updating documentation

MachineLearningBCAM · Aug 9, 2021 · 012012a · 012012a
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diff --git a/.buildinfo b/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: dab5c77040d718b25e532551623570bb
+config: 82633e0620d612db95f8a2e1acfe652d
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/_downloads/79dda51f6f77b894ece477ed9c8a01d4/example2.py b/_downloads/79dda51f6f77b894ece477ed9c8a01d4/example2.py
@@ -84,7 +84,7 @@ def runCMRC(phi, loss):
           ********************** \n\n')
 
     print('\t\t 1. Using 0-1 loss and relu feature mapping \n\n')
-    runCMRC(phi='relu', loss='0-1')
+    runCMRC(phi='linear', loss='0-1')
 
     print('\t\t 2. Using log loss and relu feature mapping \n\n')
     runCMRC(phi='relu', loss='log')
diff --git a/_downloads/cffe955e48c6ac5c42cd9ae87562553d/example2.ipynb b/_downloads/cffe955e48c6ac5c42cd9ae87562553d/example2.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\n\nimport numpy as np\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom MRCpy import CMRC\n# Import the datasets\nfrom MRCpy.datasets import *\n\n# Data sets\nloaders = [load_mammographic, load_haberman, load_indian_liver,\n           load_diabetes, load_credit]\ndataName = [\"mammographic\", \"haberman\", \"indian_liver\", \"diabetes\", \"credit\"]\n\n\ndef runCMRC(phi, loss):\n\n    random_seed = 0\n    res_mean = np.zeros(len(dataName))\n    res_std = np.zeros(len(dataName))\n    np.random.seed(random_seed)\n\n    # Iterate through each of the dataset and fit the MRC classfier.\n    for j, load in enumerate(loaders):\n\n        # Loading the dataset\n        X, Y = load(return_X_y=True)\n        r = len(np.unique(Y))\n        n, d = X.shape\n\n        # Print the dataset name\n        print(\" ############## \\n\" + dataName[j] + \" n= \" + str(n) +\n              \" , d= \" + str(d) + \", cardY= \" + str(r))\n\n        clf = CMRC(phi=phi, loss=loss, use_cvx=True,\n                   solver='MOSEK', max_iters=10000, s=0.3)\n\n        # Generate the partitions of the stratified cross-validation\n        cv = StratifiedKFold(n_splits=10, random_state=random_seed,\n                             shuffle=True)\n\n        np.random.seed(random_seed)\n        cvError = list()\n        auxTime = 0\n\n        # Paired and stratified cross-validation\n        for train_index, test_index in cv.split(X, Y):\n\n            X_train, X_test = X[train_index], X[test_index]\n            y_train, y_test = Y[train_index], Y[test_index]\n\n            # Normalizing the data\n            std_scale = preprocessing.StandardScaler().fit(X_train, y_train)\n            X_train = std_scale.transform(X_train)\n            X_test = std_scale.transform(X_test)\n\n            # Save start time for computing training time\n            startTime = time.time()\n\n            clf.fit(X_train, y_train)\n\n            # Calculate the training time\n            auxTime += time.time() - startTime\n\n            y_pred = clf.predict(X_test)\n\n            cvError.append(np.average(y_pred != y_test))\n\n        res_mean[j] = np.average(cvError)\n        res_std[j] = np.std(cvError)\n\n        print(\" error= \" + \":\\t\" + str(res_mean[j]) + \"\\t+/-\\t\" +\n              str(res_std[j]) + \"\\navg_train_time= \" + \":\\t\" +\n              str(auxTime / 10) + ' secs' + \"\\n ############## \\n\\n\\n\")\n\n\nif __name__ == '__main__':\n\n    print('******************** \\\n          Example 2 (CMRC with the additional marginal constraints) \\\n          ********************** \\n\\n')\n\n    print('\\t\\t 1. Using 0-1 loss and relu feature mapping \\n\\n')\n    runCMRC(phi='relu', loss='0-1')\n\n    print('\\t\\t 2. Using log loss and relu feature mapping \\n\\n')\n    runCMRC(phi='relu', loss='log')"
+        "import time\n\nimport numpy as np\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom MRCpy import CMRC\n# Import the datasets\nfrom MRCpy.datasets import *\n\n# Data sets\nloaders = [load_mammographic, load_haberman, load_indian_liver,\n           load_diabetes, load_credit]\ndataName = [\"mammographic\", \"haberman\", \"indian_liver\", \"diabetes\", \"credit\"]\n\n\ndef runCMRC(phi, loss):\n\n    random_seed = 0\n    res_mean = np.zeros(len(dataName))\n    res_std = np.zeros(len(dataName))\n    np.random.seed(random_seed)\n\n    # Iterate through each of the dataset and fit the MRC classfier.\n    for j, load in enumerate(loaders):\n\n        # Loading the dataset\n        X, Y = load(return_X_y=True)\n        r = len(np.unique(Y))\n        n, d = X.shape\n\n        # Print the dataset name\n        print(\" ############## \\n\" + dataName[j] + \" n= \" + str(n) +\n              \" , d= \" + str(d) + \", cardY= \" + str(r))\n\n        clf = CMRC(phi=phi, loss=loss, use_cvx=True,\n                   solver='MOSEK', max_iters=10000, s=0.3)\n\n        # Generate the partitions of the stratified cross-validation\n        cv = StratifiedKFold(n_splits=10, random_state=random_seed,\n                             shuffle=True)\n\n        np.random.seed(random_seed)\n        cvError = list()\n        auxTime = 0\n\n        # Paired and stratified cross-validation\n        for train_index, test_index in cv.split(X, Y):\n\n            X_train, X_test = X[train_index], X[test_index]\n            y_train, y_test = Y[train_index], Y[test_index]\n\n            # Normalizing the data\n            std_scale = preprocessing.StandardScaler().fit(X_train, y_train)\n            X_train = std_scale.transform(X_train)\n            X_test = std_scale.transform(X_test)\n\n            # Save start time for computing training time\n            startTime = time.time()\n\n            clf.fit(X_train, y_train)\n\n            # Calculate the training time\n            auxTime += time.time() - startTime\n\n            y_pred = clf.predict(X_test)\n\n            cvError.append(np.average(y_pred != y_test))\n\n        res_mean[j] = np.average(cvError)\n        res_std[j] = np.std(cvError)\n\n        print(\" error= \" + \":\\t\" + str(res_mean[j]) + \"\\t+/-\\t\" +\n              str(res_std[j]) + \"\\navg_train_time= \" + \":\\t\" +\n              str(auxTime / 10) + ' secs' + \"\\n ############## \\n\\n\\n\")\n\n\nif __name__ == '__main__':\n\n    print('******************** \\\n          Example 2 (CMRC with the additional marginal constraints) \\\n          ********************** \\n\\n')\n\n    print('\\t\\t 1. Using 0-1 loss and relu feature mapping \\n\\n')\n    runCMRC(phi='linear', loss='0-1')\n\n    print('\\t\\t 2. Using log loss and relu feature mapping \\n\\n')\n    runCMRC(phi='relu', loss='log')"
       ]
     }
   ],

diff --git a/_sources/auto_examples/example2.rst.txt b/_sources/auto_examples/example2.rst.txt
@@ -108,7 +108,7 @@ Example of using CMRC with some of the common classification datasets.
               ********************** \n\n')
 
         print('\t\t 1. Using 0-1 loss and relu feature mapping \n\n')
-        runCMRC(phi='relu', loss='0-1')
+        runCMRC(phi='linear', loss='0-1')
 
         print('\t\t 2. Using log loss and relu feature mapping \n\n')
         runCMRC(phi='relu', loss='log')

diff --git a/auto_examples/example2.html b/auto_examples/example2.html
@@ -253,7 +253,7 @@
 <span class="s1">          ********************** </span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
 
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t\t</span><span class="s1"> 1. Using 0-1 loss and relu feature mapping </span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="n">runCMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;0-1&#39;</span><span class="p">)</span>
+    <span class="n">runCMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;0-1&#39;</span><span class="p">)</span>
 
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t\t</span><span class="s1"> 2. Using log loss and relu feature mapping </span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
     <span class="n">runCMRC</span><span class="p">(</span><span class="n">phi</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>

diff --git a/searchindex.js b/searchindex.js