class FeatureVec(object):
"Feature-vector class."
def __init__(
self,
mode,
max_depth=3,
feature_names=None,
max_sentences=20000,
exp_rand_tree_size=True,
tree_generator=None,
):
'''
mode: 'classify' or 'regress'
max_depth: maximum depth of trained trees
feature_names: names of features
max_sentences: maximum number of extracted sentences
exp_rand_tree_size: Having trees with different sizes
tree_generator: Tree generator model (overwrites above features)
'''
self.feature_names = feature_names
self.mode = mode
max_leafs = 2**max_depth
num_trees = max_sentences // max_leafs
if tree_generator is None:
tree_generator = RandomForestClassifier(num_trees,
max_depth=max_depth)
self.exp_rand_tree_size = exp_rand_tree_size
self.rf = RuleFit(rfmode=mode,
tree_size=max_leafs,
max_rules=max_sentences,
tree_generator=tree_generator,
exp_rand_tree_size=True,
fit_lasso=False,
Cs=10.**np.arange(-4, 1),
cv=3)
def fit(self, X, y, restart=True, bagging=0):
'''Fit the tree model.
X: inputs
y: outputs (integer class label or real value)
restart: To train from scratch tree generator model
bagging: If >0 applies bagging on trees to compute confidence intervals
'''
if not bagging:
bagging = 0
dimred = TruncatedSVD(2)
self.rf.fit(X, y, restart=restart)
rules = self.rf.get_rules()['rule'].values
cm = cooccurance_matrix(rules, X.shape[-1])
vectors = dimred.fit_transform(cm)
vectors = normalize_angles(vectors)
self.norms = np.clip(np.linalg.norm(vectors, axis=-1, keepdims=True),
1e-12, None)
vectors /= np.max(self.norms)
self.vectors = vectors
self.importance = np.linalg.norm(self.vectors, axis=-1)
self.angles = np.arctan2(self.vectors[:, 1], self.vectors[:, 0])
self.stds = np.zeros(vectors.shape)
self.predictor = self.rf.tree_generator
if bagging:
all_vectors = []
for _ in range(bagging):
self.rf.bag_trees(X, y)
rules_bag = self.rf.get_rules()['rule'].values
cm_bag = cooccurance_matrix(rules_bag, X.shape[-1])
vectors_bag = dimred.fit_transform(cm_bag)
vectors_bag = normalize_angles(vectors_bag)
norms_bag = np.clip(
np.linalg.norm(vectors_bag, axis=-1, keepdims=True), 1e-12,
None)
all_vectors.append(vectors_bag / norms_bag)
self.stds = np.std(all_vectors, 0)
def plot(self, dynamic=True, confidence=True, path=None):
'''Plot the feature-vectors.
dynamic: If True the output is a dynamic html plot. Otherwise, it will be an image.
confidence: To show confidence intervals or not
path: Path to save the image. If dy
'''
mx = 1.1
angles = np.arctan2(self.vectors[:, 1], self.vectors[:, 0])
max_angle = np.max(np.abs(angles))
feature_names = self.feature_names + ['origin', '']
plot_vectors = np.concatenate([self.vectors, [[0, 0], [0, 0]]])
vectors_sizes = np.linalg.norm(plot_vectors, axis=-1)
plot_angles = np.concatenate([angles, [-max_angle, max_angle]])
plot_data = np.stack([
plot_vectors[:, 1], plot_vectors[:, 0], plot_angles, feature_names
],
axis=-1)
plot_df = pd.DataFrame(data=plot_data,
columns=['x', 'y', 'angles', 'names'])
plot_df[["x", "y",
"angles"]] = plot_df[["x", "y",
"angles"]].apply(pd.to_numeric)
if dynamic:
fig = px.scatter(
plot_df,
x='x',
y='y',
color='angles',
width=1000,
height=500,
hover_name=feature_names,
hover_data={
'x': False,
'y': False,
'angles': False,
'names': False
},
color_continuous_scale=px.colors.sequential.Rainbow)
fig.update_yaxes(visible=False,
showticklabels=False,
range=[0, mx])
fig.update_xaxes(visible=False,
showticklabels=False,
range=[-mx, mx])
else:
fig = px.scatter(
plot_df,
x='x',
y='y',
color='angles',
width=1000,
height=500,
hover_name='names',
hover_data={
'x': False,
'y': False,
'angles': False,
'names': False
},
color_continuous_scale=px.colors.sequential.Rainbow)
max_name_len = max([len(i) for i in feature_names])
for i in range(len(plot_vectors) - 2):
if plot_vectors[:, 1][i] > 0:
name = feature_names[i] + ''.join(
[' '] * (max_name_len - len(feature_names[i])))
ax = plot_vectors[:, 1][i] + 0.2
else:
name = ''.join([' '] *
(max_name_len -
len(feature_names[i]))) + feature_names[i]
ax = plot_vectors[:, 1][i] - 0.2
if vectors_sizes[i] < 0.2:
continue
fig.add_annotation(
x=plot_vectors[:, 1][i],
y=plot_vectors[:, 0][i],
text=feature_names[i] +
''.join([' '] * (max_name_len - len(feature_names[i]))),
font=dict(size=15),
axref="x",
ayref="y",
ax=ax,
ay=plot_vectors[:, 0][i],
arrowhead=2,
)
fig.update_yaxes(visible=False,
showticklabels=False,
range=[0, mx])
fig.update_xaxes(visible=False,
showticklabels=False,
range=[-mx, mx])
fig.update_traces(marker=dict(size=10), textfont_size=15)
fig.update(layout_coloraxis_showscale=False)
fig.update_layout(showlegend=False)
for i in range(10):
fig.add_shape(type='circle',
x0=(i + 1) / 10 * mx,
y0=(i + 1) / 10 * mx,
x1=-(i + 1) / 10 * mx,
y1=-(i + 1) / 10 * mx,
line_color="red",
opacity=0.5,
line=dict(dash='dot', width=3))
if confidence:
for vector, std, angle in zip(self.vectors, self.stds, angles):
fig.add_shape(type='circle',
x0=vector[1] + 3 * std[1],
y0=vector[0] + 3 * std[0],
x1=vector[1] - 3 * std[1],
y1=vector[0] - 3 * std[0],
line_color='gray',
opacity=0.5,
line=dict(dash='solid', width=1))
fig.show()
if path:
if len(path.split('/')) > 1 and not os.path.exists('/'.join(
path.split('/')[:-1])):
os.makedirs('/'.join(path.split('/')[:-1]))
if dynamic:
assert path.split(
'.'
)[-1] == 'html', 'For a dynamic figure, path should be an html file!'
fig.write_html(path)
else:
fig.write_image(path)
