ENH Use function names that match scikit-learn

Author: luispedro

ch02/figure4_5.py

@@ -11,7 +11,7 @@
 from matplotlib.colors import ListedColormap
 from load import load_dataset
 import numpy as np
-from knn import learn_model, apply_model, accuracy
+from knn import fit_model, predict, accuracy
 
 feature_names = [
     'area',
@@ -31,8 +31,8 @@ def train_plot(features, labels):
     Y = np.linspace(y0, y1, 100)
     X, Y = np.meshgrid(X, Y)
 
-    model = learn_model(1, features[:, (0, 2)], np.array(labels))
-    C = apply_model(
+    model = fit_model(1, features[:, (0, 2)], np.array(labels))
+    C = predict(
         np.vstack([X.ravel(), Y.ravel()]).T, model).reshape(X.shape)
     if COLOUR_FIGURE:
         cmap = ListedColormap([(1., .6, .6), (.6, 1., .6), (.6, .6, 1.)])

ch02/heldout.py

@@ -11,7 +11,7 @@
 from matplotlib import pyplot as plt
 import numpy as np
 from sklearn.datasets import load_iris
-from threshold import learn_model, apply_model, accuracy
+from threshold import fit_model, accuracy
 
 data = load_iris()
 features = data['data']
@@ -29,7 +29,7 @@
 testing = np.tile([True, False], 50) # testing = [True,False,True,False,True,False...]
 training = ~testing
 
-model = learn_model(features[training], virginica[training])
+model = fit_model(features[training], virginica[training])
 train_accuracy = accuracy(features[training], virginica[training], model)
 test_accuracy = accuracy(features[testing], virginica[testing], model)
 

ch02/knn.py

@@ -7,8 +7,8 @@
 
 import numpy as np
 
-
-def learn_model(k, features, labels):
+# This function was called ``learn_model`` in the first edition
+def fit_model(k, features, labels):
     '''Learn a k-nn model'''
     # There is no model in k-nn, just a copy of the inputs
     return k, features.copy(), labels.copy()
@@ -25,8 +25,8 @@ def plurality(xs):
         if v == maxv:
             return k
 
-
-def apply_model(features, model):
+# This function was called ``apply_model`` in the first edition
+def predict(features, model):
     '''Apply k-nn model'''
     k, train_feats, labels = model
     results = []
@@ -42,5 +42,5 @@ def apply_model(features, model):
 
 
 def accuracy(features, labels, model):
-    preds = apply_model(features, model)
+    preds = predict(features, model)
     return np.mean(preds == labels)

ch02/seeds_knn.py

@@ -7,7 +7,7 @@
 
 from load import load_dataset
 import numpy as np
-from knn import learn_model, apply_model, accuracy
+from knn import fit_model, accuracy
 
 features, labels = load_dataset('seeds')
 
@@ -19,7 +19,7 @@ def cross_validate(features, labels):
         training = np.ones(len(features), bool)
         training[fold::10] = 0
         testing = ~training
-        model = learn_model(1, features[training], labels[training])
+        model = fit_model(1, features[training], labels[training])
         test_error = accuracy(features[testing], labels[testing], model)
         error += test_error
 

ch02/seeds_threshold.py

@@ -7,7 +7,7 @@
 
 from load import load_dataset
 import numpy as np
-from threshold import learn_model, apply_model, accuracy
+from threshold import fit_model, accuracy
 
 features, labels = load_dataset('seeds')
 
@@ -24,7 +24,7 @@
     # whatever is not training is for testing
     testing = ~training
 
-    model = learn_model(features[training], labels[training])
+    model = fit_model(features[training], labels[training])
     test_error = accuracy(features[testing], labels[testing], model)
     error += test_error
 

ch02/threshold.py

@@ -8,7 +8,8 @@
 import numpy as np
 
 
-def learn_model(features, labels):
+# This function was called ``learn_model`` in the first edition
+def fit_model(features, labels):
     '''Learn a simple threshold model'''
     best_acc = -1.0
     # Loop over all the features:
@@ -30,13 +31,14 @@ def learn_model(features, labels):
     return best_t, best_fi
 
 
-def apply_model(features, model):
+# This function was called ``apply_model`` in the first edition
+def predict(features, model):
     '''Apply a learned model'''
-    # A model is a pair as returned by learn_model
+    # A model is a pair as returned by fit_model
     t, fi = model
     return features[:, fi] > t
 
 def accuracy(features, labels, model):
     '''Compute the accuracy of the model'''
-    preds = apply_model(features, model)
+    preds = predict(features, model)
     return np.mean(preds == labels)