_))
if opts.print_top10 and feature_names is not None:
print("top 10 keywords per class:")
# for i, category in enumerate(categories):
# top10 = np.argsort(clf.coef_[i])[-10:]
# print(trim("%s: %s"
# % (category, " ".join(feature_names[top10]))))
print()
if opts.print_report:
print("classification report:")
print(metrics.classification_report(y_test, pred))
# print(metrics.classification_report(y_test, pred,
# target_names=categories))
if opts.print_cm:
print("confusion matrix:")
print(metrics.confusion_matrix(y_test, pred))
print("Saving data to database:")
save_my_data(cursor, name, testing_identifiant_produit_list, y_test, pred)
print()
clf_descr = str(clf).split('(')[0]
return clf_descr, score, train_time, test_time
开发者ID:sduprey，项目名称:PYTHON_WEB，代码行数:44，代码来源:prototyping_hashing_classification.py
注：本文中的sklearn.utils.extmath.density函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
客服电话
APP下载
官方微信
请发表评论