Merge pull request #122 from ContextLab/to_numpy

switch get_values and as_matrix to to_numpy

docs/_build/html/_downloads/crack_egg.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\n#import\nimport quail\n\n#load data\negg = quail.load('example')\n\n#crack egg\ncracked_egg = quail.crack_egg(egg, subjects=[0], lists=[0])\n\ncracked_egg.info()\n\npres = cracked_egg.get_pres_items().as_matrix()[0]\nrec = cracked_egg.get_rec_items().as_matrix()[0]\n\ndef distmat(egg, feature, distdict):\n    f = [xi[feature] for xi in egg.get_pres_features()]\n    return cdist(f, f, distdict[feature])\n\n\nfor idx in range(len(rec)-1):\n    ind1 = np.where(pres==rec[idx])[0][0]\n    ind2 = np.where(pres==rec[idx+1])[0][0]\n    dists = dist[ind1, :]\n    cdist = dist[ind1, ind2]\n    rank = np.mean(np.where(np.sort(dists)[::-1] == cdist))"
+        "# Code source: Andrew Heusser\n# License: MIT\n\n#import\nimport quail\n\n#load data\negg = quail.load('example')\n\n#crack egg\ncracked_egg = quail.crack_egg(egg, subjects=[0], lists=[0])\n\ncracked_egg.info()\n\npres = cracked_egg.get_pres_items().to_numpy()[0]\nrec = cracked_egg.get_rec_items().to_numpy()[0]\n\ndef distmat(egg, feature, distdict):\n    f = [xi[feature] for xi in egg.get_pres_features()]\n    return cdist(f, f, distdict[feature])\n\n\nfor idx in range(len(rec)-1):\n    ind1 = np.where(pres==rec[idx])[0][0]\n    ind2 = np.where(pres==rec[idx+1])[0][0]\n    dists = dist[ind1, :]\n    cdist = dist[ind1, ind2]\n    rank = np.mean(np.where(np.sort(dists)[::-1] == cdist))"
       ]
     }
   ],

docs/_build/html/_downloads/crack_egg.py

@@ -22,8 +22,8 @@
 
 cracked_egg.info()
 
-pres = cracked_egg.get_pres_items().as_matrix()[0]
-rec = cracked_egg.get_rec_items().as_matrix()[0]
+pres = cracked_egg.get_pres_items().to_numpy()[0]
+rec = cracked_egg.get_rec_items().to_numpy()[0]
 
 def distmat(egg, feature, distdict):
     f = [xi[feature] for xi in egg.get_pres_features()]

docs/_build/html/_sources/auto_examples/crack_egg.rst.txt

@@ -33,8 +33,8 @@ This an example of how to crack an egg (take a slice of subjects/lists from it)
 
     cracked_egg.info()
 
-    pres = cracked_egg.get_pres_items().as_matrix()[0]
-    rec = cracked_egg.get_rec_items().as_matrix()[0]
+    pres = cracked_egg.get_pres_items().to_numpy()[0]
+    rec = cracked_egg.get_rec_items().to_numpy()[0]
 
     def distmat(egg, feature, distdict):
         f = [xi[feature] for xi in egg.get_pres_features()]

docs/auto_examples/crack_egg.ipynb

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\n#import\nimport quail\n\n#load data\negg = quail.load('example')\n\n#crack egg\ncracked_egg = quail.crack_egg(egg, subjects=[0], lists=[0])\n\ncracked_egg.info()\n\npres = cracked_egg.get_pres_items().as_matrix()[0]\nrec = cracked_egg.get_rec_items().as_matrix()[0]\n\ndef distmat(egg, feature, distdict):\n    f = [xi[feature] for xi in egg.get_pres_features()]\n    return cdist(f, f, distdict[feature])\n\n\nfor idx in range(len(rec)-1):\n    ind1 = np.where(pres==rec[idx])[0][0]\n    ind2 = np.where(pres==rec[idx+1])[0][0]\n    dists = dist[ind1, :]\n    cdist = dist[ind1, ind2]\n    rank = np.mean(np.where(np.sort(dists)[::-1] == cdist))"
+        "# Code source: Andrew Heusser\n# License: MIT\n\n#import\nimport quail\n\n#load data\negg = quail.load('example')\n\n#crack egg\ncracked_egg = quail.crack_egg(egg, subjects=[0], lists=[0])\n\ncracked_egg.info()\n\npres = cracked_egg.get_pres_items().to_numpy()[0]\nrec = cracked_egg.get_rec_items().to_numpy()[0]\n\ndef distmat(egg, feature, distdict):\n    f = [xi[feature] for xi in egg.get_pres_features()]\n    return cdist(f, f, distdict[feature])\n\n\nfor idx in range(len(rec)-1):\n    ind1 = np.where(pres==rec[idx])[0][0]\n    ind2 = np.where(pres==rec[idx+1])[0][0]\n    dists = dist[ind1, :]\n    cdist = dist[ind1, ind2]\n    rank = np.mean(np.where(np.sort(dists)[::-1] == cdist))"
       ]
     }
   ],

docs/auto_examples/crack_egg.py

@@ -22,8 +22,8 @@
 
 cracked_egg.info()
 
-pres = cracked_egg.get_pres_items().as_matrix()[0]
-rec = cracked_egg.get_rec_items().as_matrix()[0]
+pres = cracked_egg.get_pres_items().to_numpy()[0]
+rec = cracked_egg.get_rec_items().to_numpy()[0]
 
 def distmat(egg, feature, distdict):
     f = [xi[feature] for xi in egg.get_pres_features()]

docs/auto_examples/crack_egg.rst

@@ -33,8 +33,8 @@ This an example of how to crack an egg (take a slice of subjects/lists from it)
 
     cracked_egg.info()
 
-    pres = cracked_egg.get_pres_items().as_matrix()[0]
-    rec = cracked_egg.get_rec_items().as_matrix()[0]
+    pres = cracked_egg.get_pres_items().to_numpy()[0]
+    rec = cracked_egg.get_rec_items().to_numpy()[0]
 
     def distmat(egg, feature, distdict):
         f = [xi[feature] for xi in egg.get_pres_features()]

quail/analysis/recmat.py

@@ -51,16 +51,16 @@ def recall_matrix(egg, match='exact', distance='euclidean', features=None):
         return _recmat_smooth(egg.pres, egg.rec, features, distance, match)
 
 def _recmat_exact(presented, recalled, features):
-    lists = presented.index.get_values()
+    lists = presented.index.to_numpy()()
     cols = max(presented.shape[1], recalled.shape[1])
     result = np.empty((presented.shape[0], cols))*np.nan
     for li, l in enumerate(lists):
         p_list = presented.loc[l]
         r_list = recalled.loc[l]
         for i, feature in enumerate(features):
             get_feature = lambda x: np.array(x[feature]) if not np.array(pd.isnull(x['item'])).any() else np.nan
-            p = np.vstack(p_list.apply(get_feature).get_values())
-            r = r_list.dropna().apply(get_feature).get_values()
+            p = np.vstack(p_list.apply(get_feature).to_numpy()())
+            r = r_list.dropna().apply(get_feature).to_numpy()()
             r = np.vstack(list(filter(lambda x: x is not np.nan, r)))
             try:
                 m = [np.where((p==x).all(axis=1))[0] for x in r]
@@ -90,15 +90,15 @@ def _recmat_smooth(presented, recalled, features, distance, match):
     return recmat
 
 def _similarity_smooth(presented, recalled, features, distance):
-    lists = presented.index.get_values()
+    lists = presented.index.to_numpy()()
     res = np.empty((len(lists), len(features), recalled.iloc[0].shape[0], presented.iloc[0].shape[0]))*np.nan
     for li, l in enumerate(lists):
         p_list = presented.loc[l]
         r_list = recalled.loc[l]
         for i, feature in enumerate(features):
             get_feature = lambda x: np.array(x[feature]) if np.array(pd.notna(x['item'])).any() else np.nan
-            p = np.vstack(p_list.apply(get_feature).get_values())
-            r = r_list.dropna().apply(get_feature).get_values()
+            p = np.vstack(p_list.apply(get_feature).to_numpy()())
+            r = r_list.dropna().apply(get_feature).to_numpy()()
             r = np.vstack(list(filter(lambda x: x is not np.nan, r)))
             tmp = 1 - cdist(r, p, distance)
             res[li, i, :tmp.shape[0], :] =  tmp