some experiments run, not much to say though
This commit is contained in:
parent
7ee224521a
commit
1f3b1597dc
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import numpy as np
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from sklearn.linear_model import LogisticRegressionCV
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from quapy.data import LabelledCollection
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
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from commons import *
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from table import Table
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from tqdm import tqdm
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import quapy as qp
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np.set_printoptions(linewidth=np.inf)
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def classifier():
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return LogisticRegressionCV()
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def quantifiers():
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cls = classifier()
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yield 'MLPE', MLPE()
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yield 'CC', CC(cls)
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yield 'PCC', PCC(cls)
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yield 'ACC', ACC(cls)
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yield 'PACC', PACC(cls)
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yield 'SLD', EMQ(cls)
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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data = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(data)
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Madj = AdjMatrix('./data/matrice_adiacenza.csv')
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areas = [Ai for Ai, _, _ in data]
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q_names = [q_name for q_name, _ in quantifiers()]
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# tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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# areas on which a quantifier is trained, e.g., 'PACC-w/o46' means a PACC quantifier
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# has been trained on all areas but 46
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methods = [f'{q_name}-cat' for q_name in q_names]
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table = Table(name='adjacentconcat', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.sta = False
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table.format.remove_zero = True
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for q_name, q in quantifiers():
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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#training
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trainings = [LabelledCollection(Xj, yj) for Aj, Xj, yj in data if Aj!=Ai and Aj in Madj.get_adjacent(Ai)]
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print(f'for test Ai={Ai} there should be {Madj.get_adjacent(Ai)}: len={len(trainings)}')
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tr = LabelledCollection.join(*trainings)
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q.fit(tr)
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#test
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te = LabelledCollection(Xi, yi)
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qp.environ["SAMPLE_SIZE"] = len(te)
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pred_prev = q.quantify(te.X)
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true_prev = te.prevalence()
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err = qp.error.mae(true_prev, pred_prev)
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method_name = f'{q_name}-cat'
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table.add(benchmark=f'te-{Ai}', method=method_name, v=err)
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# text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
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Table.LatexPDF(f'./results/adjacentconcat/doc.pdf', [table])
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# with open(f'./results/classifier/output.txt', 'tw') as foo:
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# foo.write('\n'.join(text_outputs))
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import numpy as np
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from sklearn.linear_model import LogisticRegressionCV
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from Census.methods import AreaQuantifier, AggregationRule
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from quapy.data import LabelledCollection
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ, MS, MS2
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from commons import *
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from table import Table
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from tqdm import tqdm
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import quapy as qp
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from copy import deepcopy
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np.set_printoptions(linewidth=np.inf)
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def classifier():
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return LogisticRegressionCV()
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def quantifiers():
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cls = classifier()
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yield 'MLPE', MLPE()
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yield 'CC', CC(cls)
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yield 'PCC', PCC(cls)
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yield 'ACC', ACC(cls)
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yield 'PACC', PACC(cls)
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yield 'MS', MS(cls)
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# yield 'MS2', MS2(cls)
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# yield 'SLD', EMQ(cls)
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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data = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(data)
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areas = [Ai for Ai, _, _ in data]
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q_names = [q_name for q_name, _ in quantifiers()]
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Madj = AdjMatrix('./data/matrice_adiacenza.csv')
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tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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for aggr in ['median', 'mean']:
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# areas on which a quantifier is trained, e.g., 'PACC-w/o46' means a PACC quantifier
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# has been trained on all areas but 46
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methods = [f'{q_name}-{aggr}' for q_name in q_names]
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table = Table(name=f'adjacent{aggr}', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.sta = False
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table.format.remove_zero = True
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for q_name, q in quantifiers():
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# pretrain quantifiers per area
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pretrained_area_q = []
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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q_i = deepcopy(q)
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q_i.fit(LabelledCollection(Xi, yi))
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pretrained_area_q.append(AreaQuantifier(Ai, q_i))
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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# compose members of the rule (quantifiers are already fit)
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#training
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area_quantifiers = [qA_j for qA_j in pretrained_area_q if qA_j.area != Ai]
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rule = AggregationRule(area_quantifiers, adjacent_matrix=Madj, aggr=aggr)
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#test
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te = LabelledCollection(Xi, yi)
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qp.environ["SAMPLE_SIZE"] = len(te)
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pred_prev = rule.predict(Ai, te.X)
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true_prev = te.prevalence()
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err = qp.error.mae(true_prev, pred_prev)
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method_name = f'{q_name}-{aggr}'
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table.add(benchmark=f'te-{Ai}', method=method_name, v=err)
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# text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
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tables.append(table)
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Table.LatexPDF(f'./results/adjacentaggregation/doc.pdf', tables)
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# with open(f'./results/classifier/output.txt', 'tw') as foo:
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# foo.write('\n'.join(text_outputs))
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import numpy as np
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from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
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from Census.methods import AreaQuantifier, AggregationRule, optimize_ensemble
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from quapy.data import LabelledCollection
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ, MS, MS2
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from commons import *
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from table import Table
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from tqdm import tqdm
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import quapy as qp
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from copy import deepcopy
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np.set_printoptions(linewidth=np.inf)
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def classifier():
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return LogisticRegression()
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def quantifiers():
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cls = classifier()
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# yield 'MLPE', MLPE()
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yield 'CC', CC(cls)
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yield 'PCC', PCC(cls)
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yield 'ACC', ACC(cls)
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yield 'PACC', PACC(cls)
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yield 'MS', MS(cls)
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# yield 'MS2', MS2(cls)
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# yield 'SLD', EMQ(cls)
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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data = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(data)
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areas = [Ai for Ai, _, _ in data]
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q_names = [q_name for q_name, _ in quantifiers()]
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Madj = AdjMatrix('./data/matrice_adiacenza.csv')
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tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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for aggr in ['median', 'mean']:
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# areas on which a quantifier is trained, e.g., 'PACC-w/o46' means a PACC quantifier
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# has been trained on all areas but 46
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methods = [f'{q_name}-{aggr}' for q_name in q_names]
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table = Table(name=f'adjacent{aggr}optim', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.sta = False
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table.format.remove_zero = True
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for q_name, q in quantifiers():
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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# compose members of the rule (quantifiers are optimized wrt the rest of the areas)
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#training
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other_area = [(Aj, Xj, yj) for Aj, Xj, yj in data if Aj != Ai]
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area_quantifiers = optimize_ensemble(other_area, q, Madj)
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rule = AggregationRule(area_quantifiers, adjacent_matrix=Madj, aggr=aggr)
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#test
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te = LabelledCollection(Xi, yi)
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qp.environ["SAMPLE_SIZE"] = len(te)
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pred_prev = rule.predict(Ai, te.X)
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true_prev = te.prevalence()
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err = qp.error.mae(true_prev, pred_prev)
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method_name = f'{q_name}-{aggr}'
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table.add(benchmark=f'te-{Ai}', method=method_name, v=err)
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# text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
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tables.append(table)
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Table.LatexPDF(f'./results/adjacentaggregationoptim/doc.pdf', tables)
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# with open(f'./results/classifier/output.txt', 'tw') as foo:
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# foo.write('\n'.join(text_outputs))
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@ -0,0 +1,84 @@
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import numpy as np
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from sklearn.linear_model import LogisticRegressionCV
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from quapy.data import LabelledCollection
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
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from commons import *
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from table import Table
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from tqdm import tqdm
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import quapy as qp
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np.set_printoptions(linewidth=np.inf)
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def classifier():
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return LogisticRegressionCV()
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def quantifiers():
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cls = classifier()
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yield 'MLPE', MLPE()
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yield 'CC', CC(cls)
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yield 'PCC', PCC(cls)
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yield 'ACC', ACC(cls)
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yield 'PACC', PACC(cls)
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yield 'SLD', EMQ(cls)
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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data = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(data)
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areas = [Ai for Ai, _, _ in data]
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q_names = [q_name for q_name, _ in quantifiers()]
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# tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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# areas on which a quantifier is trained, e.g., 'PACC-w/o46' means a PACC quantifier
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# has been trained on all areas but 46
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methods = [f'{q_name}-cat' for q_name in q_names]
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table = Table(name='allconcat', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.sta = False
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table.format.remove_zero = True
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for q_name, q in quantifiers():
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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#training
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trainings = [LabelledCollection(Xj, yj) for Aj, Xj, yj in data if Aj!=Ai]
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tr = LabelledCollection.join(*trainings)
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q.fit(tr)
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#test
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te = LabelledCollection(Xi, yi)
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qp.environ["SAMPLE_SIZE"] = len(te)
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pred_prev = q.quantify(te.X)
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true_prev = te.prevalence()
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err = qp.error.mae(true_prev, pred_prev)
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method_name = f'{q_name}-cat'
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table.add(benchmark=f'te-{Ai}', method=method_name, v=err)
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# text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
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Table.LatexPDF(f'./results/allconcat/doc.pdf', [table])
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# with open(f'./results/classifier/output.txt', 'tw') as foo:
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# foo.write('\n'.join(text_outputs))
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@ -0,0 +1,96 @@
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import numpy as np
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from sklearn.linear_model import LogisticRegressionCV
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from Census.methods import AreaQuantifier, AggregationRule
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from quapy.data import LabelledCollection
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from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
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from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
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from commons import *
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from table import Table
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from tqdm import tqdm
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import quapy as qp
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from copy import deepcopy
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np.set_printoptions(linewidth=np.inf)
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def classifier():
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return LogisticRegressionCV()
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def quantifiers():
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cls = classifier()
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yield 'MLPE', MLPE()
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yield 'CC', CC(cls)
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yield 'PCC', PCC(cls)
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yield 'ACC', ACC(cls)
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yield 'PACC', PACC(cls)
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yield 'SLD', EMQ(cls)
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survey_y = './data/survey_y.csv'
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Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
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preprocessor = Preprocessor()
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Xtr = preprocessor.fit_transform(Xtr)
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data = get_dataset_by_area(Atr, Xtr, ytr)
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n_areas = len(data)
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areas = [Ai for Ai, _, _ in data]
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q_names = [q_name for q_name, _ in quantifiers()]
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tables = []
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text_outputs = []
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benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
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for aggr in ['median', 'mean']:
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# areas on which a quantifier is trained, e.g., 'PACC-w/o46' means a PACC quantifier
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# has been trained on all areas but 46
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methods = [f'{q_name}-{aggr}' for q_name in q_names]
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table = Table(name=f'all{aggr}', benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local')
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table.format.mean_prec = 4
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table.format.show_std = False
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table.format.sta = False
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table.format.remove_zero = True
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for q_name, q in quantifiers():
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# pretrain quantifiers per area
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pretrained_area_q = []
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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q_i = deepcopy(q)
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q_i.fit(LabelledCollection(Xi, yi))
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pretrained_area_q.append(AreaQuantifier(Ai, q_i))
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for i, (Ai, Xi, yi) in tqdm(enumerate(data), total=n_areas):
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# compose members of the rule (quantifiers are already fit)
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#training
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area_quantifiers = [qA_j for qA_j in pretrained_area_q if qA_j.area != Ai]
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rule = AggregationRule(area_quantifiers, aggr=aggr)
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#test
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te = LabelledCollection(Xi, yi)
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qp.environ["SAMPLE_SIZE"] = len(te)
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pred_prev = rule.predict(Ai, te.X)
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true_prev = te.prevalence()
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err = qp.error.mae(true_prev, pred_prev)
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method_name = f'{q_name}-{aggr}'
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table.add(benchmark=f'te-{Ai}', method=method_name, v=err)
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|
||||
# text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
|
||||
|
||||
tables.append(table)
|
||||
|
||||
Table.LatexPDF(f'./results/allaggregation/doc.pdf', tables)
|
||||
|
||||
# with open(f'./results/classifier/output.txt', 'tw') as foo:
|
||||
# foo.write('\n'.join(text_outputs))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
import numpy as np
|
||||
from sklearn.svm import SVC
|
||||
|
||||
from commons import *
|
||||
from table import Table
|
||||
|
||||
|
||||
np.set_printoptions(linewidth=np.inf)
|
||||
|
||||
def classifiers():
|
||||
yield 'LR-opt', LogisticRegressionCV(class_weight='balanced', Cs=10)
|
||||
yield 'LR-def', LogisticRegressionCV()
|
||||
yield 'SVM-linear', LinearSVC()
|
||||
yield 'SVM-rbf', SVC(kernel='rbf')
|
||||
|
||||
|
||||
survey_y = './data/survey_y.csv'
|
||||
|
||||
Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
|
||||
|
||||
preprocessor = Preprocessor()
|
||||
Xtr = preprocessor.fit_transform(Xtr)
|
||||
|
||||
trains = get_dataset_by_area(Atr, Xtr, ytr)
|
||||
n_areas = len(trains)
|
||||
|
||||
areas = [Ai for Ai, _, _ in trains]
|
||||
|
||||
tables = []
|
||||
text_outputs = []
|
||||
|
||||
benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
|
||||
methods = [f'tr-{Ai}' for Ai in areas] # areas on which a quantifier is trained
|
||||
|
||||
for cls_name, c in classifiers():
|
||||
|
||||
table = Table(name=cls_name, benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='local', lower_is_better=False)
|
||||
table.format.mean_prec = 4
|
||||
table.format.show_std = False
|
||||
table.format.sta = False
|
||||
table.format.remove_zero = True
|
||||
|
||||
for i, (Ai, Xi, yi) in tqdm(enumerate(trains), total=n_areas):
|
||||
c.fit(Xi, yi)
|
||||
for j, (Aj, Xj, yj) in enumerate(trains):
|
||||
if i==j: continue
|
||||
pred_labels = c.predict(Xj)
|
||||
true_labels = yj
|
||||
acc = (pred_labels==true_labels).mean()
|
||||
table.add(benchmark=f'te-{Aj}', method=f'tr-{Ai}', v=acc)
|
||||
|
||||
for test in benchmarks:
|
||||
values = table.get_benchmark_values(test)
|
||||
table.add(benchmark=test, method='Best', v=max(values))
|
||||
table.add(benchmark=test, method='Worst', v=min(values))
|
||||
table.add(benchmark=test, method='AVE', v=np.mean(values))
|
||||
|
||||
tables.append(table)
|
||||
|
||||
text_outputs.append(f'{cls_name} got mean {table.all_mean():.5f}')
|
||||
|
||||
|
||||
Table.LatexPDF(f'./results/classifier/doc.pdf', tables)
|
||||
with open(f'./results/classifier/output.txt', 'tw') as foo:
|
||||
foo.write('\n'.join(text_outputs))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
np.set_printoptions(linewidth=np.inf)
|
||||
|
||||
|
||||
def load_csv(file, use_yhat=True):
|
||||
df = pd.read_csv(file)
|
||||
|
||||
cod_area = 'cod.prov'
|
||||
if use_yhat:
|
||||
covariates = ['owner', 'eta', 'work', 'sex', 'year_edu', 'hsize', 'y.hat', 'prob']
|
||||
else:
|
||||
covariates = ['owner', 'eta', 'work', 'sex', 'year_edu', 'hsize', 'prob']
|
||||
y_true = 'y.true'
|
||||
|
||||
X = df[covariates].values
|
||||
A = df[cod_area].values
|
||||
|
||||
# for i, cov in enumerate(covariates):
|
||||
# print(f'values of col {i} "{cov}" {np.unique(X[:,i])}')
|
||||
|
||||
if y_true in df.columns:
|
||||
y = df[y_true].values
|
||||
return A, X, y
|
||||
else:
|
||||
return A, X
|
||||
|
||||
|
||||
def get_dataset_by_area(A, X, y=None):
|
||||
data = []
|
||||
for area in np.unique(A):
|
||||
sel = (A == area)
|
||||
Xsel = X[sel]
|
||||
if y is not None:
|
||||
ysel = y[sel]
|
||||
else:
|
||||
ysel = None
|
||||
data.append((area, Xsel, ysel))
|
||||
return data
|
||||
|
||||
|
||||
class AdjMatrix:
|
||||
|
||||
def __init__(self, path):
|
||||
df = pd.read_csv(path)
|
||||
|
||||
area_codes = df.columns[1:].values
|
||||
area_codes = np.asarray([int(c) for c in area_codes])
|
||||
|
||||
values = df.values[:, 1:]
|
||||
print(area_codes)
|
||||
print(values)
|
||||
self.area2idx = {area:i for i, area in enumerate(area_codes)}
|
||||
self.idx2area = area_codes
|
||||
self.M = np.asarray(values)
|
||||
|
||||
def adjacent(self, cod_1, cod_2):
|
||||
idx1 = self.area2idx[cod_1]
|
||||
idx2 = self.area2idx[cod_2]
|
||||
return (self.M[idx1, idx2] == 1)
|
||||
|
||||
def get_adjacent(self, cod):
|
||||
idx = self.area2idx[cod]
|
||||
idx_adj = np.argwhere(self.M[idx]==1).flatten()
|
||||
return self.idx2area[idx_adj]
|
||||
|
||||
|
||||
class Preprocessor:
|
||||
def __init__(self):
|
||||
self.scaler = StandardScaler()
|
||||
# self.standardize_col_ids = np.asarray([1, 4, 5]) # eta, year_edu, hsize
|
||||
self.standardize_col_ids = np.arange(8) # everything
|
||||
|
||||
def fit(self, X, y=None):
|
||||
Xsel = X[:, self.standardize_col_ids]
|
||||
self.scaler.fit(Xsel)
|
||||
return self
|
||||
|
||||
def transform(self, X):
|
||||
Xsel = X[:, self.standardize_col_ids]
|
||||
Xsel_zscore = self.scaler.transform(Xsel)
|
||||
X[:, self.standardize_col_ids] = Xsel_zscore
|
||||
return X
|
||||
|
||||
def fit_transform(self, X, y=None):
|
||||
return self.fit(X, y).transform(X)
|
||||
|
||||
|
||||
|
|
@ -9,6 +9,7 @@ from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation a
|
|||
from quapy.method.aggregative import EMQ, PACC, CC, PCC, MS2, MS, ACC
|
||||
from quapy.data import LabelledCollection
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from commons import *
|
||||
|
||||
np.set_printoptions(linewidth=np.inf)
|
||||
|
||||
|
|
@ -16,67 +17,6 @@ np.set_printoptions(linewidth=np.inf)
|
|||
cens_y = './data/cens_y.csv'
|
||||
survey_y = './data/survey_y.csv'
|
||||
|
||||
|
||||
def load_csv(file, use_yhat=True):
|
||||
df = pd.read_csv(file)
|
||||
|
||||
cod_area = 'cod.prov'
|
||||
if use_yhat:
|
||||
covariates = ['owner', 'eta', 'work', 'sex', 'year_edu', 'hsize', 'y.hat', 'prob']
|
||||
else:
|
||||
covariates = ['owner', 'eta', 'work', 'sex', 'year_edu', 'hsize', 'prob']
|
||||
y_true = 'y.true'
|
||||
|
||||
X = df[covariates].values
|
||||
A = df[cod_area].values
|
||||
|
||||
for i, cov in enumerate(covariates):
|
||||
print(f'values of col {i} "{cov}" {np.unique(X[:,i])}')
|
||||
|
||||
if y_true in df.columns:
|
||||
y = df[y_true].values
|
||||
return A, X, y
|
||||
else:
|
||||
return A, X
|
||||
|
||||
|
||||
def get_dataset_by_area(A, X, y=None):
|
||||
lc = []
|
||||
for area in np.unique(A):
|
||||
sel = (A == area)
|
||||
Xsel = X[sel]
|
||||
if y is not None:
|
||||
ysel = y[sel]
|
||||
else:
|
||||
ysel = None
|
||||
lc.append((area, Xsel, ysel))
|
||||
return lc
|
||||
|
||||
|
||||
class Preprocessor:
|
||||
def __init__(self):
|
||||
self.scaler = StandardScaler()
|
||||
# self.standardize_col_ids = np.asarray([1, 4, 5]) # eta, year_edu, hsize
|
||||
self.standardize_col_ids = np.arange(8) # everything
|
||||
|
||||
def fit(self, X, y=None):
|
||||
Xsel = X[:, self.standardize_col_ids]
|
||||
self.scaler.fit(Xsel)
|
||||
return self
|
||||
|
||||
def transform(self, X):
|
||||
Xsel = X[:, self.standardize_col_ids]
|
||||
Xsel_zscore = self.scaler.transform(Xsel)
|
||||
X[:, self.standardize_col_ids] = Xsel_zscore
|
||||
return X
|
||||
|
||||
def fit_transform(self, X, y=None):
|
||||
return self.fit(X, y).transform(X)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Ate, Xte = load_csv(cens_y)
|
||||
Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,111 @@
|
|||
from abc import abstractmethod, ABC
|
||||
from copy import deepcopy
|
||||
from typing import List, Iterable
|
||||
|
||||
import numpy as np
|
||||
|
||||
import quapy as qp
|
||||
from quapy.method.aggregative import AggregativeQuantifier
|
||||
from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.base import BaseQuantifier
|
||||
|
||||
|
||||
class AreaQuantifier:
|
||||
def __init__(self, area:int, quantifier: BaseQuantifier):
|
||||
self.area = area
|
||||
self.quantifier = quantifier
|
||||
|
||||
def quantify(self, X):
|
||||
return self.quantifier.quantify(X)
|
||||
|
||||
|
||||
class CombinationRule(ABC):
|
||||
|
||||
def __init__(self, area_quantifiers: List[AreaQuantifier]):
|
||||
self.area_quantifiers = area_quantifiers
|
||||
|
||||
@abstractmethod
|
||||
def select_quantifiers(self, area:int, X):
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def combination(self, choice, X):
|
||||
...
|
||||
|
||||
def predict(self, area:int, X):
|
||||
choice = self.select_quantifiers(area, X)
|
||||
prevalence = self.combination(choice, X)
|
||||
return prevalence
|
||||
|
||||
|
||||
|
||||
def optimize_ensemble(area_data: Iterable, q: BaseQuantifier, Madj=None, hyper=None, error='mae'):
|
||||
if hyper is None:
|
||||
hyper = {
|
||||
'classifier__C': np.logspace(-4, 4, 9),
|
||||
'classifier__class_weight': ['balanced', None]
|
||||
}
|
||||
|
||||
labelled_collections = [(A, LabelledCollection(X, y)) for A, X, y in area_data]
|
||||
|
||||
area_quantifiers = []
|
||||
for A, lc in labelled_collections:
|
||||
if Madj is None:
|
||||
rest = [lc_j for Aj, lc_j in labelled_collections if Aj != A]
|
||||
else:
|
||||
rest = [lc_j for Aj, lc_j in labelled_collections if Aj != A and Aj in Madj.get_adjacent(A)]
|
||||
q = optim(q, lc, rest, hyper, error)
|
||||
area_quantifiers.append(AreaQuantifier(A, q))
|
||||
|
||||
return area_quantifiers
|
||||
|
||||
|
||||
class AggregationRule(CombinationRule):
|
||||
|
||||
def __init__(self, area_quantifiers: List[AreaQuantifier], adjacent_matrix: 'AdjMatrix' = None, aggr='median'):
|
||||
assert aggr in ['mean', 'median'], f'unknown {aggr=}'
|
||||
self.area_quantifiers = area_quantifiers
|
||||
self.adjacent_matrix = adjacent_matrix
|
||||
self.aggr = aggr
|
||||
|
||||
def select_quantifiers(self, area:int, X):
|
||||
if self.adjacent_matrix is None:
|
||||
chosen = self.area_quantifiers
|
||||
else:
|
||||
adjacent = self.adjacent_matrix.get_adjacent(area)
|
||||
chosen = [q_i for q_i in self.area_quantifiers if q_i.area in adjacent]
|
||||
return chosen
|
||||
|
||||
def combination(self, choice, X):
|
||||
prevs = np.asarray([q.quantify(X) for q in choice])
|
||||
if self.aggr == 'median':
|
||||
prev = np.median(prevs, axis=0)
|
||||
elif self.aggr == 'mean':
|
||||
prev = np.mean(prevs, axis=0)
|
||||
else:
|
||||
raise NotImplementedError(f'{self.aggr=} not implemented')
|
||||
return prev
|
||||
|
||||
|
||||
def optim(q: BaseQuantifier, train: LabelledCollection, labelled_collections: Iterable[LabelledCollection], hyper:dict, error='mae'):
|
||||
q = deepcopy(q)
|
||||
|
||||
prot = qp.protocol.IterateProtocol(labelled_collections)
|
||||
try:
|
||||
mod_sel = qp.model_selection.GridSearchQ(
|
||||
model=q,
|
||||
param_grid=hyper,
|
||||
protocol=prot,
|
||||
error=error,
|
||||
refit=False,
|
||||
n_jobs=-1
|
||||
).fit(train)
|
||||
|
||||
fitted = mod_sel.best_model_
|
||||
except ValueError:
|
||||
print(f'method {q} failed; training without model selection')
|
||||
fitted = q.fit(train)
|
||||
|
||||
return fitted
|
||||
|
||||
|
|
@ -0,0 +1,86 @@
|
|||
import numpy as np
|
||||
from sklearn.linear_model import LogisticRegressionCV
|
||||
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as MLPE
|
||||
from quapy.method.aggregative import CC, PCC, ACC, PACC, EMQ
|
||||
from commons import *
|
||||
from table import Table
|
||||
from tqdm import tqdm
|
||||
import quapy as qp
|
||||
|
||||
np.set_printoptions(linewidth=np.inf)
|
||||
|
||||
def classifier():
|
||||
#return LogisticRegressionCV(class_weight='balanced', Cs=10)
|
||||
return LogisticRegressionCV()
|
||||
|
||||
def quantifiers():
|
||||
cls = classifier()
|
||||
yield 'MLPE', MLPE()
|
||||
yield 'CC', CC(cls)
|
||||
yield 'PCC', PCC(cls)
|
||||
yield 'ACC', ACC(cls)
|
||||
yield 'PACC', PACC(cls)
|
||||
|
||||
|
||||
survey_y = './data/survey_y.csv'
|
||||
|
||||
Atr, Xtr, ytr = load_csv(survey_y, use_yhat=True)
|
||||
|
||||
preprocessor = Preprocessor()
|
||||
Xtr = preprocessor.fit_transform(Xtr)
|
||||
|
||||
trains = get_dataset_by_area(Atr, Xtr, ytr)
|
||||
n_areas = len(trains)
|
||||
|
||||
areas = [Ai for Ai, _, _ in trains]
|
||||
|
||||
tables = []
|
||||
text_outputs = []
|
||||
|
||||
benchmarks = [f'te-{Ai}' for Ai in areas] # areas used as test
|
||||
methods = [f'tr-{Ai}' for Ai in areas] # areas on which a quantifier is trained
|
||||
|
||||
|
||||
for q_name, q in quantifiers():
|
||||
|
||||
table = Table(name=q_name, benchmarks=benchmarks, methods=methods, stat_test=None, color_mode='global')
|
||||
table.format.mean_prec = 4
|
||||
table.format.show_std = False
|
||||
table.format.sta = False
|
||||
table.format.remove_zero = True
|
||||
table.with_mean = True
|
||||
|
||||
for i, (Ai, Xi, yi) in tqdm(enumerate(trains), total=n_areas):
|
||||
tr = LabelledCollection(Xi, yi)
|
||||
q.fit(tr)
|
||||
len_tr = len(tr)
|
||||
for j, (Aj, Xj, yj) in enumerate(trains):
|
||||
if i==j: continue
|
||||
te = LabelledCollection(Xj, yj)
|
||||
qp.environ["SAMPLE_SIZE"] = len(te)
|
||||
pred_prev = q.quantify(te.X)
|
||||
true_prev = te.prevalence()
|
||||
# err = qp.error.mrae(true_prev, pred_prev)
|
||||
err = qp.error.mae(true_prev, pred_prev)
|
||||
table.add(benchmark=f'te-{Aj}', method=f'tr-{Ai}', v=err)
|
||||
|
||||
for test in benchmarks:
|
||||
values = table.get_benchmark_values(test)
|
||||
table.add(benchmark=test, method='Best', v=min(values))
|
||||
|
||||
tables.append(table)
|
||||
|
||||
text_outputs.append(f'{q_name} got mean {table.all_mean():.5f}, best mean {table.get_method_values("Best").mean():.5f}')
|
||||
|
||||
|
||||
Table.LatexPDF(f'./results/pairwise/doc.pdf', tables)
|
||||
|
||||
with open(f'./results/classifier/output.txt', 'tw') as foo:
|
||||
foo.write('\n'.join(text_outputs))
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,476 @@
|
|||
import numpy as np
|
||||
from typing import Union, List
|
||||
from collections.abc import Iterable
|
||||
from dataclasses import dataclass
|
||||
from scipy.stats import wilcoxon, ttest_ind_from_stats
|
||||
import pandas as pd
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
@dataclass
|
||||
class CellFormat:
|
||||
mean_prec: int = 3
|
||||
std_prec: int = 3
|
||||
show_std: bool = True
|
||||
remove_zero: bool = False
|
||||
color: bool = True
|
||||
maxtone: int = 50
|
||||
|
||||
|
||||
class Cell:
|
||||
|
||||
def __init__(self, format: CellFormat, group: 'CellGroup'):
|
||||
self.values = []
|
||||
self.format = format
|
||||
self.touch()
|
||||
self.group = group
|
||||
self.group.register_cell(self)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.values)
|
||||
|
||||
def mean(self):
|
||||
if self.mean_ is None:
|
||||
self.mean_ = np.mean(self.values)
|
||||
return self.mean_
|
||||
|
||||
def std(self):
|
||||
if self.std_ is None:
|
||||
self.std_ = np.std(self.values)
|
||||
return self.std_
|
||||
|
||||
def touch(self):
|
||||
self.mean_ = None
|
||||
self.std_ = None
|
||||
|
||||
def append(self, v: Union[float,Iterable]):
|
||||
if isinstance(v, Iterable):
|
||||
self.values.extend(v)
|
||||
self.values.append(v)
|
||||
self.touch()
|
||||
|
||||
def isEmpty(self):
|
||||
return len(self)==0
|
||||
|
||||
def isBest(self):
|
||||
best = self.group.best()
|
||||
if best is not None:
|
||||
return (best == self) or (np.isclose(best.mean(), self.mean()))
|
||||
return False
|
||||
|
||||
def print_mean(self):
|
||||
if self.isEmpty():
|
||||
return ''
|
||||
else:
|
||||
return f'{self.mean():.{self.format.mean_prec}f}'
|
||||
|
||||
def print(self):
|
||||
if self.isEmpty():
|
||||
return ''
|
||||
|
||||
# mean
|
||||
# ---------------------------------------------------
|
||||
mean = self.print_mean()
|
||||
if self.format.remove_zero:
|
||||
mean = mean.replace('0.', '.')
|
||||
|
||||
# std ?
|
||||
# ---------------------------------------------------
|
||||
if self.format.show_std:
|
||||
std = f' $\pm$ {self.std():.{self.format.std_prec}f}'
|
||||
else:
|
||||
std = ''
|
||||
|
||||
# bold or statistical test
|
||||
# ---------------------------------------------------
|
||||
if self.isBest():
|
||||
str_cell = f'\\textbf{{{mean}{std}}}'
|
||||
else:
|
||||
comp_symbol = ''
|
||||
pval = self.group.compare(self)
|
||||
if pval is not None:
|
||||
if 0.005 > pval:
|
||||
comp_symbol = ''
|
||||
elif 0.05 > pval >= 0.005:
|
||||
comp_symbol = '$^{\dag}$'
|
||||
elif pval >= 0.05:
|
||||
comp_symbol = '${\ddag}$'
|
||||
str_cell = f'{mean}{comp_symbol}{std}'
|
||||
|
||||
# color ?
|
||||
# ---------------------------------------------------
|
||||
if self.format.color:
|
||||
str_cell += ' ' + self.group.color(self)
|
||||
|
||||
return str_cell
|
||||
|
||||
|
||||
class CellGroup:
|
||||
|
||||
def __init__(self, lower_is_better=True, stat_test='wilcoxon', color_mode='local', color_global_min=None, color_global_max=None):
|
||||
assert stat_test in ['wilcoxon', 'ttest', None], \
|
||||
f"unknown {stat_test=}, valid ones are wilcoxon, ttest, or None"
|
||||
assert color_mode in ['local', 'global'], \
|
||||
f"unknown {color_mode=}, valid ones are local and global"
|
||||
if (color_global_min is not None or color_global_max is not None) and color_mode=='local':
|
||||
print('warning: color_global_min and color_global_max are only considered when color_mode==local')
|
||||
self.cells = []
|
||||
self.lower_is_better = lower_is_better
|
||||
self.stat_test = stat_test
|
||||
self.color_mode = color_mode
|
||||
self.color_global_min = color_global_min
|
||||
self.color_global_max = color_global_max
|
||||
|
||||
def register_cell(self, cell: Cell):
|
||||
self.cells.append(cell)
|
||||
|
||||
def non_empty_cells(self):
|
||||
return [c for c in self.cells if not c.isEmpty()]
|
||||
|
||||
def max(self):
|
||||
cells = self.non_empty_cells()
|
||||
if len(cells)>0:
|
||||
return cells[np.argmax([c.mean() for c in cells])]
|
||||
return None
|
||||
|
||||
def min(self):
|
||||
cells = self.non_empty_cells()
|
||||
if len(cells) > 0:
|
||||
return cells[np.argmin([c.mean() for c in cells])]
|
||||
return None
|
||||
|
||||
def best(self) -> Cell:
|
||||
return self.min() if self.lower_is_better else self.max()
|
||||
|
||||
def worst(self) -> Cell:
|
||||
return self.max() if self.lower_is_better else self.min()
|
||||
|
||||
def isEmpty(self):
|
||||
return len(self.non_empty_cells())==0
|
||||
|
||||
def compare(self, cell: Cell):
|
||||
best = self.best()
|
||||
best_n = len(best)
|
||||
cell_n = len(cell)
|
||||
if best_n > 0 and cell_n > 0:
|
||||
if self.stat_test == 'wilcoxon':
|
||||
try:
|
||||
_, p_val = wilcoxon(best.values, cell.values)
|
||||
except ValueError:
|
||||
p_val = None
|
||||
return p_val
|
||||
elif self.stat_test == 'ttest':
|
||||
best_mean, best_std = best.mean(), best.std()
|
||||
cell_mean, cell_std = cell.mean(), cell.std()
|
||||
_, p_val = ttest_ind_from_stats(best_mean, best_std, best_n, cell_mean, cell_std, cell_n)
|
||||
return p_val
|
||||
elif self.stat_test is None:
|
||||
return None
|
||||
else:
|
||||
raise ValueError(f'unknown statistical test {self.stat_test}')
|
||||
else:
|
||||
return None
|
||||
|
||||
def color(self, cell: Cell):
|
||||
cell_mean = cell.mean()
|
||||
|
||||
if self.color_mode == 'local':
|
||||
best = self.best()
|
||||
worst = self.worst()
|
||||
best_mean = best.mean()
|
||||
worst_mean = worst.mean()
|
||||
|
||||
if best is None or worst is None or best_mean == worst_mean or cell.isEmpty():
|
||||
return ''
|
||||
|
||||
# normalize val in [0,1]
|
||||
maxval = max(best_mean, worst_mean)
|
||||
minval = min(best_mean, worst_mean)
|
||||
else:
|
||||
maxval = self.color_global_max
|
||||
minval = self.color_global_min
|
||||
|
||||
normval = (cell_mean - minval) / (maxval - minval)
|
||||
|
||||
if self.lower_is_better:
|
||||
normval = 1 - normval
|
||||
|
||||
normval = np.clip(normval, 0, 1)
|
||||
|
||||
normval = normval * 2 - 1 # rescale to [-1,1]
|
||||
if normval < 0:
|
||||
color = 'red'
|
||||
tone = cell.format.maxtone * (-normval)
|
||||
else:
|
||||
color = 'green'
|
||||
tone = cell.format.maxtone * normval
|
||||
|
||||
return f'\cellcolor{{{color}!{int(tone)}}}'
|
||||
|
||||
|
||||
|
||||
class Table:
|
||||
|
||||
def __init__(self,
|
||||
name,
|
||||
benchmarks=None,
|
||||
methods=None,
|
||||
format:CellFormat=None,
|
||||
lower_is_better=True,
|
||||
stat_test='wilcoxon',
|
||||
color_mode='local',
|
||||
with_mean=True
|
||||
):
|
||||
self.name = name
|
||||
self.benchmarks = [] if benchmarks is None else benchmarks
|
||||
self.methods = [] if methods is None else methods
|
||||
self.format = format if format is not None else CellFormat()
|
||||
self.lower_is_better = lower_is_better
|
||||
self.stat_test = stat_test
|
||||
self.color_mode = color_mode
|
||||
self.with_mean = with_mean
|
||||
self.only_full_mean = True # if False, compute the mean of partially empty methods also
|
||||
|
||||
if self.color_mode == 'global':
|
||||
self.color_global_min = 0
|
||||
self.color_global_max = 1
|
||||
else:
|
||||
self.color_global_min = None
|
||||
self.color_global_max = None
|
||||
|
||||
self.T = {}
|
||||
self.groups = {}
|
||||
|
||||
def add(self, benchmark, method, v):
|
||||
cell = self.get(benchmark, method)
|
||||
cell.append(v)
|
||||
|
||||
def get_benchmarks(self):
|
||||
return self.benchmarks
|
||||
|
||||
def get_methods(self):
|
||||
return self.methods
|
||||
|
||||
def n_benchmarks(self):
|
||||
return len(self.benchmarks)
|
||||
|
||||
def n_methods(self):
|
||||
return len(self.methods)
|
||||
|
||||
def _new_group(self):
|
||||
return CellGroup(self.lower_is_better, self.stat_test, color_mode=self.color_mode,
|
||||
color_global_max=self.color_global_max, color_global_min=self.color_global_min)
|
||||
|
||||
def get(self, benchmark, method) -> Cell:
|
||||
if benchmark not in self.benchmarks:
|
||||
self.benchmarks.append(benchmark)
|
||||
if benchmark not in self.groups:
|
||||
self.groups[benchmark] = self._new_group()
|
||||
if method not in self.methods:
|
||||
self.methods.append(method)
|
||||
b_idx = self.benchmarks.index(benchmark)
|
||||
m_idx = self.methods.index(method)
|
||||
idx = tuple((b_idx, m_idx))
|
||||
if idx not in self.T:
|
||||
self.T[idx] = Cell(self.format, group=self.groups[benchmark])
|
||||
cell = self.T[idx]
|
||||
return cell
|
||||
|
||||
def get_value(self, benchmark, method) -> float:
|
||||
return self.get(benchmark, method).mean()
|
||||
|
||||
def get_benchmark(self, benchmark):
|
||||
cells = [self.get(benchmark, method=m) for m in self.get_methods()]
|
||||
cells = [c for c in cells if not c.isEmpty()]
|
||||
return cells
|
||||
|
||||
def get_method(self, method):
|
||||
cells = [self.get(benchmark=b, method=method) for b in self.get_benchmarks()]
|
||||
cells = [c for c in cells if not c.isEmpty()]
|
||||
return cells
|
||||
|
||||
def get_method_means(self, method_order):
|
||||
mean_group = self._new_group()
|
||||
cells = []
|
||||
for method in method_order:
|
||||
method_mean = Cell(self.format, group=mean_group)
|
||||
for bench in self.get_benchmarks():
|
||||
mean_value = self.get_value(benchmark=bench, method=method)
|
||||
if not np.isnan(mean_value):
|
||||
method_mean.append(mean_value)
|
||||
cells.append(method_mean)
|
||||
return cells
|
||||
|
||||
def get_benchmark_values(self, benchmark):
|
||||
values = np.asarray([c.mean() for c in self.get_benchmark(benchmark)])
|
||||
return values
|
||||
|
||||
def get_method_values(self, method):
|
||||
values = np.asarray([c.mean() for c in self.get_method(method)])
|
||||
return values
|
||||
|
||||
def all_mean(self):
|
||||
values = [c.mean() for c in self.T.values() if not c.isEmpty()]
|
||||
return np.mean(values)
|
||||
|
||||
def print(self): # todo: missing method names?
|
||||
data_dict = {}
|
||||
data_dict['Benchmark'] = [b for b in self.get_benchmarks()]
|
||||
for method in self.get_methods():
|
||||
data_dict[method] = [self.get(bench, method).print_mean() for bench in self.get_benchmarks()]
|
||||
df = pd.DataFrame(data_dict)
|
||||
pd.set_option('display.max_columns', None)
|
||||
pd.set_option('display.max_rows', None)
|
||||
print(df.to_string(index=False))
|
||||
|
||||
def tabular(self, path=None, benchmark_replace=None, method_replace=None, benchmark_order=None, method_order=None, transpose=False):
|
||||
if benchmark_replace is None:
|
||||
benchmark_replace = {}
|
||||
if method_replace is None:
|
||||
method_replace = {}
|
||||
if benchmark_order is None:
|
||||
benchmark_order = self.get_benchmarks()
|
||||
if method_order is None:
|
||||
method_order = self.get_methods()
|
||||
|
||||
if transpose:
|
||||
row_order, row_replace = method_order, method_replace
|
||||
col_order, col_replace = benchmark_order, benchmark_replace
|
||||
else:
|
||||
row_order, row_replace = benchmark_order, benchmark_replace
|
||||
col_order, col_replace = method_order, method_replace
|
||||
|
||||
n_cols = len(col_order)
|
||||
add_mean_col = self.with_mean and transpose
|
||||
add_mean_row = self.with_mean and not transpose
|
||||
last_col_idx = n_cols+2 if add_mean_col else n_cols+1
|
||||
|
||||
if self.with_mean:
|
||||
mean_cells = self.get_method_means(method_order)
|
||||
|
||||
lines = []
|
||||
lines.append('\\begin{tabular}{|c' + '|c' * n_cols + ('||c' if add_mean_col else '') + "|}")
|
||||
|
||||
lines.append(f'\\cline{{2-{last_col_idx}}}')
|
||||
l = '\multicolumn{1}{c|}{} & '
|
||||
l += ' & '.join([col_replace.get(col, col) for col in col_order])
|
||||
if add_mean_col:
|
||||
l += ' & Ave.'
|
||||
l += ' \\\\\\hline'
|
||||
lines.append(l)
|
||||
|
||||
for i, row in enumerate(row_order):
|
||||
rowname = row_replace.get(row, row)
|
||||
l = rowname + ' & '
|
||||
l += ' & '.join([
|
||||
self.get(benchmark=col if transpose else row, method=row if transpose else col).print()
|
||||
for col in col_order
|
||||
])
|
||||
if add_mean_col:
|
||||
l+= ' & ' + mean_cells[i].print()
|
||||
l += ' \\\\\\hline'
|
||||
lines.append(l)
|
||||
|
||||
if add_mean_row:
|
||||
lines.append('\hline')
|
||||
l = 'Ave. & '
|
||||
l+= ' & '.join([mean_cell.print() for mean_cell in mean_cells])
|
||||
l += ' \\\\\\hline'
|
||||
lines.append(l)
|
||||
|
||||
lines.append('\\end{tabular}')
|
||||
|
||||
tabular_tex = '\n'.join(lines)
|
||||
|
||||
if path is not None:
|
||||
parent = Path(path).parent
|
||||
if parent:
|
||||
os.makedirs(parent, exist_ok=True)
|
||||
with open(path, 'wt') as foo:
|
||||
foo.write(tabular_tex)
|
||||
|
||||
return tabular_tex
|
||||
|
||||
def table(self, tabular_path, benchmark_replace=None, method_replace=None, resizebox=True, caption=None, label=None, benchmark_order=None, method_order=None, transpose=False):
|
||||
if benchmark_replace is None:
|
||||
benchmark_replace = {}
|
||||
if method_replace is None:
|
||||
method_replace = {}
|
||||
|
||||
lines = []
|
||||
lines.append('\\begin{table}')
|
||||
lines.append('\center')
|
||||
if resizebox:
|
||||
lines.append('\\resizebox{\\textwidth}{!}{%')
|
||||
|
||||
tabular_str = self.tabular(tabular_path, benchmark_replace, method_replace, benchmark_order, method_order, transpose)
|
||||
if tabular_path is None:
|
||||
lines.append(tabular_str)
|
||||
else:
|
||||
lines.append(f'\input{{tables/{Path(tabular_path).name}}}')
|
||||
|
||||
if resizebox:
|
||||
lines.append('}%')
|
||||
if caption is None:
|
||||
caption = tabular_path.replace('_', '\_')
|
||||
lines.append(f'\caption{{{caption}}}')
|
||||
if label is not None:
|
||||
lines.append(f'\label{{{label}}}')
|
||||
lines.append('\end{table}')
|
||||
|
||||
table_tex = '\n'.join(lines)
|
||||
|
||||
return table_tex
|
||||
|
||||
def document(self, tex_path, tabular_dir='tables', *args, **kwargs):
|
||||
Table.Document(tex_path, tables=[self], tabular_dir=tabular_dir, *args, **kwargs)
|
||||
|
||||
def latexPDF(self, pdf_path, tabular_dir='tables', *args, **kwargs):
|
||||
return Table.LatexPDF(pdf_path, tables=[self], tabular_dir=tabular_dir, *args, **kwargs)
|
||||
|
||||
@classmethod
|
||||
def Document(self, tex_path, tables:List['Table'], tabular_dir='tables', *args, **kwargs):
|
||||
lines = []
|
||||
lines.append('\\documentclass[10pt,a4paper]{article}')
|
||||
lines.append('\\usepackage[utf8]{inputenc}')
|
||||
lines.append('\\usepackage{amsmath}')
|
||||
lines.append('\\usepackage{amsfonts}')
|
||||
lines.append('\\usepackage{amssymb}')
|
||||
lines.append('\\usepackage{graphicx}')
|
||||
lines.append('\\usepackage{xcolor}')
|
||||
lines.append('\\usepackage{colortbl}')
|
||||
lines.append('')
|
||||
lines.append('\\begin{document}')
|
||||
for table in tables:
|
||||
lines.append('')
|
||||
lines.append(table.table(os.path.join(Path(tex_path).parent, tabular_dir, table.name + '_table.tex'), *args, **kwargs))
|
||||
lines.append('\\end{document}')
|
||||
|
||||
document = '\n'.join(lines)
|
||||
|
||||
parent = Path(tex_path).parent
|
||||
if parent:
|
||||
os.makedirs(parent, exist_ok=True)
|
||||
with open(tex_path, 'wt') as foo:
|
||||
foo.write(document)
|
||||
|
||||
return document
|
||||
|
||||
@classmethod
|
||||
def LatexPDF(cls, pdf_path: str, tables:List['Table'], tabular_dir: str = 'tables', *args, **kwargs):
|
||||
assert pdf_path.endswith('.pdf'), f'{pdf_path=} does not seem a valid name for a pdf file'
|
||||
tex_path = pdf_path.replace('.pdf', '.tex')
|
||||
|
||||
cls.Document(tex_path, tables, tabular_dir, *args, **kwargs)
|
||||
|
||||
dir = Path(pdf_path).parent
|
||||
pwd = os.getcwd()
|
||||
|
||||
print('currently in', pwd)
|
||||
print("[Tables Done] runing latex")
|
||||
os.chdir(dir)
|
||||
os.system('pdflatex ' + Path(tex_path).name)
|
||||
basename = Path(tex_path).name.replace('.tex', '')
|
||||
os.system(f'rm {basename}.aux {basename}.bbl {basename}.blg {basename}.log {basename}.out {basename}.dvi')
|
||||
os.chdir(pwd)
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from Census.commons import AdjMatrix, load_csv, get_dataset_by_area
|
||||
|
||||
census = './data/cens_y.csv'
|
||||
Areas, X = load_csv(census, use_yhat=True)
|
||||
data = get_dataset_by_area(Areas, X)
|
||||
|
||||
areas = [a for a, *_ in data]
|
||||
|
||||
print(f'Area codes={areas}')
|
||||
|
||||
A = AdjMatrix('./data/matrice_adiacenza.csv')
|
||||
print(A.adjacent(45, 46))
|
||||
print(A.get_adjacent(50))
|
||||
Loading…
Reference in New Issue