<jupyter_start><jupyter_text>Linear Regression accuracy plots with error bars.<jupyter_code>attribTypes = ['sparse','non_sparse','non_zero','abv_mean']
rgrAlgoTypes = ['linear','ridge','lasso']

embedCodes = []
for attrib in attribTypes:
    code = []
    for alg in rgrAlgoTypes:
        rgrObj, arr,arr_min = runRgr(alg,attrib)
        title = "%s regression results using %s attributes" %(alg,attrib)
        flNm = "%s_regession_%s_attributes_%s" %(alg,attrib,str(True))
        a = plot(rgrObj,arr,arr_min,title,flNm,[-100,200],errorBar = True)
        code.append(a.embed_code)    
    embedCodes.append(code)<jupyter_output><empty_output><jupyter_text>Linear Regression plots without error bars<jupyter_code>attribTypes = ['sparse','non_sparse','non_zero','abv_mean']
rgrAlgoTypes = ['linear','ridge','lasso']

embedCodes = []
for attrib in attribTypes:
    code = []
    for alg in rgrAlgoTypes:
        rgrObj, _, _ = runRgr(alg,attrib)
        title = "%s regression results using %s attributes" %(alg,attrib)
        flNm = "%s_regession_%s_attributes_%s" %(alg,attrib,str(False))
        a = plot(rgrObj,errs,[],title,flNm,[-10,110],errorBar = False)
        code.append(a.embed_code)
    embedCodes.append(code)
for c in embedCodes[3]:
    print(c)
rgrObj, arr,arr_min = runRgr('dtree_regressor','non_sparse')
title = "%s regression results using %s attributes" %(alg,attrib)
flNm = "%s_regession_%s_attributes_%s" %(alg,attrib,str(True))
a = plot(rgrObj,arr,arr_min,title,flNm,[-100,200],errorBar = True)
attribTypes = ['sparse','non_sparse','non_zero','abv_mean']
rgrAlgoTypes = ['linear_svr','svr','dtree_regressor']

embedCodes = []
for attrib in attribTypes:
    code = []
    for alg in rgrAlgoTypes:
        rgrObj, _, _ = runRgr(alg,attrib)
        print("Absolute error for %s using %s : %f" %(alg,attrib,rgrObj.abserr))
        print("Mean Squared error for %s using %s : %f" %(alg,attrib,rgrObj.sqerr))
        title = "%s regression results using %s attributes" %(alg,attrib)
        flNm = "%s_regession_%s_attributes_%s" %(alg,attrib,str(False))
        a = plot(rgrObj,[],[],title,flNm,[-10,110],errorBar = False)
        code.append(a.embed_code)
    embedCodes.append(code)
len(embedCodes)
attribTypes = ['sparse','non_sparse','non_zero','abv_mean']
rgrAlgoTypes = ['linear_svr','svr','dtree_regressor']

embedCodes = []
for attrib in attribTypes:
    code = []
    for alg in rgrAlgoTypes:
        rgrObj, arr,arr_min = runRgr(alg,attrib)
        title = "%s regression results using %s attributes" %(alg,attrib)
        flNm = "%s_regession_%s_attributes_%s" %(alg,attrib,str(True))
        a = plot(rgrObj,arr,arr_min,title,flNm,[-100,200],errorBar = True)
        code.append(a.embed_code)    
    embedCodes.append(code)
import PopulationEstimatorFromClf as PE
import importlib
importlib.reload(PE)
import ClassiferHelperAPI as CH
train_data_fl = "../FinalResults/ImgShrRnkListWithTags.csv"
test_data_fl = "../data/full_gid_aid_ftr_agg.csv"
methodName = "linear"
attribType = "non_sparse"
infoGainFl=None
regrArgs=kwargsDict
obj, pred_results = PE.trainTestRgrs(train_data_fl,
                test_data_fl,
                methodName,
                attribType,
                 None,
                regrArgs=kwargsDict)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14359598418010344, 'fraction_numerical': 0.008822634621235169, 'mean_word_length': 4.286173633440514, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '418979', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1244, 'n_tokens_neox': 1212, 'n_words': 209, 'path': 'Notebooks/.ipynb_checkpoints/ImageSharebilityPredictorsNB-checkpoint.ipynb', 'repo_name': 'smenon8/AnimalPhotoBias', 'size': '3287'}