[DOC, MNT] remove remove import from examples (con't)

docs/Examples/HED.rst

@@ -28,13 +28,13 @@ model.
     import torch
     import torch.nn as nn
     import matplotlib.pyplot as plt
-    from examples.hed.datasets import get_dataset, split_equation
-    from examples.models.nn import SymbolNet
+    from datasets import get_dataset, split_equation
+    from models.nn import SymbolNet
     from abl.learning import ABLModel, BasicNN
-    from examples.hed.reasoning import HedKB, HedReasoner
+    from reasoning import HedKB, HedReasoner
     from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
-    from examples.hed.bridge import HedBridge
+    from bridge import HedBridge
 
 Working with Data
 -----------------

docs/Examples/HWF.rst

@@ -26,8 +26,8 @@ machine learning model.
     import torch
     import torch.nn as nn
     import matplotlib.pyplot as plt
-    from examples.hwf.datasets import get_dataset
-    from examples.models.nn import SymbolNet
+    from datasets import get_dataset
+    from models.nn import SymbolNet
     from abl.learning import ABLModel, BasicNN
     from abl.reasoning import KBBase, Reasoner
     from abl.data.evaluation import ReasoningMetric, SymbolAccuracy

docs/Examples/MNISTAdd.rst

@@ -27,8 +27,8 @@ machine learning model.
 
     from torch.optim import RMSprop, lr_scheduler
 
-    from examples.mnist_add.datasets import get_dataset
-    from examples.models.nn import LeNet5
+    from datasets import get_dataset
+    from models.nn import LeNet5
     from abl.learning import ABLModel, BasicNN
     from abl.reasoning import KBBase, Reasoner
     from abl.data.evaluation import ReasoningMetric, SymbolAccuracy

docs/Examples/Zoo.rst

@@ -22,9 +22,9 @@ further update the learning model.
     import os.path as osp
     import numpy as np
     from sklearn.ensemble import RandomForestClassifier
-    from examples.zoo.get_dataset import load_and_preprocess_dataset, split_dataset
+    from get_dataset import load_and_preprocess_dataset, split_dataset
     from abl.learning import ABLModel
-    from examples.zoo.kb import ZooKB
+    from kb import ZooKB
     from abl.reasoning import Reasoner
     from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log, confidence_dist

docs/Intro/Quick-Start.rst

@@ -21,7 +21,7 @@ In the MNIST Addition task, the data loading looks like
 
 .. code:: python
 
-   from examples.mnist_add.datasets.get_mnist_add import get_mnist_add
+   from datasets.get_mnist_add import get_mnist_add
    
    # train_data and test_data are tuples in the format (X, gt_pseudo_label, Y)
    # If get_pseudo_label is set to False, the gt_pseudo_label in each tuple will be None.
@@ -38,7 +38,7 @@ In this example, we build a simple LeNet5 network as the base model.
 
 .. code:: python
 
-   from examples.models.nn import LeNet5
+   from models.nn import LeNet5
 
    # The number of pseudo-labels is 10
    cls = LeNet5(num_classes=10)

examples/hed/bridge.py

@@ -11,9 +11,9 @@ from abl.learning import ABLModel, BasicNN
 from abl.reasoning import Reasoner
 from abl.data.structures import ListData
 from abl.utils import print_log
-from examples.hed.datasets import get_pretrain_data
-from examples.hed.utils import InfiniteSampler, gen_mappings
-from examples.models.nn import SymbolNetAutoencoder
+from datasets import get_pretrain_data
+from utils import InfiniteSampler, gen_mappings
+from models.nn import SymbolNetAutoencoder
 
 
 class HedBridge(SimpleBridge):

examples/hed/hed.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,14 +23,13 @@
     "import torch.nn as nn\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.utils import ABLLogger, print_log\n",
-    "\n",
-    "from bridge import HedBridge\n",
-    "from consistency_metric import ConsistencyMetric\n",
     "from datasets import get_dataset, split_equation\n",
     "from models.nn import SymbolNet\n",
-    "from reasoning import HedKB, HedReasoner"
+    "from abl.learning import ABLModel, BasicNN\n",
+    "from reasoning import HedKB, HedReasoner\n",
+    "from consistency_metric import ConsistencyMetric\n",
+    "from abl.utils import ABLLogger, print_log\n",
+    "from bridge import HedBridge"
    ]
   },
   {
@@ -49,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -121,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -242,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [

examples/hed/main.py

@@ -4,14 +4,13 @@ import argparse
 import torch
 import torch.nn as nn
 
-from abl.learning import ABLModel, BasicNN
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-from abl.utils import ABLLogger, print_log
-
-from bridge import HedBridge
 from datasets import get_dataset, split_equation
 from models.nn import SymbolNet
+from abl.learning import ABLModel, BasicNN
 from reasoning import HedKB, HedReasoner
+from consistency_metric import ConsistencyMetric
+from abl.utils import ABLLogger, print_log
+from bridge import HedBridge
 
 
 def main():
@@ -82,7 +81,7 @@ def main():
     reasoner = HedReasoner(kb, dist_func="hamming", use_zoopt=True, max_revision=args.max_revision)
 
     ### Building Evaluation Metrics
-    metric_list = [SymbolAccuracy(prefix="hed"), ReasoningMetric(kb=kb, prefix="hed")]
+    metric_list = [ConsistencyMetric(kb=kb)]
 
     ### Bridge Learning and Reasoning
     bridge = HedBridge(model, reasoner, metric_list)

examples/hed/models/nn.py

@@ -1,16 +1,3 @@
-# coding: utf-8
-# ================================================================#
-#   Copyright (C) 2021 Freecss All rights reserved.
-#
-#   File Name     ：lenet5.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2021/03/03
-#   Description   ：
-#
-# ================================================================#
-
-
 import torch
 from torch import nn
 

examples/hwf/hwf.ipynb

@@ -24,14 +24,13 @@
     "import torch.nn as nn\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "from datasets import get_dataset\n",
+    "from models.nn import SymbolNet\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from abl.reasoning import KBBase, Reasoner\n",
     "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log\n",
-    "from abl.bridge import SimpleBridge\n",
-    "\n",
-    "from datasets import get_dataset\n",
-    "from models.nn import SymbolNet"
+    "from abl.bridge import SimpleBridge"
    ]
   },
   {

examples/hwf/main.py

@@ -1,19 +1,18 @@
-import os.path as osp
 import argparse
+import os.path as osp
 
 import numpy as np
 import torch
 from torch import nn
 
+from datasets import get_dataset
+from models.nn import SymbolNet
 from abl.learning import ABLModel, BasicNN
 from abl.reasoning import KBBase, GroundKB, Reasoner
 from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.utils import ABLLogger, print_log
 from abl.bridge import SimpleBridge
 
-from datasets import get_dataset
-from models.nn import SymbolNet
-
 
 class HwfKB(KBBase):
     def __init__(

examples/hwf/models/nn.py

@@ -1,16 +1,3 @@
-# coding: utf-8
-# ================================================================#
-#   Copyright (C) 2021 Freecss All rights reserved.
-#
-#   File Name     ：lenet5.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2021/03/03
-#   Description   ：
-#
-# ================================================================#
-
-
 import torch
 from torch import nn
 

examples/mnist_add/main.py

@@ -5,14 +5,13 @@ import torch
 from torch import nn
 from torch.optim import RMSprop, lr_scheduler
 
-from abl.bridge import SimpleBridge
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
+from datasets import get_dataset
+from models.nn import LeNet5
 from abl.learning import ABLModel, BasicNN
 from abl.reasoning import GroundKB, KBBase, PrologKB, Reasoner
+from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.utils import ABLLogger, print_log
-
-from datasets import get_dataset
-from models.nn import LeNet5
+from abl.bridge import SimpleBridge
 
 
 class AddKB(KBBase):

examples/mnist_add/mnist_add.ipynb

@@ -25,14 +25,13 @@
     "\n",
     "from torch.optim import RMSprop, lr_scheduler\n",
     "\n",
-    "from abl.bridge import SimpleBridge\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
+    "from datasets import get_dataset\n",
+    "from models.nn import LeNet5\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from abl.reasoning import KBBase, Reasoner\n",
+    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log\n",
-    "\n",
-    "from datasets import get_dataset\n",
-    "from models.nn import LeNet5"
+    "from abl.bridge import SimpleBridge"
    ]
   },
   {

examples/mnist_add/models/nn.py

@@ -1,16 +1,3 @@
-# coding: utf-8
-# ================================================================#
-#   Copyright (C) 2021 Freecss All rights reserved.
-#
-#   File Name     ：lenet5.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2021/03/03
-#   Description   ：
-#
-# ================================================================#
-
-
 import numpy as np
 import torch
 from torch import nn
@@ -45,50 +32,3 @@ class LeNet5(nn.Module):
         x = self.fc2(x)
         x = self.fc3(x)
         return x
-
-
-class SymbolNet(nn.Module):
-    def __init__(self, num_classes=4, image_size=(28, 28, 1)):
-        super(SymbolNet, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(1, 32, 5, stride=1),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.BatchNorm2d(32, momentum=0.99, eps=0.001),
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(32, 64, 5, padding=2, stride=1),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.BatchNorm2d(64, momentum=0.99, eps=0.001),
-        )
-
-        num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1)
-        self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU())
-        self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU())
-        self.fc3 = nn.Sequential(nn.Linear(84, num_classes))
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = torch.flatten(x, 1)
-        x = self.fc1(x)
-        x = self.fc2(x)
-        x = self.fc3(x)
-        return x
-
-
-class SymbolNetAutoencoder(nn.Module):
-    def __init__(self, num_classes=4, image_size=(28, 28, 1)):
-        super(SymbolNetAutoencoder, self).__init__()
-        self.base_model = SymbolNet(num_classes, image_size)
-        self.softmax = nn.Softmax(dim=1)
-        self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU())
-        self.fc2 = nn.Sequential(nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU())
-
-    def forward(self, x):
-        x = self.base_model(x)
-        # x = self.softmax(x)
-        x = self.fc1(x)
-        x = self.fc2(x)
-        return x

examples/models/nn.py

@@ -1,94 +0,0 @@
-# coding: utf-8
-# ================================================================#
-#   Copyright (C) 2021 Freecss All rights reserved.
-#
-#   File Name     ：lenet5.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2021/03/03
-#   Description   ：
-#
-# ================================================================#
-
-
-import numpy as np
-import torch
-from torch import nn
-
-
-class LeNet5(nn.Module):
-    def __init__(self, num_classes=10, image_size=(28, 28)):
-        super(LeNet5, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(1, 6, 3, padding=1),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(6, 16, 3), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)
-        )
-        self.conv3 = nn.Sequential(nn.Conv2d(16, 16, 3), nn.ReLU())
-
-        feature_map_size = (np.array(image_size) // 2 - 2) // 2 - 2
-        num_features = 16 * feature_map_size[0] * feature_map_size[1]
-
-        self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU())
-        self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU())
-        self.fc3 = nn.Linear(84, num_classes)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = self.conv3(x)
-        x = torch.flatten(x, 1)
-        x = self.fc1(x)
-        x = self.fc2(x)
-        x = self.fc3(x)
-        return x
-
-
-class SymbolNet(nn.Module):
-    def __init__(self, num_classes=4, image_size=(28, 28, 1)):
-        super(SymbolNet, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(1, 32, 5, stride=1),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.BatchNorm2d(32, momentum=0.99, eps=0.001),
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(32, 64, 5, padding=2, stride=1),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.BatchNorm2d(64, momentum=0.99, eps=0.001),
-        )
-
-        num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1)
-        self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU())
-        self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU())
-        self.fc3 = nn.Sequential(nn.Linear(84, num_classes))
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = torch.flatten(x, 1)
-        x = self.fc1(x)
-        x = self.fc2(x)
-        x = self.fc3(x)
-        return x
-
-
-class SymbolNetAutoencoder(nn.Module):
-    def __init__(self, num_classes=4, image_size=(28, 28, 1)):
-        super(SymbolNetAutoencoder, self).__init__()
-        self.base_model = SymbolNet(num_classes, image_size)
-        self.softmax = nn.Softmax(dim=1)
-        self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU())
-        self.fc2 = nn.Sequential(nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU())
-
-    def forward(self, x):
-        x = self.base_model(x)
-        # x = self.softmax(x)
-        x = self.fc1(x)
-        x = self.fc2(x)
-        return x

examples/zoo/main.py

@@ -4,9 +4,9 @@ import argparse
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 
-from examples.zoo.get_dataset import load_and_preprocess_dataset, split_dataset
+from get_dataset import load_and_preprocess_dataset, split_dataset
 from abl.learning import ABLModel
-from examples.zoo.kb import ZooKB
+from kb import ZooKB
 from abl.reasoning import Reasoner
 from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.utils import ABLLogger, print_log, confidence_dist

examples/zoo/zoo.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,9 +21,9 @@
     "import os.path as osp\n",
     "import numpy as np\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
-    "from examples.zoo.get_dataset import load_and_preprocess_dataset, split_dataset\n",
+    "from get_dataset import load_and_preprocess_dataset, split_dataset\n",
     "from abl.learning import ABLModel\n",
-    "from examples.zoo.kb import ZooKB\n",
+    "from kb import ZooKB\n",
     "from abl.reasoning import Reasoner\n",
     "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log, confidence_dist\n",
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -366,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.18"
   },
   "orig_nbformat": 4,
   "vscode": {