From 5e64d2588aa1eb5eb40054d9208c757952c3e604 Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Fri, 15 Jan 2021 18:32:32 +0100
Subject: [PATCH] import fixes
---
TweetSentQuant/experiments.py | 26 ++++++++++++++++----------
quapy/__init__.py | 6 +++---
quapy/classification/neural.py | 5 +++--
quapy/classification/svmperf.py | 3 ++-
quapy/data/__init__.py | 4 ++--
quapy/data/base.py | 2 +-
quapy/data/datasets.py | 11 ++++++-----
quapy/data/preprocessing.py | 9 +++++----
quapy/data/reader.py | 1 -
quapy/error.py | 4 ++--
quapy/evaluation.py | 15 ++++++++-------
quapy/functional.py | 12 +++---------
quapy/method/__init__.py | 5 ++---
quapy/method/aggregative.py | 23 ++++++++++++-----------
quapy/method/base.py | 3 ++-
quapy/method/meta.py | 17 +++++++++--------
quapy/method/neural.py | 7 ++++---
quapy/method/non_aggregative.py | 2 +-
quapy/model_selection.py | 19 ++++++++++---------
quapy/plot.py | 9 +++++----
quapy/util.py | 11 ++++++-----
21 files changed, 102 insertions(+), 92 deletions(-)
diff --git a/TweetSentQuant/experiments.py b/TweetSentQuant/experiments.py
index 136ddea..5add09b 100644
--- a/TweetSentQuant/experiments.py
+++ b/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']
diff --git a/quapy/__init__.py b/quapy/__init__.py
index 011412e..20dc49b 100644
--- a/quapy/__init__.py
+++ b/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 = {
diff --git a/quapy/classification/neural.py b/quapy/classification/neural.py
index 5f8038d..c14ca36 100644
--- a/quapy/classification/neural.py
+++ b/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:
diff --git a/quapy/classification/svmperf.py b/quapy/classification/svmperf.py
index ceab225..38cf6c6 100644
--- a/quapy/classification/svmperf.py
+++ b/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
diff --git a/quapy/data/__init__.py b/quapy/data/__init__.py
index 9c119ab..563cfcf 100644
--- a/quapy/data/__init__.py
+++ b/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
diff --git a/quapy/data/base.py b/quapy/data/base.py
index 322ba71..697bcf6 100644
--- a/quapy/data/base.py
+++ b/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:
diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py
index 2af3de3..dca22cd 100644
--- a/quapy/data/datasets.py
+++ b/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',
diff --git a/quapy/data/preprocessing.py b/quapy/data/preprocessing.py
index 972a3db..4376482 100644
--- a/quapy/data/preprocessing.py
+++ b/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):
diff --git a/quapy/data/reader.py b/quapy/data/reader.py
index 7597fd0..f7e45f4 100644
--- a/quapy/data/reader.py
+++ b/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):
diff --git a/quapy/error.py b/quapy/error.py
index e39aa8a..b1e5f14 100644
--- a/quapy/error.py
+++ b/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):
diff --git a/quapy/evaluation.py b/quapy/evaluation.py
index 498f284..78917d1 100644
--- a/quapy/evaluation.py
+++ b/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
diff --git a/quapy/functional.py b/quapy/functional.py
index 579e738..726b214 100644
--- a/quapy/functional.py
+++ b/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)
diff --git a/quapy/method/__init__.py b/quapy/method/__init__.py
index 47d2b3a..6ef83f1 100644
--- a/quapy/method/__init__.py
+++ b/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,
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 77c585f..ef233e9 100644
--- a/quapy/method/aggregative.py
+++ b/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
# ------------------------------------
diff --git a/quapy/method/base.py b/quapy/method/base.py
index 2627b87..9dfd74c 100644
--- a/quapy/method/base.py
+++ b/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
diff --git a/quapy/method/meta.py b/quapy/method/meta.py
index 08d4813..cc2a473 100644
--- a/quapy/method/meta.py
+++ b/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
diff --git a/quapy/method/neural.py b/quapy/method/neural.py
index 9adf175..896ff43 100644
--- a/quapy/method/neural.py
+++ b/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):
diff --git a/quapy/method/non_aggregative.py b/quapy/method/non_aggregative.py
index a23ffce..4defdeb 100644
--- a/quapy/method/non_aggregative.py
+++ b/quapy/method/non_aggregative.py
@@ -1,4 +1,4 @@
-from data import LabelledCollection
+from quapy.data import LabelledCollection
from .base import BaseQuantifier
diff --git a/quapy/model_selection.py b/quapy/model_selection.py
index 8f782c1..c3a2556 100644
--- a/quapy/model_selection.py
+++ b/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:
diff --git a/quapy/plot.py b/quapy/plot.py
index 5164a59..7fbdae6 100644
--- a/quapy/plot.py
+++ b/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
diff --git a/quapy/util.py b/quapy/util.py
index 3bb9446..4f5fc02 100644
--- a/quapy/util.py
+++ b/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):