10000 [MRG+1] Don't use deprecated 1d X (or deprecated matplotlib stuff) in examples by amueller · Pull Request #5721 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Don't use deprecated 1d X (or deprecated matplotlib stuff) in examples #5721

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Dec 11, 2015
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[MRG+1] Don't use deprecated 1d X (or deprecated matplotlib stuff) in examples #5721

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4 changes: 2 additions & 2 deletions examples/applications/plot_prediction_latency.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def atomic_benchmark_estimator(estimator, X_test, verbose=False):
n_instances = X_test.shape[0]
runtimes = np.zeros(n_instances, dtype=np.float)
for i in range(n_instances):
instance = X_test[i, :]
instance = X_test[[i], :]
start = time.time()
estimator.predict(instance)
runtimes[i] = time.time() - start
Expand Down Expand Up @@ -241,7 +241,7 @@ def benchmark_throughputs(configuration, duration_secs=0.1):
start_time = time.time()
n_predictions = 0
while (time.time() - start_time) < duration_secs:
estimator_config['instance'].predict(X_test[0])
estimator_config['instance'].predict(X_test[[0]])
n_predictions += 1
throughputs[estimator_config['name']] = n_predictions / duration_secs
return throughputs
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6 changes: 2 additions & 4 deletions examples/classification/plot_lda_qda.py
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Expand Up @@ -67,8 +67,6 @@ def plot_data(lda, X, y, y_pred, fig_index):
X0, X1 = X[y == 0], X[y == 1]
X0_tp, X0_fp = X0[tp0], X0[~tp0]
X1_tp, X1_fp = X1[tp1], X1[~tp1]
xmin, xmax = X[:, 0].min(), X[:, 0].max()
ymin, ymax = X[:, 1].min(), X[:, 1].max()

# class 0: dots
plt.plot(X0_tp[:, 0], X0_tp[:, 1], 'o', color='red')
Expand Down Expand Up @@ -133,8 +131,8 @@ def plot_qda_cov(qda, splot):
plt.axis('tight')

# Quadratic Discriminant Analysis
qda = QuadraticDiscriminantAnalysis()
y_pred = qda.fit(X, y, store_covariances=True).predict(X)
qda = QuadraticDiscriminantAnalysis(store_covariances=True)
y_pred = qda.fit(X, y).predict(X)
splot = plot_data(qda, X, y, y_pred, fig_index=2 * i + 2)
plot_qda_cov(qda, splot)
plt.axis('tight')
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2 changes: 1 addition & 1 deletion examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py
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Expand Up @@ -85,7 +85,7 @@
clf = GridSearchCV(clf, {'anova__percentile': [5, 10, 20]}, cv=cv)
clf.fit(X, y) # set the best parameters
coef_ = clf.best_estimator_.steps[-1][1].coef_
coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_)
coef_ = clf.best_estimator_.steps[0][1].inverse_transform(coef_.reshape(1, -1))
coef_selection_ = coef_.reshape(size, size)

###############################################################################
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4 changes: 2 additions & 2 deletions examples/decomposition/plot_sparse_coding.py
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Expand Up @@ -75,7 +75,7 @@ def ricker_matrix(width, resolution, n_components):
for title, algo, alpha, n_nonzero, color in estimators:
coder = SparseCoder(dictionary=D, transform_n_nonzero_coefs=n_nonzero,
transform_alpha=alpha, transform_algorithm=algo)
x = coder.transform(y)
x = coder.transform(y.reshape(1, -1))
density = len(np.flatnonzero(x))
x = np.ravel(np.dot(x, D))
squared_error = np.sum((y - x) ** 2)
Expand All @@ -86,7 +86,7 @@ def ricker_matrix(width, resolution, n_components):
# Soft thresholding debiasing
coder = SparseCoder(dictionary=D, transform_algorithm='threshold',
transform_alpha=20)
x = coder.transform(y)
x = coder.transform(y.reshape(1, -1))
_, idx = np.where(x != 0)
x[0, idx], _, _, _ = np.linalg.lstsq(D[idx, :].T, y)
x = np.ravel(np.dot(x, D))
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2 changes: 1 addition & 1 deletion examples/ensemble/plot_gradient_boosting_regression.py
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Expand Up @@ -47,7 +47,7 @@
# compute test set deviance
test_score = np.zeros((params['n_estimators'],), dtype=np.float64)

for i, y_pred in enumerate(clf.staged_decision_function(X_test)):
for i, y_pred in enumerate(clf.staged_predict(X_test)):
test_score[i] = clf.loss_(y_test, y_pred)

plt.figure(figsize=(12, 6))
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2 changes: 1 addition & 1 deletion examples/linear_model/plot_sgd_separating_hyperplane.py
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Expand Up @@ -30,7 +30,7 @@
for (i, j), val in np.ndenumerate(X1):
x1 = val
x2 = X2[i, j]
p = clf.decision_function([x1, x2])
p = clf.decision_function([[x1, x2]])
Z[i, j] = p[0]
levels = [-1.0, 0.0, 1.0]
linestyles = ['dashed', 'solid', 'dashed']
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2 changes: 1 addition & 1 deletion examples/manifold/plot_compare_methods.py
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Expand Up @@ -113,7 +113,7 @@
Y = tsne.fit_transform(X)
t1 = time()
print("t-SNE: %.2g sec" % (t1 - t0))
ax = fig.add_subplot(250)
ax = fig.add_subplot(2, 5, 10)
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are you sure about the 10 ?

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yes. they deprecated the previous syntax because it was real weird ;)

plt.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral)
plt.title("t-SNE (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
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10 changes: 5 additions & 5 deletions examples/manifold/plot_manifold_sphere.py
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Expand Up @@ -103,7 +103,7 @@
print("%s: %.2g sec" % ('ISO', t1 - t0))

ax = fig.add_subplot(257)
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add_subplot(2, 5, 7)

same below

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this is not actually deprecated, only the 0 meaning 10. I'm happy to replace all of them if you like. Personally I favor plt.subplots ;)

plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.title("%s (%.2g sec)" % ('Isomap', t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
Expand All @@ -117,7 +117,7 @@
print("MDS: %.2g sec" % (t1 - t0))

ax = fig.add_subplot(258)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.title("MDS (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
Expand All @@ -132,7 +132,7 @@
print("Spectral Embedding: %.2g sec" % (t1 - t0))

ax = fig.add_subplot(259)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.title("Spectral Embedding (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
Expand All @@ -145,8 +145,8 @@
t1 = time()
print("t-SNE: %.2g sec" % (t1 - t0))

ax = fig.add_subplot(250)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
ax = fig.add_subplot(2, 5, 10)
plt.scatter(trans_data[0], trans_data[1], c=colors, cmap=plt.cm.rainbow)
plt.title("t-SNE (%.2g sec)" % (t1 - t0))
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
Expand Down
2 changes: 1 addition & 1 deletion examples/neighbors/plot_approximate_nearest_neighbors_scalability.py
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Expand Up @@ -89,7 +89,7 @@

for i in range(n_iter):
# pick one query at random to study query time variability in LSHForest
query = queries[rng.randint(0, n_queries)]
query = queries[[rng.randint(0, n_queries)]]

t0 = time.time()
exact_neighbors = nbrs.kneighbors(query, return_distance=False)
Expand Down
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