documentation fixes

doc/conf.py

@@ -25,7 +25,11 @@
 sys.path.insert(0, curr_path)
 
 import mock
-MOCK_MODULES = ['numpy', 'scipy', 'scipy.sparse', 'sklearn', 'sklearn.preprocessing', 'sklearn.linear_model', 'matplotlib']
+MOCK_MODULES = ['numpy', 'scipy', 'scipy.sparse', 'sklearn',
+        'sklearn.preprocessing', 'sklearn.linear_model', 'matplotlib',
+        'sklearn.datasets', 'sklearn.ensemble', 'sklearn.cross_validation',
+        'sklearn.feature_extraction', 'sklearn.feature_extraction.text',
+        'sklearn.metrics', 'sklearn.naive_bayes', 'sklearn.pipeline']
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = mock.Mock()
 

doc/lime.rst

@@ -1,46 +1,77 @@
 lime package
 ============
 
+Subpackages
+-----------
+
+.. toctree::
+
+    lime.tests
+
 Submodules
 ----------
 
-lime.lime_tabular module
-------------------------
+lime\.discretize module
+-----------------------
 
-.. automodule:: lime.lime_tabular
+.. automodule:: lime.discretize
     :members:
     :undoc-members:
     :show-inheritance:
 
-lime.lime_text module
----------------------
+lime\.exceptions module
+-----------------------
 
-.. automodule:: lime.lime_text
+.. automodule:: lime.exceptions
     :members:
     :undoc-members:
     :show-inheritance:
 
-lime.discretize module
-----------------------
+lime\.explanation module
+------------------------
 
-.. automodule:: lime.discretize
+.. automodule:: lime.explanation
     :members:
     :undoc-members:
     :show-inheritance:
 
-lime.explanation module
+lime\.lime\_base module
 -----------------------
 
-.. automodule:: lime.explanation
+.. automodule:: lime.lime_base
     :members:
     :undoc-members:
     :show-inheritance:
 
-lime.lime_base module
----------------------
+lime\.lime\_image module
+------------------------
 
-.. automodule:: lime.lime_base
+.. automodule:: lime.lime_image
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+lime\.lime\_tabular module
+--------------------------
+
+.. automodule:: lime.lime_tabular
     :members:
     :undoc-members:
     :show-inheritance:
 
+lime\.lime\_text module
+-----------------------
+
+.. automodule:: lime.lime_text
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: lime
+    :members:
+    :undoc-members:
+    :show-inheritance:

lime/lime_tabular.py

@@ -123,7 +123,7 @@ def __init__(self,
                 categorical_names[x][y] represents the name of the yth value of
                 column x.
             kernel_width: kernel width for the exponential kernel.
-            If None, defaults to sqrt(number of columns) * 0.75
+                If None, defaults to sqrt (number of columns) * 0.75
             verbose: if true, print local prediction values from linear model
             class_names: list of class names, ordered according to whatever the
                 classifier is using. If not present, class names will be '0',
@@ -229,8 +229,8 @@ def explain_instance(self,
                 function that takes a numpy array and outputs prediction
                 probabilities. For regressors, this takes a numpy array and
                 returns the predictions. For ScikitClassifiers, this is
-                    `classifier.predict_proba()`. For ScikitRegressors, this
-                    is `regressor.predict()`.
+                `classifier.predict_proba()`. For ScikitRegressors, this
+                is `regressor.predict()`.
             labels: iterable with labels to be explained.
             top_labels: if not None, ignore labels and produce explanations for
                 the K labels with highest prediction probabilities, where K is
@@ -239,8 +239,8 @@ def explain_instance(self,
             num_samples: size of the neighborhood to learn the linear model
             distance_metric: the distance metric to use for weights.
             model_regressor: sklearn regressor to use in explanation. Defaults
-            to Ridge regression in LimeBase. Must have model_regressor.coef_
-            and 'sample_weight' as a parameter to model_regressor.fit()
+                to Ridge regression in LimeBase. Must have model_regressor.coef_
+                and 'sample_weight' as a parameter to model_regressor.fit()
 
         Returns:
             An Explanation object (see explanation.py) with the corresponding

lime/tests/test_lime_tabular.py

@@ -1,6 +1,10 @@
 import unittest
 
 import numpy as np
+import sklearn
+import sklearn.datasets
+import sklearn.ensemble
+import sklearn.linear_model
 from sklearn.datasets import load_iris, make_classification
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.linear_model import Lasso

lime/tests/test_lime_text.py

@@ -1,5 +1,6 @@
 import unittest
 
+import sklearn
 from sklearn.datasets import fetch_20newsgroups
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.linear_model import Lasso