Commit 806673e4 authored by Janez K's avatar Janez K
Browse files

dodal mere za regresijo

parent 52232413
......@@ -20,7 +20,7 @@
"workflow": null,
"user": null,
"order": 1,
"name": "Classification"
"name": "Classification and Regression"
}
},
{
......@@ -82,7 +82,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 2,
"description": ""
}
},
......@@ -120,7 +120,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 3,
"description": ""
}
},
......@@ -158,7 +158,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 4,
"description": ""
}
},
......@@ -196,7 +196,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 5,
"description": ""
}
},
......@@ -234,7 +234,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 6,
"description": ""
}
},
......@@ -272,7 +272,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 7,
"description": ""
}
},
......@@ -310,7 +310,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 8,
"description": ""
}
},
......@@ -366,7 +366,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 9,
"description": ""
}
},
......@@ -404,7 +404,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 10,
"description": ""
}
},
......@@ -442,7 +442,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 11,
"description": ""
}
},
......@@ -480,7 +480,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 12,
"description": ""
}
},
......@@ -666,7 +666,7 @@
"workflow": null,
"user": null,
"order": 1,
"name": "Statistics"
"name": "Classification Statistics"
}
},
{
......@@ -804,7 +804,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 2,
"description": "Computes the Brier's score, defined as the average (over test examples) of sumx(t(x)-p(x))^2, where x is a class, t(x) is 1 for the correct class and 0 for the others, and p(x) is the probability that the classifier assigned to the class x."
}
},
......@@ -878,7 +878,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 3,
"description": "Computes classification accuracy, i.e. percentage of matches between predicted and actual class. The function returns a list of classification accuracies of all classifiers tested. If reportSE is set to true, the list will contain tuples with accuracies and standard errors."
}
},
......@@ -952,7 +952,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 4,
"description": ""
}
},
......@@ -1044,7 +1044,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 5,
"description": "With the confusion matrix defined in terms of positive and negative classes, you can also compute the sensitivity [TP/(TP+FN)], specificity [TN/(TN+FP)], positive predictive value [TP/(TP+FP)] and negative predictive value [TN/(TN+FN)]. In information retrieval, positive predictive value is called precision (the ratio of the number of relevant records retrieved to the total number of irrelevant and relevant records retrieved), and sensitivity is called recall (the ratio of the number of relevant records retrieved to the total number of relevant records in the database). The harmonic mean of precision and recall is called an F-measure, where, depending on the ratio of the weight between precision and recall is implemented as F1 [2*precision*recall/(precision+recall)] or, for a general case, Falpha [(1+alpha)*precision*recall / (alpha*precision + recall)]."
}
},
......@@ -1222,7 +1222,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 6,
"description": "Computes the average probability assigned to the correct class."
}
},
......@@ -1296,7 +1296,7 @@
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"order": 7,
"description": "Computes the information score as defined by Kononenko and Bratko (1991). "
}
},
......@@ -1349,6 +1349,354 @@
"description": ""
}
},
{
"pk": 17,
"model": "workflows.category",
"fields": {
"uid": "d13c01e6-675f-4c48-a211-b24af72229b0",
"parent": 12,
"workflow": null,
"user": null,
"order": 1,
"name": "Regression Statistics"
}
},
{
"pk": 84,
"model": "workflows.abstractwidget",
"fields": {
"category": 17,
"treeview_image": "",
"name": "Mean Squared Error",
"is_streaming": false,
"uid": "c527ea0f-336b-4334-8288-bce4e742e043",
"interaction_view": "",
"image": "",
"package": "cforange",
"static_image": "",
"post_interact_action": "",
"user": null,
"visualization_view": "",
"action": "cforange_MSE",
"wsdl_method": "",
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"description": ""
}
},
{
"pk": 165,
"model": "workflows.abstractinput",
"fields": {
"widget": 84,
"name": "Results",
"short_name": "res",
"uid": "1f40bcbb-e919-4078-8f11-fc2462bba84b",
"default": "",
"required": false,
"multi": false,
"parameter_type": null,
"variable": "results",
"parameter": false,
"order": 1,
"description": ""
}
},
{
"pk": 94,
"model": "workflows.abstractoutput",
"fields": {
"widget": 84,
"name": "Mean Squared Error",
"short_name": "mse",
"variable": "MSE",
"uid": "b5a01b55-2f97-4401-8183-6f774b7f18cc",
"order": 1,
"description": ""
}
},
{
"pk": 86,
"model": "workflows.abstractwidget",
"fields": {
"category": 17,
"treeview_image": "",
"name": "Mean absolute error",
"is_streaming": false,
"uid": "feffec1f-247b-4740-9ddc-df549c5fbaf6",
"interaction_view": "",
"image": "",
"package": "cforange",
"static_image": "",
"post_interact_action": "",
"user": null,
"visualization_view": "",
"action": "cforange_MAE",
"wsdl_method": "",
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"description": ""
}
},
{
"pk": 167,
"model": "workflows.abstractinput",
"fields": {
"widget": 86,
"name": "Results",
"short_name": "res",
"uid": "b8b260d7-3497-42f5-93a0-09a411a03852",
"default": "",
"required": false,
"multi": false,
"parameter_type": null,
"variable": "results",
"parameter": false,
"order": 1,
"description": ""
}
},
{
"pk": 96,
"model": "workflows.abstractoutput",
"fields": {
"widget": 86,
"name": "Mean Absolute Error",
"short_name": "mse",
"variable": "MSE",
"uid": "bf09d357-d915-4ba6-b970-955bef6ee5c5",
"order": 1,
"description": ""
}
},
{
"pk": 89,
"model": "workflows.abstractwidget",
"fields": {
"category": 17,
"treeview_image": "",
"name": "R-squared",
"is_streaming": false,
"uid": "2299e615-4d72-4db7-9b32-3566b903c749",
"interaction_view": "",
"image": "",
"package": "cforange",
"static_image": "",
"post_interact_action": "",
"user": null,
"visualization_view": "",
"action": "cforange_R2",
"wsdl_method": "",
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"description": ""
}
},
{
"pk": 170,
"model": "workflows.abstractinput",
"fields": {
"widget": 89,
"name": "Results",
"short_name": "res",
"uid": "e06248e9-dd6d-4746-8972-1cbf6e6f116f",
"default": "",
"required": false,
"multi": false,
"parameter_type": null,
"variable": "results",
"parameter": false,
"order": 1,
"description": ""
}
},
{
"pk": 99,
"model": "workflows.abstractoutput",
"fields": {
"widget": 89,
"name": "R-Squared",
"short_name": "r2",
"variable": "R2",
"uid": "889fcdf7-7475-47f7-b1e0-0c199398f037",
"order": 1,
"description": ""
}
},
{
"pk": 87,
"model": "workflows.abstractwidget",
"fields": {
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"treeview_image": "",
"name": "Relative Squared Error",
"is_streaming": false,
"uid": "0d32c3a7-1f94-4414-92f0-bb81a9152636",
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"image": "",
"package": "cforange",
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"post_interact_action": "",
"user": null,
"visualization_view": "",
"action": "cforange_RSE",
"wsdl_method": "",
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"description": ""
}
},
{
"pk": 168,
"model": "workflows.abstractinput",
"fields": {
"widget": 87,
"name": "Results",
"short_name": "res",
"uid": "64ff64b7-5878-4c9f-8a43-908d3d9b187a",
"default": "",
"required": false,
"multi": false,
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"variable": "results",
"parameter": false,
"order": 1,
"description": ""
}
},
{
"pk": 97,
"model": "workflows.abstractoutput",
"fields": {
"widget": 87,
"name": "Relative Squared Error",
"short_name": "rse",
"variable": "RSE",
"uid": "66f3b71d-c53f-4653-9cb8-307b9eec70aa",
"order": 1,
"description": ""
}
},
{
"pk": 88,
"model": "workflows.abstractwidget",
"fields": {
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"treeview_image": "",
"name": "Root Relative Squared Error",
"is_streaming": false,
"uid": "c9ecd12e-eaff-41b6-b104-dff7fb60b124",
"interaction_view": "",
"image": "",
"package": "cforange",
"static_image": "",
"post_interact_action": "",
"user": null,
"visualization_view": "",
"action": "cforange_RRSE",
"wsdl_method": "",
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"description": ""
}
},
{
"pk": 169,
"model": "workflows.abstractinput",
"fields": {
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"name": "Results",
"short_name": "res",
"uid": "44ac11fb-201e-4d8f-b0b2-4d7668ea49a2",
"default": "",
"required": false,
"multi": false,
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"variable": "results",
"parameter": false,
"order": 1,
"description": ""
}
},
{
"pk": 98,
"model": "workflows.abstractoutput",
"fields": {
"widget": 88,
"name": "Root Relative Squared Error",
"short_name": "rrs",
"variable": "RRSE",
"uid": "aa8347f5-5243-4336-9c0e-2d9649fc5de2",
"order": 1,
"description": ""
}
},
{
"pk": 85,
"model": "workflows.abstractwidget",
"fields": {
"category": 17,
"treeview_image": "",
"name": "Root mean-squared error",
"is_streaming": false,
"uid": "dc66f264-14a6-491b-9c39-36bded8f95d0",
"interaction_view": "",
"image": "",
"package": "cforange",
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"post_interact_action": "",
"user": null,
"visualization_view": "",
"action": "cforange_RMSE",
"wsdl_method": "",
"wsdl": "",
"interactive": false,
"has_progress_bar": false,
"order": 1,
"description": ""
}
},
{
"pk": 166,
"model": "workflows.abstractinput",
"fields": {
"widget": 85,
"name": "Results",
"short_name": "res",
"uid": "588d6e48-4365-43e7-8388-09da5fe660ea",
"default": "",
"required": false,
"multi": false,
"parameter_type": null,
"variable": "results",
"parameter": false,
"order": 1,
"description": ""
}
},
{
"pk": 95,
"model": "workflows.abstractoutput",
"fields": {
"widget": 85,
"name": "Root Mean Squared Error",
"short_name": "rms",
"variable": "RMSE",
"uid": "9ff06017-8f60-4456-916a-6261222631db",
"order": 1,
"description": ""
}
},
{
"pk": 15,
"model": "workflows.category",
......@@ -2528,6 +2876,16 @@
"name": "Horse Colic"
}
},
{
"pk": 40,
"model": "workflows.abstractoption",
"fields": {
"uid": "1e765b4f-0c6f-43d6-a4c8-630faa528030",
"abstract_input": 122,
"value": "housing.tab",
"name": "Housing"
}
},
{
"pk": 20,
"model": "workflows.abstractoption",
......
......@@ -197,4 +197,74 @@ def cforange_auc(input_dict):
auc = orngStat.AUC(results,method)
output_dict = {}
output_dict['AUC']=auc
return output_dict
\ No newline at end of file
return output_dict
def cforange_MSE(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.MSE(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}
output_dict['MSE']=errors
return output_dict
def cforange_RMSE(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.RMSE(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}
output_dict['RMSE']=errors
return output_dict
def cforange_MAE(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.MAE(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}
output_dict['MAE']=errors
return output_dict
def cforange_RSE(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.RSE(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}
output_dict['RSE']=errors
return output_dict
def cforange_RRSE(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.RRSE(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}
output_dict['RRSE']=errors
return output_dict
def cforange_RAE(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.RAE(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}
output_dict['RAE']=errors
return output_dict
def cforange_R2(input_dict):
import orngStat
results = input_dict['results']
errors = orngStat.R2(results)
if len(errors)==1:
errors = errors[0]
output_dict = {}