Evaluation

emjun · Apr 5, 2019 · c7c1aa0b08c9b360fe41e4ac49723a5d8350d781 · c7c1aa0
1 parent a08e74a
commit c7c1aa0b08c9b360fe41e4ac49723a5d8350d781
Unified Split

Showing with 12 additions and 4 deletions.

+3 −1 evaluation/evaluation.R

+5 −1 tea/evaluate.py

+2 −0 tea/evaluate_helper_methods.py

+2 −2 tests/integration_tests/test_integration.py
diff --git a/evaluation/evaluation.R b/evaluation/evaluation.R
@@ -59,6 +59,7 @@ rcorr(liarMatrix)
 cor(liarData$Position, liarData$Creativity, method = "kendall")
 cor(liarData$Position, liarData$Creativity, method = "spearman")
 cor.test(liarData$Position, liarData$Creativity, alternative = "less", method = "pearson")
+  
+cor.test(liarData$Position, liarData$Creativity, alternative = "less", method = "kendall")
 ### Pointbiseral (p. 229 -233)
 # Field et al.
@@ -77,6 +78,7 @@ prop.table(catFrequencies)
 ### Independent T-Test 
 # Kabacoff (p. 164-165)
 library (MASS)
+  
+t.test(Prob ~ So, data=UScrime)
 t.test(Prob ~ So, data=UScrime, var.equal=TRUE)
 # could also be written as 
 # t.test(y1, y2) # y1 and y2 dependent variables for the two groups
@@ -309,7 +311,7 @@ catData <- read.delim("/Users/emjun/Git/tea-lang/evaluation/discovering-statisti
 food <- c(10, 28)
 affection <- c(114, 48)
 catsTable <- cbind(food, affection) 
-  
-CrossTable(catsData$Training, catsData$Dance, fisher = TRUE, chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")
+  
+CrossTable(catData$Training, catData$Dance, fisher = TRUE, chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")
 # Equivalent to the above
 #CrossTable(catsTable, fisher = TRUE, chisq = TRUE, expected = TRUE, sresid = TRUE, format = "SPSS")
 #CrossTable(catsData$Training, catsData$Dance, fisher = TRUE, chisq = TRUE, expected = TRUE, prop.c = FALSE, prop.t = FALSE, prop.chisq = FALSE,  sresid = TRUE, format = "SPSS")

diff --git a/tea/evaluate.py b/tea/evaluate.py
@@ -395,14 +395,18 @@ def evaluate(dataset: Dataset, expr: Node, assumptions: Dict[str, str], design:
        for test in tests: 
            test_result = execute_test(dataset, design, combined_data, test)
            results[test] = test_result
+  
+
+  
+        if 'bootstrap' in tests: 
+  
+            # import pdb; pdb.set_trace()
+  
+            pass
        # TODO: Calculate effect size for experiments!!
        # calculate_effect_size()
        # import pdb; pdb.set_trace()
        # TODO One-sided test?
        if expr.predictions:
-  
-            import pdb; pdb.set_trace()
+  
+            # import pdb; pdb.set_trace()
            # T-tests: may greater-than test when p/2 < alpha and t > 0, and of a less-than test when p/2 < alpha and t < 0
            # --> may want to divide p-value in t-test (before return)

diff --git a/tea/evaluate_helper_methods.py b/tea/evaluate_helper_methods.py
@@ -683,6 +683,8 @@ def bootstrap(dataset: Dataset, combined_data: CombinedData):
                # store all the medians & confidence intervals
                # return all the medians & CIs
                # data.append(cat_data)
+  
+
+  
+    return calculations
        # Interaction effects
        # Add the interactions

diff --git a/tests/integration_tests/test_integration.py b/tests/integration_tests/test_integration.py
@@ -302,8 +302,8 @@ def test_indep_t_test():
                        }
    assumptions = {
        'Type I (False Positive) Error Rate': 0.05,
-  
-        'normal distribution': ['So'],
-  
-        'groups normally distributed': [['So', 'Prob']]
+  
+        # 'normal distribution': ['So'],
+  
+        # 'groups normally distributed': [['So', 'Prob']]
    }