import fixes

TweetSentQuant/experiments.py

@@ -8,22 +8,28 @@ import pickle
 import itertools
 from joblib import Parallel, delayed
 import settings
+import argparse
+
+parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
+parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
+args = parser.parse_args()
 
 
 def quantification_models():
     def newLR():
         return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
     __C_range = np.logspace(-4, 5, 10)
-    lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
+    #lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
     svmperf_params = {'C': __C_range}
+    lr_params = {'C': [1,10]}
     yield 'cc', qp.method.aggregative.CC(newLR()), lr_params
-    yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
-    yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
-    yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
-    yield 'sld', qp.method.aggregative.EMQ(newLR()), lr_params
-    yield 'svmq', OneVsAll(qp.method.aggregative.SVMQ(settings.SVMPERF_HOME)), svmperf_params
-    yield 'svmkld', OneVsAll(qp.method.aggregative.SVMKLD(settings.SVMPERF_HOME)), svmperf_params
-    yield 'svmnkld', OneVsAll(qp.method.aggregative.SVMNKLD(settings.SVMPERF_HOME)), svmperf_params
+    #yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
+    #yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
+    #yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
+    #yield 'sld', qp.method.aggregative.EMQ(newLR()), lr_params
+    #yield 'svmq', OneVsAll(qp.method.aggregative.SVMQ(settings.SVMPERF_HOME)), svmperf_params
+    #yield 'svmkld', OneVsAll(qp.method.aggregative.SVMKLD(settings.SVMPERF_HOME)), svmperf_params
+    #yield 'svmnkld', OneVsAll(qp.method.aggregative.SVMNKLD(settings.SVMPERF_HOME)), svmperf_params
 
 #     'svmmae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mae'),
 #     'svmmrae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mrae'),
@@ -47,7 +53,7 @@ def evaluate_method_point_test(true_prev, estim_prev):
 
 
 def result_path(dataset_name, model_name, optim_loss):
-    return f'./results/{dataset_name}-{model_name}-{optim_loss}.pkl'
+    return os.path.join(args.results, f'{dataset_name}-{model_name}-{optim_loss}.pkl')
 
 
 def is_already_computed(dataset_name, model_name, optim_loss):
@@ -77,7 +83,6 @@ def run(experiment):
         return
     else:
         print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}')
-    return
 
     benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
     benchmark_devel.stats()
@@ -125,6 +130,7 @@ def run(experiment):
 
 if __name__ == '__main__':
 
+    print(f'Result folder: {args.results}')
     np.random.seed(0)
 
     optim_losses = ['mae', 'mrae']

quapy/__init__.py

@@ -1,13 +1,13 @@
 from . import error
-from .data import datasets
+from . import data
+from quapy.data import datasets
 from . import functional
 from . import method
-from . import data
 from . import evaluation
 from . import plot
 from . import util
 from . import model_selection
-from method.aggregative import isaggregative, isprobabilistic
+from quapy.method.aggregative import isaggregative, isprobabilistic
 
 
 environ = {

quapy/classification/neural.py

@@ -1,6 +1,7 @@
 import os
 from abc import ABCMeta, abstractmethod
 from pathlib import Path
+
 import numpy as np
 import torch
 import torch.nn as nn
@@ -8,10 +9,10 @@ import torch.nn.functional as F
 from sklearn.metrics import accuracy_score, f1_score
 from torch.nn.utils.rnn import pad_sequence
 from tqdm import tqdm
+
+import quapy as qp
 from data import LabelledCollection
 from util import EarlyStop
-import quapy as qp
-
 
 
 class NeuralClassifierTrainer:

quapy/classification/svmperf.py

@@ -1,9 +1,10 @@
 import random
 import subprocess
 import tempfile
-from os.path import join, exists
 from os import remove
+from os.path import join, exists
 from subprocess import PIPE, STDOUT
+
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.datasets import dump_svmlight_file

quapy/data/__init__.py

@@ -1,6 +1,6 @@
+from . import datasets
+from . import preprocessing
 from .base import *
 from .reader import *
-from . import preprocessing
-from . import datasets
 
 

quapy/data/base.py

@@ -1,8 +1,8 @@
 import numpy as np
 from scipy.sparse import issparse
+from scipy.sparse import vstack
 from sklearn.model_selection import train_test_split
 from quapy.functional import artificial_prevalence_sampling, strprev
-from scipy.sparse import vstack
 
 
 class LabelledCollection:

quapy/data/datasets.py

@@ -1,12 +1,13 @@
-import zipfile
-from util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
 import os
+import zipfile
 from os.path import join
-from data.base import Dataset, LabelledCollection
-from data.reader import *
-from data.preprocessing import text2tfidf, reduce_columns
+
 import pandas as pd
 
+from data.base import Dataset, LabelledCollection
+from quapy.data.preprocessing import text2tfidf, reduce_columns
+from quapy.data.reader import *
+from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
 
 REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
 TWITTER_SENTIMENT_DATASETS_TEST = ['gasp', 'hcr', 'omd', 'sanders',

quapy/data/preprocessing.py

@@ -1,11 +1,12 @@
 import numpy as np
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
-from data.base import Dataset
 from scipy.sparse import spmatrix
-from util import parallelize
-from .base import LabelledCollection
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
 from tqdm import tqdm
+
 import quapy as qp
+from quapy.data.base import Dataset
+from quapy.util import parallelize
+from .base import LabelledCollection
 
 
 def text2tfidf(dataset:Dataset, min_df=3, sublinear_tf=True, inplace=False, **kwargs):

quapy/data/reader.py

@@ -1,7 +1,6 @@
 import numpy as np
 from scipy.sparse import dok_matrix
 from tqdm import tqdm
-import pandas as pd
 
 
 def from_text(path):

quapy/error.py

@@ -1,7 +1,7 @@
-from sklearn.metrics import f1_score
 import numpy as np
-import quapy as qp
+from sklearn.metrics import f1_score
 
+import quapy as qp
 
 
 def f1e(y_true, y_pred):

quapy/evaluation.py

@@ -1,12 +1,13 @@
-import quapy as qp
 from typing import Union, Callable, Iterable
-from data import LabelledCollection
-from method.base import BaseQuantifier
-from util import temp_seed
+
 import numpy as np
 from joblib import Parallel, delayed
 from tqdm import tqdm
-import error
+
+import quapy as qp
+from quapy.data import LabelledCollection
+from quapy.method.base import BaseQuantifier
+from quapy.util import temp_seed
 
 
 def artificial_sampling_prediction(
@@ -72,8 +73,8 @@ def artificial_sampling_prediction(
 
 def evaluate(model: BaseQuantifier, test_samples:Iterable[LabelledCollection], err:Union[str, Callable], n_jobs:int=-1):
     if isinstance(err, str):
-        err = getattr(error, err)
-    assert err.__name__ in error.QUANTIFICATION_ERROR_NAMES, \
+        err = getattr(qp.error, err)
+    assert err.__name__ in qp.error.QUANTIFICATION_ERROR_NAMES, \
         f'error={err} does not seem to be a quantification error'
     scores = Parallel(n_jobs=n_jobs)(
         delayed(_delayed_eval)(model, Ti, err) for Ti in test_samples

quapy/functional.py

@@ -1,6 +1,7 @@
-from collections import defaultdict
-import numpy as np
 import itertools
+from collections import defaultdict
+
+import numpy as np
 
 
 def artificial_prevalence_sampling(dimensions, n_prevalences=21, repeat=1, return_constrained_dim=False):
@@ -61,13 +62,6 @@ def HellingerDistance(P, Q):
     return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
 
 
-#def uniform_simplex_sampling(n_classes):
-    # from https://cs.stackexchange.com/questions/3227/uniform-sampling-from-a-simplex
-#    r = [0.] + sorted(np.random.rand(n_classes-1)) + [1.]
-#    return np.asarray([b-a for a,b in zip(r[:-1],r[1:])])
-
-
-
 def uniform_prevalence_sampling(n_classes, size=1):
     if n_classes == 2:
         u = np.random.rand(size)

quapy/method/__init__.py

@@ -1,8 +1,7 @@
-from . import base
 from . import aggregative
-from . import non_aggregative
+from . import base
 from . import meta
-
+from . import non_aggregative
 
 AGGREGATIVE_METHODS = {
     aggregative.CC,

quapy/method/aggregative.py

@@ -1,19 +1,20 @@
-import numpy as np
-from copy import deepcopy
-from sklearn.base import BaseEstimator, clone
-import functional as F
-import error
-from method.base import BaseQuantifier, BinaryQuantifier
-from classification.svmperf import SVMperf
-from data import LabelledCollection
-from sklearn.metrics import confusion_matrix
-from sklearn.calibration import CalibratedClassifierCV
-from joblib import Parallel, delayed
 from abc import abstractmethod
+from copy import deepcopy
 from typing import Union
+
+import numpy as np
+from joblib import Parallel, delayed
+from sklearn.base import BaseEstimator
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.metrics import confusion_matrix
 from sklearn.model_selection import StratifiedKFold
 from tqdm import tqdm
 
+import quapy.functional as F
+from quapy.classification.svmperf import SVMperf
+from quapy.data import LabelledCollection
+from quapy.method.base import BaseQuantifier, BinaryQuantifier
+
 
 # Abstract classes
 # ------------------------------------

quapy/method/base.py

@@ -1,5 +1,6 @@
 from abc import ABCMeta, abstractmethod
-from data import LabelledCollection
+
+from quapy.data import LabelledCollection
 
 
 # Base Quantifier abstract class

quapy/method/meta.py

@@ -1,16 +1,17 @@
+from copy import deepcopy
+
 import numpy as np
-from sklearn.linear_model import LogisticRegressionCV, LogisticRegression
+from joblib import Parallel, delayed
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV, cross_val_predict
 
 import quapy as qp
-from sklearn.model_selection import GridSearchCV, cross_val_predict
-from model_selection import GridSearchQ
-from .base import BaseQuantifier, BinaryQuantifier
-from joblib import Parallel, delayed
-from copy import deepcopy
-from data import LabelledCollection
 from quapy import functional as F
+from quapy.data import LabelledCollection
+from quapy.evaluation import evaluate
+from quapy.model_selection import GridSearchQ
 from . import neural
-from evaluation import evaluate
+from .base import BaseQuantifier
 
 QuaNet = neural.QuaNetTrainer
 

quapy/method/neural.py

@@ -1,11 +1,12 @@
 import os
 from pathlib import Path
+
 import torch
 from torch.nn import MSELoss
 from torch.nn.functional import relu
-from tqdm import tqdm
-from method.aggregative import *
-from util import EarlyStop
+
+from quapy.method.aggregative import *
+from quapy.util import EarlyStop
 
 
 class QuaNetTrainer(BaseQuantifier):

quapy/method/non_aggregative.py

@@ -1,4 +1,4 @@
-from data import LabelledCollection
+from quapy.data import LabelledCollection
 from .base import BaseQuantifier
 
 

quapy/model_selection.py

@@ -1,12 +1,13 @@
 import itertools
-import quapy as qp
-from evaluation import artificial_sampling_prediction
-from data.base import LabelledCollection
-from method.aggregative import BaseQuantifier
-from typing import Union, Callable
-import functional as F
-from copy import deepcopy
 import signal
+from copy import deepcopy
+from typing import Union, Callable
+
+import quapy as qp
+import quapy.functional as F
+from data.base import LabelledCollection
+from quapy.evaluation import artificial_sampling_prediction
+from quapy.method.aggregative import BaseQuantifier
 
 
 class GridSearchQ(BaseQuantifier):
@@ -80,8 +81,8 @@ class GridSearchQ(BaseQuantifier):
             training, validation = training.split_stratified(train_prop=1-validation)
             return training, validation
         else:
-            raise ValueError('"validation" must either be a LabelledCollection or a float in (0,1) indicating the'
-                             'proportion of training documents to extract')
+            raise ValueError(f'"validation" must either be a LabelledCollection or a float in (0,1) indicating the'
+                             f'proportion of training documents to extract (found) {type(validation)}')
 
     def __check_num_evals(self, n_prevpoints, eval_budget, n_repetitions, n_classes):
         if n_prevpoints is None and eval_budget is None:

quapy/plot.py

@@ -1,9 +1,10 @@
 from collections import defaultdict
-import matplotlib.pyplot as plt
-from matplotlib import cm
-import numpy as np
-import quapy as qp
 
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib import cm
+
+import quapy as qp
 
 plt.rcParams['figure.figsize'] = [12, 8]
 plt.rcParams['figure.dpi'] = 200

quapy/util.py

@@ -1,12 +1,13 @@
+import contextlib
 import itertools
 import multiprocessing
-from joblib import Parallel, delayed
-import contextlib
-import numpy as np
-import urllib
 import os
-from pathlib import Path
 import pickle
+import urllib
+from pathlib import Path
+
+import numpy as np
+from joblib import Parallel, delayed
 
 
 def get_parallel_slices(n_tasks, n_jobs=-1):