fix deprecated sklearn syntax as well as more py2/py3 compatiblity fixes

examples_alt/visualization/clustering.py

@@ -23,11 +23,10 @@
 windpark = ds.get_windpark(NREL.park_id['tehachapi'], 15, 2004)
 X = np.array(windpark.get_powermatrix())
 
-clf = KMeans(k=3)
+clf = KMeans(n_clusters=3)
 info = []
 
 for turbine in windpark.turbines:
-
     month_power = windml.util.power_features.compute_highlevel_features(turbine)
     info.append(month_power)
 

examples_alt/visualization/sequence.py

@@ -1,4 +1,4 @@
-"""
+'''
 Sequence Visualization Based on ISOMAP
 -------------------------------------------------------------------------
 
@@ -8,11 +8,12 @@
 Thereby, the mapping maintains important properties of the original
 high-dimensional data so that varying wind conditions and seasonal changes can
 be monitored.
-"""
+'''
 
 # Author: Oliver Kramer <oliver.kramer@uni-oldenburg.de>
 # License: BSD 3 clause
 
+from __future__ import print_function
 import sklearn
 import numpy as np
 import pylab as plt
@@ -28,50 +29,49 @@
 
 X = np.array(windpark.get_powermatrix())
 X_train = X[:2000]
-X_test = X[2000:2000+200*4]
+X_test = X[2000:2000 + 200 * 4]
 
 # computation of ISOMAP projection
-print "computation of ISOMAP projection"
+print('computation of the ISOMAP projection')
 
-if(sklearn.__version__ == "0.9"):
-    X_latent = manifold.Isomap(K, out_dim=2).fit_transform(X_train)
-else:
-    X_latent = manifold.Isomap(K, n_components=2).fit_transform(X_train)
+X_latent = manifold.Isomap(K, n_components=2).fit_transform(X_train)
 
 # computation of sequence of closest embedded patterns
 sequence = []
 for x in X_test:
     win = 0
     smallest = 10E100
     for b in range(len(X_train)):
-        if np.dot(x-X_train[b],x-X_train[b])<smallest:
-            smallest = np.dot(x-X_train[b],x-X_train[b])
+        if np.dot(x - X_train[b], x - X_train[b]) < smallest:
+            smallest = np.dot(x - X_train[b], x - X_train[b])
             win = b
     sequence.append(X_latent[win])
 
 # normalization of the sequence
 sequence = np.array(sequence)
-sequence[:,0] = (sequence[:,0]-sequence[:,0].min())/abs(sequence[:,0].max()-sequence[:,0].min())
-sequence[:,1] = (sequence[:,1]-sequence[:,1].min())/abs(sequence[:,1].max()-sequence[:,1].min())
-col = [[i,j,0.5] for [i,j] in sequence]
+sequence[:, 0] = (sequence[:, 0] - sequence[:, 0].min()) / \
+    abs(sequence[:, 0].max() - sequence[:, 0].min())
+sequence[:, 1] = (sequence[:, 1] - sequence[:, 1].min()) / \
+    abs(sequence[:, 1].max() - sequence[:, 1].min())
+col = [[i, j, 0.5] for [i, j] in sequence]
 
 # plotting ...
-fig = plt.figure(figsize=(20,4))
+fig = plt.figure(figsize=(20, 4))
 ax = plt.subplot(4, 1, 1)
 
-for i in range(0,200):
+for i in range(0, 200):
     ax.bar(i, 1, 1, linewidth=0.0, color=col[i])
 
 ax = plt.subplot(4, 1, 2)
-for i in range(200,400):
+for i in range(200, 400):
     ax.bar(i, 1, 1, linewidth=0.0, color=col[i])
 
 ax = plt.subplot(4, 1, 3)
-for i in range(400,600):
+for i in range(400, 600):
     ax.bar(i, 1, 1, linewidth=0.0, color=col[i])
 
 ax = plt.subplot(4, 1, 4)
-for i in range(600,800):
+for i in range(600, 800):
     ax.bar(i, 1, 1, linewidth=0.0, color=col[i])
 
 plt.show()

examples_alt/visualization/show_coord_topo.py

@@ -1,14 +1,15 @@
-"""
+'''
 Topography of a Windpark
 -------------------------------------------------------------------------
 
 This example shows the topography of a wind park near Tehachapi. The black dots
 illustrate the locations of turbines. The red dot is the target turbine.
-"""
+'''
 
 # Author: Nils A. Treiber <nils.andre.treiber@uni-oldenburg.de>
 # License: BSD 3 clause
 
+from __future__ import print_function
 from windml.datasets.nrel import NREL
 from windml.visualization.show_coord_topo import show_coord_topo
 
@@ -17,8 +18,8 @@
 
 windpark = NREL().get_windpark(NREL.park_id['tehachapi'], 30, 2004)
 
-print "Working on windpark around target turbine", str(windpark.get_target_idx())
-print "Plotting windpark ..."
+print('Working on windpark around target turbine', str(windpark.get_target_idx()))
+print('Plotting windpark ...')
 
-title = "Some Turbines of NREL Data Set"
+title = 'Some Turbines of NREL Data Set'
 show_coord_topo(windpark, title)

examples_alt/visualization/wind_embeddings.py

@@ -1,22 +1,24 @@
-"""
+'''
 Manifold Learning with PCA, LDA, ISOMAP and LLE.
 --------------------------------------------------
 
 For various post-provessing purposes, the reduction of the dimensionality of
 high-dimensional wind power time series may be desirable. The embedding example
 employs dimensionality reduction methods like ISOMAP to embed time series of a
 single turbine into 2-dimensional latent spaces.
-"""
+'''
 
 # Author: Oliver Kramer <oliver.kramer@uni-oldenburg.de>
 # Jendrik Poloczek <jendrik.poloczek@madewithtea.com>
 # License: BSD 3 clause
 
+from __future__ import print_function
 import sklearn
 import numpy as np
 import pylab as plt
 from matplotlib import offsetbox
-from sklearn import manifold, datasets, decomposition, lda
+from sklearn import manifold, datasets, decomposition
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as lda
 
 from windml.visualization.plot_timeseries import plot_timeseries
 from windml.datasets.nrel import NREL
@@ -26,67 +28,60 @@
 windpark = ds.get_windpark(NREL.park_id['tehachapi'], 20, 2004)
 
 X = np.array(windpark.get_powermatrix())
-y = np.array(X[:,-1])
+y = np.array(X[:, -1], dtype=np.int32)
 
-X=X[:1000]
-y=y[:1000]
+X = X[:1000]
+y = y[:1000]
 
-# scale and plot method of embeddings - from SKLEARN
+# scale and plot method of embeddings
 
 def plot_embedding(X, title=None, j=1):
-    x_min, x_max = np.min(X, 0), np.max(X, 0)
-    X = (X - x_min) / (x_max - x_min)
+  x_min, x_max = np.min(X, 0), np.max(X, 0)
+  X = (X - x_min) / (x_max - x_min)
 
-    ax = plt.subplot(2, 2, j)
-    for i in range(X.shape[0]):
-        plt.text(X[i, 0], X[i, 1], str(int(y[i])),
-                color = plt.cm.jet(y[i] / 30.),
-                fontdict={'weight': 'bold', 'size': 9})
+  ax = plt.subplot(2, 2, j)
+  for i in range(X.shape[0]):
+    plt.text(X[i, 0], X[i, 1], str(int(y[i])),
+             color=plt.cm.jet(y[i] / 30.),
+             fontdict={'weight': 'bold', 'size': 9})
+
+  if title is not None:
+    plt.title(title)
 
-    if title is not None:
-        plt.title(title)
 
 j = 1
 figure = plt.figure(figsize=(15, 10))
-title = "Projection of Wind Time Series"
+title = 'Projection of the Wind Time Series'
 
 # computation of PCA projection
-print "Computation of PCA Projection"
+print('Computation of PCA Projection')
 
-X_pca = decomposition.RandomizedPCA(n_components=2).fit_transform(X)
+X_pca = (decomposition
+		 .PCA(svd_solver='randomized', 
+			  n_components=2)
+		 .fit_transform(X))
 
-plot_embedding(X_pca,
-               "PCA "+title,j=1)
+plot_embedding(X_pca, 'PCA '+title, j=1)
 
-# computation of LDA projection
-print "Computation of LDA Projection"
+# computation of the LDA projection
+print('Computation of the LDA Projection')
 X2 = X.copy()
 X2.flat[::X.shape[1] + 1] += 0.01  # make X invertible
 
-X_lda = lda.LDA(n_components=2).fit_transform(X2, y)
-
-plot_embedding(X_lda,
-               "LDA "+title,j=2)
+X_lda = lda(n_components=2).fit_transform(X2, y)
+plot_embedding(X_lda, 'LDA '+title, j=2)
 
-# computation of ISOMAP projection
-print "Computation of ISOMAP Projection"
-if(sklearn.__version__ == "0.9"):
-    X_iso = manifold.Isomap(K, out_dim=2).fit_transform(X)
-else:
-    X_iso = manifold.Isomap(K, n_components=2).fit_transform(X)
+# computation of the ISOMAP projection
+print('Computation of the ISOMAP Projection')
+X_iso = manifold.Isomap(K, n_components=2).fit_transform(X)
 
-plot_embedding(X_iso,
-               "ISOMAP "+title,j=3)
+plot_embedding(X_iso, 'ISOMAP '+title, j=3)
 
-# computation of LLE projection
-print "Computation of LLE Projection"
-if(sklearn.__version__ == "0.9"):
-    clf = manifold.LocallyLinearEmbedding(K, out_dim=2, method='standard')
-else:
-    clf = manifold.LocallyLinearEmbedding(K, n_components=2, method='standard')
+# computation of the LLE projection
+print('Computation of the LLE Projection')
+clf = manifold.LocallyLinearEmbedding(K, n_components=2, method='standard')
 
 X_lle = clf.fit_transform(X)
-plot_embedding(X_lle,
-               "LLE "+title,j=4)
+plot_embedding(X_lle, 'LLE '+title, j=4)
 
 plt.show()