我试图从简单的 LSTM 模型中绘制摘要。我ValueError: shape mismatch: objects cannot be broadcast to a single shape打电话时得到shap.summary_plot。重现问题的 Colab
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, LSTM
import shap
# Create random training values.
#
# train_x is [
# [
# [0.3, 0.54 ... 0.8],
# [0.4, 0.6 ... 0.55],
# ...
# ],
# [
# [0.3, 0.54 ... 0.8],
# [0.4, 0.6 ... 0.55],
# ...
# ],
# ...
# ]
#
# train_y is corresponding classification of train_x sequences, always 0 or 1
# [0, 1, 0, 1, 0, ... 0]
SAMPLES_CNT = 1000
train_x = np.random.rand(SAMPLES_CNT,5,4)
train_y = np.vectorize(lambda x: int(round(x)))(np.random.rand(SAMPLES_CNT))
val_x = np.random.rand(int(SAMPLES_CNT * 0.1),5,4)
val_y = np.vectorize(lambda x: int(round(x)))(np.random.rand(int(SAMPLES_CNT * 0.1)))
# Train model
model = Sequential()
model.add(LSTM(32,input_shape=train_x.shape[1:], return_sequences=False, stateful=False))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001, decay=1e-6),
loss='binary_crossentropy',metrics=['accuracy'])
fit = model.fit(train_x, train_y, batch_size=64, epochs=2,
validation_data=(val_x, val_y), shuffle=False)
explainer = shap.DeepExplainer(model, train_x[:10])
shap_vals = explainer.shap_values(val_x[:10])
shap.summary_plot(shap_vals, val_x[:10], plot_type="bar")
崩溃
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-78-906a7898852e> in <module>
----> 1 shap.summary_plot(shap_vals, val_x[:10], feature_names=feature_names, plot_type="bar")
2
/usr/local/lib/python3.7/site-packages/shap/plots/summary.py in summary_plot(shap_values, features, feature_names, max_display, plot_type, color, axis_color, title, alpha, show, sort, color_bar, plot_size, layered_violin_max_num_bins, class_names, class_inds, color_bar_label, auto_size_plot)
442 pl.barh(
443 y_pos, global_shap_values[feature_inds], 0.7, left=left_pos, align='center',
--> 444 color=color(i), label=class_names[ind]
445 )
446 left_pos += global_shap_values[feature_inds]
/usr/local/lib/python3.7/site-packages/matplotlib/pyplot.py in barh(y, width, height, left, align, **kwargs)
2421 def barh(y, width, height=0.8, left=None, *, align='center', **kwargs):
2422 return gca().barh(
-> 2423 y, width, height=height, left=left, align=align, **kwargs)
2424
2425
/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py in barh(self, y, width, height, left, align, **kwargs)
2544 kwargs.setdefault('orientation', 'horizontal')
2545 patches = self.bar(x=left, height=height, width=width, bottom=y,
-> 2546 align=align, **kwargs)
2547 return patches
2548
/usr/local/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, data, *args, **kwargs)
1563 def inner(ax, *args, data=None, **kwargs):
1564 if data is None:
-> 1565 return func(ax, *map(sanitize_sequence, args), **kwargs)
1566
1567 bound = new_sig.bind(ax, *args, **kwargs)
/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
2339 x, height, width, y, linewidth = np.broadcast_arrays(
2340 # Make args iterable too.
-> 2341 np.atleast_1d(x), height, width, y, linewidth)
2342
2343 # Now that units have been converted, set the tick locations.
<__array_function__ internals> in broadcast_arrays(*args, **kwargs)
/usr/local/lib/python3.7/site-packages/numpy/lib/stride_tricks.py in broadcast_arrays(*args, **kwargs)
262 args = [np.array(_m, copy=False, subok=subok) for _m in args]
263
--> 264 shape = _broadcast_shape(*args)
265
266 if all(array.shape == shape for array in args):
/usr/local/lib/python3.7/site-packages/numpy/lib/stride_tricks.py in _broadcast_shape(*args)
189 # use the old-iterator because np.nditer does not handle size 0 arrays
190 # consistently
--> 191 b = np.broadcast(*args[:32])
192 # unfortunately, it cannot handle 32 or more arguments directly
193 for pos in range(32, len(args), 31):
ValueError: shape mismatch: objects cannot be broadcast to a single shape
我做错了什么还是这是一个错误？这一天我一直在敲我的头。提前谢谢你的帮助。
