def classify(self):
y_data = self.get_result(self.task.label)
X_data = self.get_result(self.task.features)
y = np.array(y_data.data).ravel()
X = np.array(pd.get_dummies(X_data.data))
#X = MinMaxScaler().fit_transform(X)
X_train = X[:-TILE_SIZE]
y_train = y[:-TILE_SIZE]
X_test = X[-TILE_SIZE:]
y_test = y[-TILE_SIZE:]
cw = compute_class_weight('auto', np.array([0, 1]), y)
cw = {0: cw[0], 1: cw[1]}
b = get_classifier(self.task.classifier, cw)
b.partial_fit(X_train, y_train, classes=np.array([0, 1]))
y_prob = None
y_pred = None
if self.task.classifier in ['perceptron', 'svm']:
y_pred = b.predict(X_test)
y_prob = np.array([[0, y] for y in y_pred])
else:
y_prob = b.predict_proba(X_test)
y_pred = [1 if t[0] >= 0.5 else 0 for t in y_prob]
cm = confusion_matrix(y_test, y_pred)
stats = classify_stats(cm, y_test, y_prob, TILE_SIZE)
result = ClassifyResult(self.task, 1.0, b, stats)
self.results[self.task.uuid] = result
def classify(self):
y_data = self.get_result(self.task.label)
X_data = self.get_result(self.task.features)
y = np.array(y_data.data).ravel()
X = np.array(pd.get_dummies(X_data.data))
#X = MinMaxScaler().fit_transform(X)
X_train = X[:-TILE_SIZE]
y_train = y[:-TILE_SIZE]
X_test = X[-TILE_SIZE:]
y_test = y[-TILE_SIZE:]
cw = compute_class_weight('auto', np.array([0,1]), y)
cw = {0:cw[0],1:cw[1]}
b = get_classifier(self.task.classifier, cw)
b.partial_fit(X_train, y_train, classes=np.array([0,1]))
y_prob = None
y_pred = None
if self.task.classifier in ['perceptron','svm']:
y_pred = b.predict(X_test)
y_prob = np.array([[0,y] for y in y_pred])
else:
y_prob = b.predict_proba(X_test)
y_pred = [1 if t[0] >= 0.5 else 0 for t in y_prob]
cm = confusion_matrix(y_test, y_pred)
stats = classify_stats(cm, y_test, y_prob, TILE_SIZE)
result = ClassifyResult(self.task, 1.0, b, stats)
self.results[self.task.uuid] = result
def classify(self):
y_data = self.results[self.task.label].data
X_data = self.results[self.task.features].data
y = np.array(y_data)
X = np.array(X_data)
cw = compute_class_weight('auto', np.array([0, 1]), y)
cw = {0: cw[0], 1: cw[1]}
print cw
b = get_classifier(self.task.classifier, cw)
tile_size = 1000
num_tiles = y.size / tile_size
for i in range(num_tiles):
pos = i * tile_size
X_sub = X[pos:pos + tile_size]
y_sub = y[pos:pos + tile_size]
y_prob = None
y_pred = None
if self.task.classifier == 'svm':
y_pred = b.predict(X_sub)
y_prob = np.array([[0, y] for y in y_pred])
else:
y_prob = b.predict_proba(X_sub)
y_pred = [1 if y[1] >= 0.5 else 0 for y in y_prob]
cm = confusion_matrix(y_sub, y_pred)
stats = classify_stats(cm, y_test, y_prob)
y_pred = pd.DataFrame(y_pred, columns=y_data.columns)
result = ClassifyResult(self.task, 1.0, b, stats)
self.results[self.task.uuid] = result
b.partial_fit(X_sub, y_sub)
def classify(self):
y_data = self.results[self.task.label].data
X_data = self.results[self.task.features].data
y = np.array(y_data)
X = np.array(X_data)
cw = compute_class_weight('auto', np.array([0,1]), y)
cw = {0:cw[0],1:cw[1]}
print cw
b = get_classifier(self.task.classifier, cw)
tile_size = 1000
num_tiles = y.size / tile_size
for i in range(num_tiles):
pos = i * tile_size
X_sub = X[pos : pos + tile_size]
y_sub = y[pos : pos + tile_size]
y_prob = None
y_pred = None
if self.task.classifier == 'svm':
y_pred = b.predict(X_sub)
y_prob = np.array([[0,y] for y in y_pred])
else:
y_prob = b.predict_proba(X_sub)
y_pred = [1 if y[1] >= 0.5 else 0 for y in y_prob]
cm = confusion_matrix(y_sub, y_pred)
stats = classify_stats(cm, y_test, y_prob)
y_pred = pd.DataFrame(y_pred, columns=y_data.columns)
result = ClassifyResult(self.task, 1.0, b, stats)
self.results[self.task.uuid] = result
b.partial_fit(X_sub, y_sub)
