o Ni@sdZddlmZddlmZddlmZddlZddlZddlmm Z ddl m Z dZdZgdZgd Zd Zed e jfd e jfd e jjedfde jjedfde jfde jfde jfde jfde jfde jfde jfde jfg ZGddde jjZdS)zForest fires dataset.)absolute_import)division)print_functionNa @misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } @article{cortez2007data, title={A data mining approach to predict forest fires using meteorological data}, author={Cortez, Paulo and Morais, Anibal de Jesus Raimundo}, year={2007}, publisher={Associa{\c{c}}{\~a}o Portuguesa para a Intelig{\^e}ncia Artificial (APPIA)} } a This is a regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data. Data Set Information: In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function. Then, several Data Mining methods were applied. After fitting the models, the outputs were post-processed with the inverse of the ln(x+1) transform. Four different input setups were used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: 12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The best RMSE was attained by the naive mean predictor. An analysis to the regression error curve (REC) shows that the SVM model predicts more examples within a lower admitted error. In effect, the SVM model predicts better small fires, which are the majority. Attribute Information: For more information, read [Cortez and Morais, 2007]. 1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9 2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9 3. month - month of the year: 'jan' to 'dec' 4. day - day of the week: 'mon' to 'sun' 5. FFMC - FFMC index from the FWI system: 18.7 to 96.20 6. DMC - DMC index from the FWI system: 1.1 to 291.3 7. DC - DC index from the FWI system: 7.9 to 860.6 8. ISI - ISI index from the FWI system: 0.0 to 56.10 9. temp - temperature in Celsius degrees: 2.2 to 33.30 10. RH - relative humidity in %: 15.0 to 100 11. wind - wind speed in km/h: 0.40 to 9.40 12. rain - outside rain in mm/m2 : 0.0 to 6.4 13. area - the burned area of the forest (in ha): 0.00 to 1090.84 (this output variable is very skewed towards 0.0, thus it may make sense to model with the logarithm transform). ) janfebmaraprmayjunjulaugsepoctnovdec)montuewedthufrisatsunzVhttps://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csvXYmonth)namesdayFFMCDMCDCISItempRHwindrainc@s4eZdZdZejdZddZddZ ddZ d S) ForestFireszARegression task aimed to predict the burned area of forest fires.z0.0.1c Cs6tjj|ttjtjddt Ddddt dS)NcSi|]\}}||qSr').0namedtyper'r'_/home/ubuntu/.local/lib/python3.10/site-packages/tensorflow_datasets/structured/forest_fires.py z%ForestFires._info..areafeaturesz4https://archive.ics.uci.edu/ml/datasets/Forest+Fires)builder descriptionr0supervised_keyshomepagecitation) tfdscore DatasetInfo _DESCRIPTIONr0 FeaturesDicttffloat32FEATURESitems _CITATION)selfr'r'r+_info}szForestFires._infocCs$|t}tjjtjjd|idgS)zReturns SplitGenerators. file_path)r) gen_kwargs)download_URLr6r7SplitGeneratorSplitTRAIN)r@ dl_managerdatar'r'r+_split_generatorss zForestFires._split_generatorsccsvtjj|)}t|}t|D]\}}||ddd|DdfVqWddS1s4wYdS)zYields examples.r/cSr&r'r')r(r)valuer'r'r+r,r-z2ForestFires._generate_examples..r.N) r;iogfileGFilecsv DictReader enumeratepopr>)r@rBfraw_datairowr'r'r+_generate_exampless  "zForestFires._generate_examplesN) __name__ __module__ __qualname____doc__r6r7VersionVERSIONrArKrXr'r'r'r+r%xs   r%)r\ __future__rrr collectionsrPtensorflow.compat.v2compatv2r;tensorflow_datasets.public_api public_apir6r?r9_MONTHS_DAYSrE OrderedDictuint8r0 ClassLabelr<r=r7GeneratorBasedBuilderr%r'r'r'r+s8    3