def tweets(entered):
name = PullIdGenTweetText.getTweeterInfo(entered)
makewordcloud.makecloud(entered)
stacked = StackedClassifier(entered,name)
gender,age = stacked.get_labels()
wordcloud = 'static/' + entered + '_cloud.png'
return age,gender,wordcloud,name
def tweets(entered):
name = PullIdGenTweetText.getTweeterInfo(entered)
makewordcloud.makecloud(entered)
stacked = StackedClassifier(entered,name)
print("Getting age and gender")
gender,age = stacked.get_labels()
wordcloud = 'static/' + entered + '_cloud.png'
print age
print gender
print wordcloud
# return age, gender, wordcloud
return age,gender,wordcloud,name
predictor.fit(X_train, y_train)
y_pred = predictor.predict(X_test)
method = getattr(predictor, "accuracy_score", None)
has_print_accuracy = callable(method)
print("-" * 60)
print(F"'{predictor.__class__.__name__}' REPORT:")
print("-" * 60)
if has_print_accuracy:
method(X_test, y_test)
else:
print("Accuracy score: ", accuracy_score(y_test, y_pred))
print("Confusion matrix:\n", confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
print("")
dt = DecisionTree(min_samples_split, min_samples_leaf)
test_predictor(dt, X_train, X_test, y_train, y_test)
from random_forest import RandomForest
rf = RandomForest(min_samples_split, min_samples_leaf, 100)
test_predictor(rf, X_train, X_test, y_train, y_test)
from stacked_classifier import StackedClassifier
sc = StackedClassifier(train_size=0.7)
test_predictor(sc, X_train, X_test, y_train, y_test)