Mglearnのエラー

Question

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons

X, y = make_moons(n_samples=100, noise=0.25, random_state=3)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)

forest = RandomForestClassifier(n_estimators=5, random_state=2)
forest.fit(X_train, y_train)

fig, axes = plt.subplots(2, 3, figsize=(20, 10))
for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):
    ax.set_title("Tree {}".format(i))
    mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)
    
mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)
    
axes[-1, -1].set_title("Random Forest")
mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)```

Pythonではじめる機械学習で勉強しています。
ランダムフォレストのところでエラーがでて先に進みません。どうすれば良いのでしょうか？Runtimeエラーでoutput shape not correctと出ています。教えていただければ幸いです。

環境は
Mac
Anaconda
Jupyter notebook 6.0.1

mglearnはインポート済みです。



---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-108-a394de2652b3> in <module>
     12 for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):
     13     ax.set_title("Tree {}".format(i))
---> 14     mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)
     15 
     16 mgleran.plots.plot_2d_separator(forest, X_train_, fill=True, ax=axes[-1, -1],

~/opt/anaconda3/lib/python3.7/site-packages/mglearn/plot_interactive_tree.py in plot_tree_partition(X, y, tree, ax)
     77     faces = tree.apply(X_grid)
     78     faces = faces.reshape(X1.shape)
---> 79     border = ndimage.laplace(faces) != 0
     80     ax.contourf(X1, X2, Z, alpha=.4, cmap=cm2, levels=[0, .5, 1])
     81     ax.scatter(X1[border], X2[border], marker='.', s=1)

~/opt/anaconda3/lib/python3.7/site-packages/scipy/ndimage/filters.py in laplace(input, output, mode, cval)
    447     def derivative2(input, axis, output, mode, cval):
    448         return correlate1d(input, [1, -2, 1], axis, output, mode, cval, 0)
--> 449     return generic_laplace(input, derivative2, output, mode, cval)
    450 
    451 

~/opt/anaconda3/lib/python3.7/site-packages/scipy/ndimage/filters.py in generic_laplace(input, derivative2, output, mode, cval, extra_arguments, extra_keywords)
    413         for ii in range(1, len(axes)):
    414             tmp = derivative2(input, axes[ii], output.dtype, modes[ii], cval,
--> 415                               *extra_arguments, **extra_keywords)
    416             output += tmp
    417     else:

~/opt/anaconda3/lib/python3.7/site-packages/scipy/ndimage/filters.py in derivative2(input, axis, output, mode, cval)
    446     """
    447     def derivative2(input, axis, output, mode, cval):
--> 448         return correlate1d(input, [1, -2, 1], axis, output, mode, cval, 0)
    449     return generic_laplace(input, derivative2, output, mode, cval)
    450 

~/opt/anaconda3/lib/python3.7/site-packages/scipy/ndimage/filters.py in correlate1d(input, weights, axis, output, mode, cval, origin)
     80     if numpy.iscomplexobj(input):
     81         raise TypeError('Complex type not supported')
---> 82     output = _ni_support._get_output(output, input)
     83     weights = numpy.asarray(weights, dtype=numpy.float64)
     84     if weights.ndim != 1 or weights.shape[0] < 1:

~/opt/anaconda3/lib/python3.7/site-packages/scipy/ndimage/_ni_support.py in _get_output(output, input, shape)
     80         output = numpy.zeros(shape, dtype=output)
     81     elif output.shape != shape:
---> 82         raise RuntimeError("output shape not correct")
     83     return output
     84 

RuntimeError: output shape not correct

Comments

[K_3578]

エラーが出て先に進みません。
エラーメッセージはどこで不具合が発生しているかを明確に教えてくれている。
意味不明！と言って逃げても先には進まない。
英文ならGoogle翻訳でもなんでも使って1行ずつ読んでいけば良い。

Answer 1

mgleran.plots.plot_2d_separator
X_train_,

間違いだらけです。

Comments

[退会済みユーザー]

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons

X, y = make_moons(n_samples=100, noise=0.25, random_state=3)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)

forest = RandomForestClassifier(n_estimators=5, random_state=2)
forest.fit(X_train, y_train)

fig, axes = plt.subplots(2, 3, figsize=(20, 10))
for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):
ax.set_title("Tree {}".format(i))
mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)

mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)

axes[-1, -1].set_title("Random Forest")
mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)

[退会済みユーザー]

RuntimeError: output shape not correct

[technocore]

他にも誤字脱字がないか探してください。