rsp-ml 0.0.44

Machine Learning

Table of Contents

• 1 metrics
  • 1.1 F1_Score
  • 1.2 FN
  • 1.3 FP
  • 1.4 FPR
  • 1.5 TN
  • 1.6 TP
  • 1.7 TPR
  • 1.8 confusion
  • 1.9 confusion_matrix
  • 1.10 plot_confusion_matrix
  • 1.11 precision
  • 1.12 recall
  • 1.13 top_10_accuracy
  • 1.14 top_1_accuracy
  • 1.15 top_2_accuracy
  • 1.16 top_3_accuracy
  • 1.17 top_5_accuracy
  • 1.18 top_k_accuracy
• 2 model
  • 2.2 Constants
  • 2.1 load_model
• 3 multi_transforms
  • 3.1 BGR2GRAY
    • 3.1.1 __call__
    • 3.1.2 __init__
  • 3.2 BGR2RGB
    • 3.2.1 __call__
    • 3.2.2 __init__
  • 3.3 Brightness
    • 3.3.1 __call__
    • 3.3.2 __init__
  • 3.4 CenterCrop
    • 3.4.1 __call__
    • 3.4.2 __init__
  • 3.5 Color
    • 3.5.1 __call__
    • 3.5.2 __init__
  • 3.6 Compose
    • 3.6.1 __call__
    • 3.6.2 __init__
  • 3.7 GaussianNoise
    • 3.7.1 __call__
    • 3.7.2 __init__
  • 3.8 MultiTransform
    • 3.8.1 __call__
    • 3.8.2 __init__
  • 3.9 Normalize
    • 3.9.1 __call__
    • 3.9.2 __init__
  • 3.10 RGB2BGR
    • 3.10.1 __call__
    • 3.10.2 __init__
  • 3.11 RandomCrop
    • 3.11.1 __call__
    • 3.11.2 __init__
  • 3.12 RandomHorizontalFlip
    • 3.12.1 __call__
    • 3.12.2 __init__
  • 3.13 RandomVerticalFlip
    • 3.13.1 __call__
    • 3.13.2 __init__
  • 3.14 Resize
    • 3.14.1 __call__
    • 3.14.2 __init__
  • 3.15 Rotate
    • 3.15.1 __call__
    • 3.15.2 __init__
  • 3.16 Satturation
    • 3.16.1 __call__
    • 3.16.2 __init__
  • 3.17 Scale
    • 3.17.1 __call__
    • 3.17.2 __init__
  • 3.18 Stack
    • 3.18.1 __call__
    • 3.18.2 __init__
  • 3.19 ToCVImage
    • 3.19.1 __call__
    • 3.19.2 __init__
  • 3.20 ToNumpy
    • 3.20.1 __call__
    • 3.20.2 __init__
  • 3.21 ToPILImage
    • 3.21.1 __call__
    • 3.21.2 __init__
  • 3.22 ToTensor
    • 3.22.1 __call__
    • 3.22.2 __init__
• 4 run
  • 4.1 Run
    • 4.1.1 __init__
    • 4.1.2 append
    • 4.1.3 get_avg
    • 4.1.4 get_val
    • 4.1.5 len
    • 4.1.6 load_best_state_dict
    • 4.1.7 load_state_dict
    • 4.1.8 pickle_dump
    • 4.1.9 pickle_load
    • 4.1.10 plot
    • 4.1.11 recalculate_moving_average
    • 4.1.12 save
    • 4.1.13 save_best_state_dict
    • 4.1.14 save_state_dict

1 metrics

TOC

1.1 F1_Score

TOC

Description

F1 Score. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

F1 Score : float

Equations

$precision = \frac{TP}{TP + FP}$

$recall = \frac{TP}{TP + FN}$

$F_1 = \frac{2 \cdot precision \cdot recall}{precision + recall} = \frac{2 \cdot TP}{2 \cdot TP + FP + FN}$

Example

import rsp.ml.metrics as m

Y = torch.tensor([
    [0.87, 0.01, 0.05, 0.07],
    [0.02, 0.09, 0.86, 0.03]
  ])
T = torch.tensor([
    [1., 0., 0., 0.],
    [0., 1., 0., 0.]
  ])

f1score = m.F1_Score(Y, T)

print(f1score) --> 0.5

1.2 FN

TOC

Description

False negatives. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

False negatives : int

1.3 FP

TOC

Description

False positives. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

False positives : int

1.4 FPR

TOC

Description

False positive rate. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

False positive rate : float

1.5 TN

TOC

Description

True negatives. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

True negatives : int

1.6 TP

TOC

Description

True positives. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

True positives : int

1.7 TPR

TOC

Description

True positive rate. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

True positive rate : float

1.8 confusion

TOC

Description

Returns the confusion matrix for the values in the prediction and truth

tensors, i.e. the amount of positions where the values of prediction

and truth are

• 1 and 1 (True Positive)

• 1 and 0 (False Positive)

• 0 and 0 (True Negative)

• 0 and 1 (False Negative)

1.9 confusion_matrix

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Description

Calculates the confusion matrix. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Confusion matrix : torch.Tensor

1.10 plot_confusion_matrix

TOC

Description

Plot the confusion matrix

Parameters

Name	Type	Description
confusion_matrix	torch.Tensor	Confusion matrix
labels	str, optional, default = None	Class labels -> automatic labeling C000, ..., CXXX if labels is None
cmap	str, optional, default = 'Blues'	Seaborn cmap, see https://r02b.github.io/seaborn_palettes/
xlabel	str, optional, default = 'Predicted label'	X-Axis label
ylabel	str, optional, default = 'True label'	Y-Axis label
title	str, optional, default = 'Confusion Matrix'	Title of the plot
plt_show	bool, optional, default = False	Set to True to show the plot
save_file_name	str, optional, default = None	If not None, the plot is saved under the specified save_file_name.

Returns

Image of the confusion matrix : np.array

1.11 precision

TOC

Description

Precision. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Precision : float

Equations

$precision = \frac{TP}{TP + FP}$

1.12 recall

TOC

Description

Recall. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Recall : float

Equations

$recall = \frac{TP}{TP + FN}$

1.13 top_10_accuracy

TOC

Description

Top 10 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Top 10 accuracy -> top k accuracy | k = 10 : float

1.14 top_1_accuracy

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Description

Top 1 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Top 1 accuracy -> top k accuracy | k = 1 : float

1.15 top_2_accuracy

TOC

Description

Top 2 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Top 2 accuracy -> top k accuracy | k = 2 : float

1.16 top_3_accuracy

TOC

Description

Top 3 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Top 3 accuracy -> top k accuracy | k = 3 : float

1.17 top_5_accuracy

TOC

Description

Top 5 accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Top 5 accuracy -> top k accuracy | k = 5 : float

1.18 top_k_accuracy

TOC

Description

Top k accuracy. Expected input shape: (batch_size, num_classes)

Parameters

Name	Type	Description
Y	torch.Tensor	Prediction
T	torch.Tensor	True values

Returns

Top k accuracy : float

2 model

TOC

2.2 Constants

TOC

Name	Value	Description
TUC_ActionPrediction_model004	TUC/ActionPrediction/Model4	TUC Action prediction model 4 CNN with Multihead-Self-Attention Input - batch size - sequence length = 30 - channels = 3 - width = 200 - height = 200 Output - batch size - number of classes = 10
TUC_ActionPrediction_model005	TUC/ActionPrediction/Model5	TUC Action prediction model 5 CNN with Multihead-Self-Attention Input - batch size - sequence length = 30 - channels = 3 - width = 300 - height = 300 Output - batch size - number of classes = 10
URL	https://drive.google.com/drive/folders/1ulNnPqg-5wvenRl2CuJMxMMcaiYfHjQ9?usp=share_link	Google Drive URL

2.1 load_model

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Description

3 multi_transforms

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3.1 BGR2GRAY

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Description

Test Description

3.1.1 __call__

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Description

3.1.2 __init__

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Description

Initializes a new instance

3.2 BGR2RGB

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.2.1 __call__

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Description

3.2.2 __init__

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Description

Initializes a new instance

3.3 Brightness

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.3.1 __call__

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Description

3.3.2 __init__

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Description

Initializes a new instance

3.4 CenterCrop

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.4.1 __call__

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Description

3.4.2 __init__

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Description

Initializes a new instance

3.5 Color

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.5.1 __call__

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Description

3.5.2 __init__

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Description

Initializes a new instance

3.6 Compose

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3.6.1 __call__

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Description

Call self as a function.

3.6.2 __init__

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Description

Initialize self. See help(type(self)) for accurate signature.

3.7 GaussianNoise

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.7.1 __call__

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Description

3.7.2 __init__

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Description

Initializes a new instance

3.8 MultiTransform

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.8.1 __call__

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Description

3.8.2 __init__

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Description

Initializes a new instance

3.9 Normalize

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.9.1 __call__

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Description

3.9.2 __init__

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Description

Initializes a new instance

3.10 RGB2BGR

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.10.1 __call__

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Description

3.10.2 __init__

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Description

Initializes a new instance

3.11 RandomCrop

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.11.1 __call__

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Description

3.11.2 __init__

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Description

Test

3.12 RandomHorizontalFlip

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.12.1 __call__

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Description

3.12.2 __init__

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Description

Initializes a new instance

3.13 RandomVerticalFlip

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.13.1 __call__

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Description

3.13.2 __init__

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Description

Initializes a new instance

3.14 Resize

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.14.1 __call__

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Description

3.14.2 __init__

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Description

Initializes a new instance

3.15 Rotate

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.15.1 __call__

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Description

3.15.2 __init__

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Description

Initializes a new instance

3.16 Satturation

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.16.1 __call__

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Description

3.16.2 __init__

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Description

Initializes a new instance

3.17 Scale

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.17.1 __call__

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Description

3.17.2 __init__

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Description

Initializes a new instance

3.18 Stack

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.18.1 __call__

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Description

3.18.2 __init__

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Description

Initializes a new instance

3.19 ToCVImage

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.19.1 __call__

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Description

3.19.2 __init__

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Description

Initializes a new instance

3.20 ToNumpy

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.20.1 __call__

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Description

3.20.2 __init__

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Description

Initializes a new instance

3.21 ToPILImage

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.21.1 __call__

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Description

3.21.2 __init__

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Description

Initializes a new instance

3.22 ToTensor

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Description

MultiTransform is an extension to keep the same transformation over a sequence of images instead of initializing a new transformation for every single image. It is inspired by torchvision.transforms and could be used for video augmentation. Use rsp.ml.multi_transforms.Composeto combine multiple image sequence transformations.

Note rsp.ml.multi_transforms.MultiTransform is a base class and should be inherited.

3.22.1 __call__

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Description

3.22.2 __init__

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Description

Initializes a new instance

4 run

TOC

4.1 Run

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4.1.1 __init__

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Description

Initialize self. See help(type(self)) for accurate signature.

4.1.2 append

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Description

4.1.3 get_avg

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Description

4.1.4 get_val

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Description

4.1.5 len

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Description

4.1.6 load_best_state_dict

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Description

4.1.7 load_state_dict

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Description

4.1.8 pickle_dump

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Description

4.1.9 pickle_load

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Description

4.1.10 plot

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Description

4.1.11 recalculate_moving_average

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Description

4.1.12 save

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Description

4.1.13 save_best_state_dict

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Description

4.1.14 save_state_dict

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Description