Merge pull request #16 from INGEOTEC/develop

Version - 0.1.4

Author: mgraffg

CompStats/__init__.py

@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-__version__ = '0.1.3'
+__version__ = '0.1.4'
 from CompStats.bootstrap import StatisticSamples
 from CompStats.measurements import CI, SE, difference_p_value
 from CompStats.performance import performance, difference, all_differences, plot_performance, plot_difference

CompStats/interface.py

@@ -59,30 +59,33 @@ class Perf(object):
     >>> perf = Perf(y_val, hy, forest=ens.predict(X_val))
     >>> perf
     <Perf>
-    Prediction statistics with standard error
-    alg-1 = 1.000 (0.000)
-    forest = 0.946 (0.038)
+    Statistic with its standard error (se)
+    statistic (se)
+    0.9792 (0.0221) <= alg-1
+    0.9744 (0.0246) <= forest
     
     If an algorithm's prediction is missing, this can be included by calling the instance, as can be seen in the following instruction. Note that the algorithm's name can also be given with the keyword :py:attr:`name.`
 
     >>> lr = LogisticRegression().fit(X_train, y_train)
     >>> perf(lr.predict(X_val), name='Log. Reg.')
     <Perf>
-    Prediction statistics with standard error
-    alg-1 = 1.000 (0.000)
-    forest = 0.946 (0.038)
-    Log. Reg. = 0.946 (0.038)
+    Statistic with its standard error (se)
+    statistic (se)
+    1.0000 (0.0000) <= Log. Reg.
+    0.9792 (0.0221) <= alg-1
+    0.9744 (0.0246) <= forest
     
     The performance function used to compare the algorithms can be changed, and the same bootstrap samples would be used if the instance were cloned. Consequently, the values are computed using the same samples, as can be seen in the following example.
 
     >>> perf_error = clone(perf)
     >>> perf_error.error_func = lambda y, hy: (y != hy).mean()
     >>> perf_error
     <Perf>
-    Prediction statistics with standard error
-    alg-1 = 0.000 (0.000)
-    forest = 0.044 (0.030)
-    Log. Reg. = 0.044 (0.030)
+    Statistic with its standard error (se)
+    statistic (se)
+    0.0000 (0.0000) <= Log. Reg.
+    0.0222 (0.0237) <= alg-1
+    0.0222 (0.0215) <= forest
 
     """
     def __init__(self, y_true, *y_pred,
@@ -137,18 +140,19 @@ def __sklearn_clone__(self):
         ins.predictions = dict(self.predictions)
         ins._statistic_samples._samples = self.statistic_samples._samples
         return ins
-    
+
     def __repr__(self):
         """Prediction statistics with standard error in parenthesis"""
         return f"<{self.__class__.__name__}>\n{self}"
 
     def __str__(self):
         """Prediction statistics with standard error in parenthesis"""
 
-        se = self.se()
-        output = ["Prediction statistics with standard error"]
-        for key, value in self.statistic().items():
-            output.append(f'{key} = {value:0.3f} ({se[key]:0.3f})')
+        se = self.se
+        output = ["Statistic with its standard error (se)"]
+        output.append("statistic (se)")
+        for key, value in self.statistic.items():
+            output.append(f'{value:0.4f} ({se[key]:0.4f}) <= {key}')
         return "\n".join(output)
 
     def __call__(self, y_pred, name=None):
@@ -196,7 +200,7 @@ def difference(self, wrt_to: str=None):
                 continue
             diff[k] = sign * (base - v)
         diff_ins = Difference(statistic_samples=clone(self.statistic_samples),
-                              statistic=self.statistic(),
+                              statistic=self.statistic,
                               best=self.best[0])
         diff_ins.statistic_samples.calls = diff
         diff_ins.statistic_samples.info['best'] = self.best[0]
@@ -209,12 +213,13 @@ def best(self):
         try:
             return self._best
         except AttributeError:
-            statistic = [(k, v) for k, v in self.statistic().items()]
+            statistic = [(k, v) for k, v in self.statistic.items()]
             statistic = sorted(statistic, key=lambda x: x[1],
                                reverse=self.statistic_samples.BiB)
             self._best = statistic[0]
         return self._best
 
+    @property
     def statistic(self):
         """Statistic
 
@@ -230,7 +235,7 @@ def statistic(self):
         >>> hy = m.predict(X_val)
         >>> ens = RandomForestClassifier().fit(X_train, y_train)
         >>> perf = Perf(y_val, hy, forest=ens.predict(X_val))
-        >>> perf.statistic()
+        >>> perf.statistic
         {'alg-1': 1.0, 'forest': 0.9500891265597148}     
         """
 
@@ -239,6 +244,7 @@ def statistic(self):
                       key=lambda x: x[1], reverse=self.statistic_samples.BiB)
         return dict(data)
 
+    @property
     def se(self):
         """Standard Error
     
@@ -254,9 +260,10 @@ def se(self):
         >>> hy = m.predict(X_val)
         >>> ens = RandomForestClassifier().fit(X_train, y_train)
         >>> perf = Perf(y_val, hy, forest=ens.predict(X_val))
-        >>> print(perf.se())
+        >>> perf.se
         {'alg-1': 0.0, 'forest': 0.026945730782184187}
         """
+
         return SE(self.statistic_samples)
 
     def plot(self, **kwargs):
@@ -405,7 +412,7 @@ class Difference:
     >>> diff
     <Difference>
     difference p-values w.r.t alg-1
-    forest 0.3
+    0.0780 <= forest
     """
 
     statistic_samples:StatisticSamples=None
@@ -420,7 +427,7 @@ def __str__(self):
         """p-value"""
         output = [f"difference p-values w.r.t {self.best}"]
         for k, v in self.p_value().items():
-            output.append(f'{k} {v}')
+            output.append(f'{v:0.4f} <= {k}')
         return "\n".join(output)
 
     def p_value(self):