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use super() (#51)
1 parent a29b94b commit 989cac0

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11 files changed

+64
-65
lines changed

11 files changed

+64
-65
lines changed

‎apricot/functions/base.py‎

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -564,7 +564,7 @@ def __init__(self, n_samples, metric='euclidean',
564564
n_neighbors=None, reservoir=None, max_reservoir_size=1000,
565565
n_jobs=1, random_state=None, verbose=False):
566566

567-
super(BaseGraphSelection, self).__init__(n_samples=n_samples,
567+
super().__init__(n_samples=n_samples,
568568
initial_subset=initial_subset, optimizer=optimizer,
569569
optimizer_kwds=optimizer_kwds, reservoir=reservoir,
570570
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
@@ -623,7 +623,7 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
623623
n_neighbors=self.n_neighbors)
624624

625625
self._X = X
626-
return super(BaseGraphSelection, self).fit(X_pairwise, y=y,
626+
return super().fit(X_pairwise, y=y,
627627
sample_weight=sample_weight, sample_cost=sample_cost)
628628

629629
def partial_fit(self, X, y=None, sample_weight=None, sample_cost=None):
@@ -645,22 +645,22 @@ def partial_fit(self, X, y=None, sample_weight=None, sample_cost=None):
645645
Y=self.reservoir[:self.reservoir_size], metric=self.metric)
646646

647647
self._X = X
648-
super(BaseGraphSelection, self).partial_fit(X_pairwise, y=y,
648+
super().partial_fit(X_pairwise, y=y,
649649
sample_weight=sample_weight, sample_cost=sample_cost)
650650

651651
self.current_values = numpy.zeros(self.reservoir_size,
652652
dtype='float64')
653653
self.n_seen_ += X.shape[0]
654654

655655
def _initialize(self, X_pairwise, idxs=None):
656-
super(BaseGraphSelection, self)._initialize(X_pairwise, idxs=idxs)
656+
super()._initialize(X_pairwise, idxs=idxs)
657657

658658
def _calculate_gains(self, X_pairwise):
659-
super(BaseGraphSelection, self)._calculate_gains(X_pairwise)
659+
super()._calculate_gains(X_pairwise)
660660

661661
def _calculate_sieve_gains(self, X, thresholds, idxs):
662-
super(BaseGraphSelection, self)._calculate_sieve_gains(X, thresholds,
662+
super()._calculate_sieve_gains(X, thresholds,
663663
idxs)
664664

665665
def _select_next(self, X_pairwise, gain, idx):
666-
super(BaseGraphSelection, self)._select_next(X_pairwise, gain, idx)
666+
super()._select_next(X_pairwise, gain, idx)

‎apricot/functions/custom.py‎

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -109,7 +109,7 @@ def __init__(self, n_samples, function, initial_subset=None,
109109
self.function = function
110110
self.function_kwds = function_kwds
111111

112-
super(CustomSelection, self).__init__(n_samples=n_samples,
112+
super().__init__(n_samples=n_samples,
113113
initial_subset=initial_subset, optimizer=optimizer,
114114
optimizer_kwds=optimizer_kwds, n_jobs=n_jobs,
115115
random_state=random_state, verbose=verbose)
@@ -152,11 +152,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
152152
The fit step returns this selector object.
153153
"""
154154

155-
return super(CustomSelection, self).fit(X, y=y,
155+
return super().fit(X, y=y,
156156
sample_weight=sample_weight, sample_cost=sample_cost)
157157

158158
def _initialize(self, X):
159-
super(CustomSelection, self)._initialize(X)
159+
super()._initialize(X)
160160

161161
if self.initial_subset is None:
162162
pass
@@ -209,7 +209,7 @@ def _calculate_sieve_gains(self, X, thresholds, idxs):
209209
used by a streaming optimizer.
210210
"""
211211

212-
super(CustomSelection, self)._calculate_sieve_gains(X,
212+
super()._calculate_sieve_gains(X,
213213
thresholds, idxs)
214214

215215
raise NotImplementedError
@@ -219,7 +219,7 @@ def _select_next(self, X, gain, idx):
219219

220220
self.total_gain += gain
221221

222-
super(CustomSelection, self)._select_next(
222+
super()._select_next(
223223
X, gain, idx)
224224

225225

@@ -321,7 +321,7 @@ def __init__(self, n_samples, function, metric='euclidean',
321321
self.function = function
322322
self.function_kwds = function_kwds
323323

324-
super(CustomGraphSelection, self).__init__(n_samples=n_samples,
324+
super().__init__(n_samples=n_samples,
325325
metric=metric, initial_subset=initial_subset, optimizer=optimizer,
326326
optimizer_kwds=optimizer_kwds, n_jobs=n_jobs,
327327
random_state=random_state, verbose=verbose)
@@ -364,11 +364,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
364364
The fit step returns this selector object.
365365
"""
366366

367-
return super(CustomGraphSelection, self).fit(X, y=y,
367+
return super().fit(X, y=y,
368368
sample_weight=sample_weight, sample_cost=sample_cost)
369369

370370
def _initialize(self, X):
371-
super(CustomGraphSelection, self)._initialize(X)
371+
super()._initialize(X)
372372

373373
if self.initial_subset is None:
374374
pass
@@ -421,7 +421,7 @@ def _calculate_sieve_gains(self, X, thresholds, idxs):
421421
used by a streaming optimizer.
422422
"""
423423

424-
super(CustomGraphSelection, self)._calculate_sieve_gains(X,
424+
super()._calculate_sieve_gains(X,
425425
thresholds, idxs)
426426

427427
raise NotImplementedError
@@ -431,5 +431,5 @@ def _select_next(self, X, gain, idx):
431431

432432
self.total_gain += gain
433433

434-
super(CustomGraphSelection, self)._select_next(
434+
super()._select_next(
435435
X, gain, idx)

‎apricot/functions/facilityLocation.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -208,7 +208,7 @@ def __init__(self, n_samples, metric='euclidean',
208208
n_neighbors=None, reservoir=None, max_reservoir_size=1000,
209209
n_jobs=1, random_state=None, verbose=False):
210210

211-
super(FacilityLocationSelection, self).__init__(n_samples=n_samples,
211+
super().__init__(n_samples=n_samples,
212212
metric=metric, initial_subset=initial_subset, optimizer=optimizer,
213213
optimizer_kwds=optimizer_kwds, n_neighbors=n_neighbors,
214214
reservoir=reservoir, max_reservoir_size=max_reservoir_size,
@@ -252,11 +252,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
252252
The fit step returns this selector object.
253253
"""
254254

255-
return super(FacilityLocationSelection, self).fit(X, y=y,
255+
return super().fit(X, y=y,
256256
sample_weight=sample_weight, sample_cost=sample_cost)
257257

258258
def _initialize(self, X_pairwise):
259-
super(FacilityLocationSelection, self)._initialize(X_pairwise)
259+
super()._initialize(X_pairwise)
260260

261261
if self.initial_subset is None:
262262
pass
@@ -317,7 +317,7 @@ def _calculate_sieve_gains(self, X_pairwise, thresholds, idxs):
317317
used by a streaming optimizer.
318318
"""
319319

320-
super(FacilityLocationSelection, self)._calculate_sieve_gains(
320+
super()._calculate_sieve_gains(
321321
X_pairwise,thresholds, idxs)
322322

323323
if self.sparse:
@@ -344,5 +344,5 @@ def _select_next(self, X_pairwise, gain, idx):
344344

345345
self.current_values_sum = self.current_values.sum()
346346

347-
super(FacilityLocationSelection, self)._select_next(
347+
super()._select_next(
348348
X_pairwise, gain, idx)

‎apricot/functions/featureBased.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -246,7 +246,7 @@ def __init__(self, n_samples, concave_func='sqrt', initial_subset=None,
246246
else:
247247
raise KeyError("Must be one of 'log', 'sqrt', 'sigmoid', or a any other numpy, scipy, or numba-compiled function.")
248248

249-
super(FeatureBasedSelection, self).__init__(n_samples=n_samples,
249+
super().__init__(n_samples=n_samples,
250250
initial_subset=initial_subset, optimizer=optimizer,
251251
optimizer_kwds=optimizer_kwds, reservoir=reservoir,
252252
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
@@ -290,11 +290,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
290290
The fit step returns this selector object.
291291
"""
292292

293-
return super(FeatureBasedSelection, self).fit(X, y=y,
293+
return super().fit(X, y=y,
294294
sample_weight=sample_weight, sample_cost=sample_cost)
295295

296296
def _initialize(self, X):
297-
super(FeatureBasedSelection, self)._initialize(X)
297+
super()._initialize(X)
298298

299299
if self.initial_subset is None:
300300
pass
@@ -353,7 +353,7 @@ def _calculate_sieve_gains(self, X, thresholds, idxs):
353353
used by a streaming optimizer.
354354
"""
355355

356-
super(FeatureBasedSelection, self)._calculate_sieve_gains(X,
356+
super()._calculate_sieve_gains(X,
357357
thresholds, idxs)
358358

359359
if self.sparse:
@@ -379,6 +379,6 @@ def _select_next(self, X, gain, idx):
379379
self.current_concave_values = self.concave_func(self.current_values)
380380
self.current_concave_values_sum = self.current_concave_values.sum()
381381

382-
super(FeatureBasedSelection, self)._select_next(
382+
super()._select_next(
383383
X, gain, idx)
384384

‎apricot/functions/graphCut.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -177,7 +177,7 @@ def __init__(self, n_samples=10, metric='euclidean', alpha=1,
177177
n_jobs=1, random_state=None, verbose=False):
178178
self.alpha = alpha
179179

180-
super(GraphCutSelection, self).__init__(n_samples=n_samples,
180+
super().__init__(n_samples=n_samples,
181181
metric=metric, initial_subset=initial_subset, optimizer=optimizer,
182182
n_neighbors=n_neighbors, reservoir=reservoir,
183183
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
@@ -221,11 +221,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
221221
The fit step returns this selector object.
222222
"""
223223

224-
return super(GraphCutSelection, self).fit(X, y=y,
224+
return super().fit(X, y=y,
225225
sample_weight=sample_weight, sample_cost=sample_cost)
226226

227227
def _initialize(self, X_pairwise):
228-
super(GraphCutSelection, self)._initialize(X_pairwise)
228+
super()._initialize(X_pairwise)
229229

230230
if self.reservoir is not None:
231231
X_pairwise = _calculate_pairwise_distances(self._X,
@@ -271,7 +271,7 @@ def _calculate_sieve_gains(self, X_pairwise, thresholds, idxs):
271271
used by a streaming optimizer.
272272
"""
273273

274-
super(GraphCutSelection, self)._calculate_sieve_gains(X_pairwise,
274+
super()._calculate_sieve_gains(X_pairwise,
275275
thresholds, idxs)
276276

277277
n, m = X_pairwise.shape[0], len(thresholds)
@@ -309,5 +309,5 @@ def _select_next(self, X_pairwise, gain, idx):
309309
else:
310310
self.current_values += X_pairwise * 2
311311

312-
super(GraphCutSelection, self)._select_next(
312+
super()._select_next(
313313
X_pairwise, gain, idx)

‎apricot/functions/maxCoverage.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -209,7 +209,7 @@ def __init__(self, n_samples, threshold=1.0, initial_subset=None,
209209
verbose=False):
210210
self.threshold = threshold
211211

212-
super(MaxCoverageSelection, self).__init__(n_samples=n_samples,
212+
super().__init__(n_samples=n_samples,
213213
initial_subset=initial_subset, optimizer=optimizer,
214214
optimizer_kwds=optimizer_kwds, n_jobs=n_jobs,
215215
random_state=random_state, verbose=verbose)
@@ -252,11 +252,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
252252
The fit step returns this selector object.
253253
"""
254254

255-
return super(MaxCoverageSelection, self).fit(X, y=y,
255+
return super().fit(X, y=y,
256256
sample_weight=sample_weight, sample_cost=sample_cost)
257257

258258
def _initialize(self, X):
259-
super(MaxCoverageSelection, self)._initialize(X)
259+
super()._initialize(X)
260260

261261
if self.initial_subset is None:
262262
self.current_values = numpy.zeros(X.shape[1], dtype='float64')
@@ -317,7 +317,7 @@ def _calculate_sieve_gains(self, X, thresholds, idxs):
317317
used by a streaming optimizer.
318318
"""
319319

320-
super(MaxCoverageSelection, self)._calculate_sieve_gains(X,
320+
super()._calculate_sieve_gains(X,
321321
thresholds, idxs)
322322

323323
if self.sparse:
@@ -344,5 +344,5 @@ def _select_next(self, X, gain, idx):
344344

345345
self.current_values_sum = self.current_values.sum()
346346

347-
super(MaxCoverageSelection, self)._select_next(
347+
super()._select_next(
348348
X, gain, idx)

‎apricot/functions/mixture.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -152,7 +152,7 @@ def __init__(self, n_samples, functions, weights=None, metric='ignore',
152152
else:
153153
self.weights = numpy.array(weights, dtype='float64')
154154

155-
super(MixtureSelection, self).__init__(n_samples=n_samples,
155+
super().__init__(n_samples=n_samples,
156156
initial_subset=initial_subset, optimizer=optimizer,
157157
optimizer_kwds=optimizer_kwds, reservoir=reservoir,
158158
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
@@ -211,11 +211,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
211211
X = _calculate_pairwise_distances(X, metric=self.metric,
212212
n_neighbors=self.n_neighbors)
213213

214-
return super(MixtureSelection, self).fit(X, y=y,
214+
return super().fit(X, y=y,
215215
sample_weight=sample_weight, sample_cost=sample_cost)
216216

217217
def _initialize(self, X):
218-
super(MixtureSelection, self)._initialize(X)
218+
super()._initialize(X)
219219

220220
for function in self.functions:
221221
function._initialize(X)
@@ -237,7 +237,7 @@ def _calculate_gains(self, X, idxs=None):
237237
return gains
238238

239239
def _calculate_sieve_gains(self, X, thresholds, idxs):
240-
super(MixtureSelection, self)._calculate_sieve_gains(X,
240+
super()._calculate_sieve_gains(X,
241241
thresholds, idxs)
242242

243243
for function in self.functions:
@@ -292,4 +292,4 @@ def _select_next(self, X, gain, idx):
292292
for function in self.functions:
293293
function._select_next(X, gain, idx)
294294

295-
super(MixtureSelection, self)._select_next(X, gain, idx)
295+
super()._select_next(X, gain, idx)

‎apricot/functions/saturatedCoverage.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -167,7 +167,7 @@ def __init__(self, n_samples=10, metric='euclidean', alpha=0.1,
167167
optimizer_kwds={}, verbose=False):
168168
self.alpha = alpha
169169

170-
super(SaturatedCoverageSelection, self).__init__(n_samples=n_samples,
170+
super().__init__(n_samples=n_samples,
171171
metric=metric,initial_subset=initial_subset, optimizer=optimizer,
172172
optimizer_kwds=optimizer_kwds, n_neighbors=n_neighbors,
173173
reservoir=reservoir, max_reservoir_size=max_reservoir_size,
@@ -211,11 +211,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
211211
The fit step returns this selector object.
212212
"""
213213

214-
return super(SaturatedCoverageSelection, self).fit(X, y=y,
214+
return super().fit(X, y=y,
215215
sample_weight=sample_weight, sample_cost=sample_cost)
216216

217217
def _initialize(self, X_pairwise):
218-
super(SaturatedCoverageSelection, self)._initialize(X_pairwise)
218+
super()._initialize(X_pairwise)
219219

220220
if self.sparse:
221221
self.max_values = self.alpha * numpy.array(
@@ -266,5 +266,5 @@ def _select_next(self, X_pairwise, gain, idx):
266266
self.current_values = numpy.minimum(self.max_values,
267267
self.current_values + X_pairwise)
268268

269-
super(SaturatedCoverageSelection, self)._select_next(
269+
super()._select_next(
270270
X_pairwise, gain, idx)

‎apricot/functions/sumRedundancy.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -141,7 +141,7 @@ def __init__(self, n_samples=10, metric='euclidean',
141141
reservoir=None, max_reservoir_size=1000, n_jobs=1,
142142
random_state=None, optimizer_kwds={}, verbose=False):
143143

144-
super(SumRedundancySelection, self).__init__(n_samples=n_samples,
144+
super().__init__(n_samples=n_samples,
145145
metric=metric, initial_subset=initial_subset, optimizer=optimizer,
146146
n_neighbors=n_neighbors, reservoir=reservoir,
147147
max_reservoir_size=max_reservoir_size, n_jobs=n_jobs,
@@ -186,11 +186,11 @@ def fit(self, X, y=None, sample_weight=None, sample_cost=None):
186186
The fit step returns this selector object.
187187
"""
188188

189-
return super(SumRedundancySelection, self).fit(X, y=y,
189+
return super().fit(X, y=y,
190190
sample_weight=sample_weight, sample_cost=sample_cost)
191191

192192
def _initialize(self, X_pairwise, idxs=None):
193-
super(SumRedundancySelection, self)._initialize(X_pairwise, idxs=idxs)
193+
super()._initialize(X_pairwise, idxs=idxs)
194194
idxs = idxs if idxs is not None else numpy.arange(X_pairwise.shape[0])
195195

196196
for i, idx in enumerate(idxs):
@@ -224,5 +224,5 @@ def _select_next(self, X_pairwise, gain, idx):
224224
else:
225225
self.current_values += X_pairwise * 2
226226

227-
super(SumRedundancySelection, self)._select_next(
227+
super()._select_next(
228228
X_pairwise, gain, idx)

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