print("start training")
clasificador.fit(X_train, y_train, classes=None, sample_weight=None)
print("end training")
# In[5]:
print("start predict")
predict = clasificador.predict(X_test)
print("end predict")
# In[6]:
print("shape_predict")
print(predict.shape)
print("score")
print(clasificador.score(X_test, y_test))
import matplotlib.pyplot as plt
#%matplotlib inline
plt.rcParams['figure.figsize'] = (16, 9)
plt.style.use('ggplot')
plt.hist([predict, y_test])
plt.show()
# # Metodos de evaluacion del modelo
# In[7]:
y_pred = predict
# In[8]:
from sklearn.model_selection import train_test_split
from skmultiflow.trees import HoeffdingTree
import matplotlib.pyplot as plt
res = []
# Create a dataset.
X, y = make_classification(10000, random_state=123)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# Define a tree for fitting the complete dataset and one for streaming.
ht_complete = HoeffdingTree()
ht_partial = HoeffdingTree()
# Fit the complete dataset.
ht_complete.fit(X_train, y_train)
ht_complete_score = ht_complete.score(X_test, y_test)
print(f'Score when fitting at once: {ht_complete_score}')
# Streaming samples one after another.
timer = False
j = 0
for i in range(len(X_train)):
ht_partial.partial_fit(X_train[i].reshape(1, -1), np.array([y_train[i]]))
res.append(ht_partial.score(X_test, y_test))
print(f'Score when streaming after {i} samples: {res[-1]}')
if res[-1] >= ht_complete_score - 0.01:
print(f'(Almost) full score reached! Continue for another {20 - j} samples.')
timer = True
if timer:
j += 1
if j == 20: