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Ho un problema con la funzione "plot_bounds" di viz. Sebbene abbia installato il modulo e riesca a importarlo, quanto cerco di importare la funzione mi dà errore (ImportError: cannot import name 'plot_bounds')

Il dendogramma clusterizza erroneamente p5 e p6 invece di p4 e p5 alla prima iterazione

ciao,
seguendo il corso, una volta attivato a questo punto,
from sklearn.preprocessing import PolynomialFeatures polyfeats = PolynomialFeatures(degree = 2) X_train_poly = polyfeats.fit_transform(X_train) X_test_poly = polyfeats.transform(X_test)

mi dà tale errore
`ValueError Traceback (most recent call last)
in
1 from sklearn.preprocessing import PolynomialFeatures
2 polyfeats = PolynomialFeatures(degree = 2)
----> 3 X_train_poly = polyfeats.fit_transform(X_train)
4 X_test_poly = polyfeats.transform(X_test)

~\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
551 if y is None:
552 # fit method of arity 1 (unsupervised transformation)
--> 553 return self.fit(X, **fit_params).transform(X)
554 else:
555 # fit method of arity 2 (supervised transformation)

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self, X, y)
1463 self : instance
1464 """
-> 1465 n_samples, n_features = check_array(X, accept_sparse=True).shape
1466 combinations = self._combinations(n_features, self.degree,
1467 self.interaction_only,

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
519 "Reshape your data either using array.reshape(-1, 1) if "
520 "your data has a single feature or array.reshape(1, -1) "
--> 521 "if it contains a single sample.".format(array))
522
523 # in the future np.flexible dtypes will be handled like object dtypes

ValueError: Expected 2D array, got 1D array instead:
array=[34.41 7.73 16.96 4.97 17.93 18.72 13.09 21.02 26.45 10.26 4.59 5.25
8.05 12.79 7.7 7.22 7.79 16.35 4.38 24.91 14.65 5.5 13.34 21.78
15.1 21.14 11.66 9.43 16.23 14.52 9.8 11.64 18.66 5.08 9.5 5.99
4.45 16.22 23.98 11.25 5.7 11.5 3.16 6.21 9.5 14.13 5.98 3.01
8.16 11.69 7.26 6.62 27.8 6.43 14.1 5.9 10.58 12.14 6.53 9.54
18.05 10.24 11.72 24.08 24.16 7.67 15.17 4.03 20.08 21.46 14.67 16.65
9.25 13.27 22.6 10.45 6.36 13.44 19.01 7.9 10.11 3.53 12.04 11.1
19.52 14.37 8.1 16.21 5.29 6.36 10.29 16.9 5.1 5.49 9.45 27.26
7.85 20.34 34.37 21.24 17.16 2.47 15.03 18.35 7.01 9.55 14.44 4.56
6.59 9.51 17.92 7.54 9.68 23.79 11.98 7.12 10.53 16.94 9.69 17.28
21.32 6.27 16.14 9.74 23.6 21.32 16.03 12.33 8.05 5.98 5.57 9.47
22.88 5.39 29.55 2.88 8.05 6.9 8.1 16.3 13.51 7.6 18.34 10.16
3.7 14.1 29.97 1.98 3.53 14.19 9.1 18.33 10.36 8.26 7.14 36.98
14.33 3.92 1.73 7.51 5.64 13.11 13. 21.45 12.12 6.58 7.18 15.55
23.34 18.46 4.73 9.59 10.19 15.94 9.67 22.98 9.52 7.83 17.11 11.28
9.97 7.39 13.65 3.13 15.17 2.94 4.5 14.81 3.76 12.93 10.27 13.98
17.21 10.42 2.98 10.4 16.59 4.82 16.74 5.29 7.53 7.79 13.27 13.44
12.86 14.79 11.41 14.98 6.86 4.84 13. 13.45 23.09 20.31 20.32 15.7
25.41 9.93 6.73 21.08 12.6 6.68 19.88 7.44 16.44 4.98 7.43 3.26
12.03 3.57 5.89 6.93 12.01 6.92 3.73 3.11 10.59 12.87 6.65 18.13
11.32 8.79 8.93 30.81 5.49 34.77 19.92 18.06 4.85 6.36 28.32 26.42
6.75 7.56 17.6 12.26 18.71 6.48 5.91 6.12 3.81 9.62 14.27 18.06
22.11 17.15 16.42 30.63 8.2 6.72 7.44 13.61 11.48 3.56 3.95 24.39
6.87 5.12 23.24 17.27 5.81 16.47 30.62 16.29 6.58 17.44 10.13 20.85
8.43 15.02 18.85 15.39 3.33 12.8 5.68 2.96 3.32 13.28 12.5 3.11
13.04 27.71 17.19 13.15 18.68 19.31 7.6 23.29 30.59 13.99 29.53 8.23
29.68 6.29 6.19 8.51 18.13 19.69 8.01 8.61 5.19 13.22 15.76 27.38
10.45 5.52 5.68 16.51 9.81 10.56 23.97 9.64 13.35 4.32 5.03 9.28
19.37 5.5 14.36 6.72 8.44 4.7 11.22 4.16 23.98 17.1 12.67 2.97
3.59 11.74 2.87 10.3 18.8 14.69].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.`

riusciresti a spiegarmi, grazie mille

Il video finisce prima di vedere il risultato di accuracy utilizzando tutte le proprietà per il SVM

Ciao,
eseguendo passo-passo tutte le istruzioni del video, quando eseguo la seguente:
X_sparse = enc.fit_transform(X)

ottengo il seguente errore:

/home/silvasonia/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:368: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values. If you want the future behaviour and silence this warning, you can specify "categories='auto'". In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly. warnings.warn(msg, FutureWarning) /home/silvasonia/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py:390: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead. "use the ColumnTransformer instead.", DeprecationWarning)

Ho fatto qualche errore durrante l'installazione?

C'è un errore nella slide che compare al minuto 5:47, in particolare la document frequency della parola guardano deve essere 2, altrimenti i conti non tornano.