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George Mihaila
2020-10-26 17:22:27 -05:00
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@@ -44,7 +44,13 @@ pip install git+https://github.com/gmihaila/ml_things
## Functions
### pad_array [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/array_functions.py#L21)
All function implemented in the **ml_things** module.
### Array Functions
Array manipulation related function that can be useful when working with machine learning.
#### pad_array [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/array_functions.py#L21)
Pad variable length array to a fixed numpy array. It can handle single arrays [1,2,3] or nested arrays [[1,2],[3]].
@@ -67,7 +73,7 @@ array([[ 1., 2., 99., 99., 99.],
[ 4., 5., 6., 99., 99.]])
```
### batch_array [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/array_functions.py#L120)
#### batch_array [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/array_functions.py#L120)
Split a list into batches/chunks. Last batch size is remaining of list values.
**Note:** *This is also called chunking. I call it batches since I use it more in ML.*
@@ -80,13 +86,16 @@ The last batch will be the reamining values:
[[1, 2, 3, 4], [5, 6, 7, 8], [8, 9, 8, 6], [5, 4, 6]]
```
### Plot Functions
Plot related function that can be useful when working with machine learning.
### plot_array [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/plot_functions.py#L23)
#### plot_array [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/plot_functions.py#L23)
Create plot from a single array of values.
All arguments are optimized for quick plots:
All arguments are optimized for quick plots. Change the `magnify` arguments to vary the size of the plot:
```python
>>> from ml_things import plot_array
@@ -96,21 +105,66 @@ All arguments are optimized for quick plots:
![plot_array](https://github.com/gmihaila/ml_things/raw/master/tests/test_samples/plot_array.png)
### plot_dict [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/plot_functions.py#L183)
#### plot_dict [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/plot_functions.py#L183)
Create plot from a single array of values.
All arguments are optimized for quick plots. Change the `magnify` arguments to vary the size of the plot:
```python
>>> from ml_things import plot_dict
>>> plot_dict({'train_acc':[1,3,5,3,7,5,8,10],
'valid_acc':[4,8,9]}, use_linestyles=['-', '--'], magnify=0.5,
start_step=0.3, step_size=0.1,path='plot_dict.png', points_values=[True, False])
```
![plot_dict](https://github.com/gmihaila/ml_things/raw/efb2574a9935c6a6ef62135efba2d965b2044175/tests/test_samples/plot_dict.png)
#### plot_confusion_matrix [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/plot_functions.py#L360)
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`.
All arguments are optimized for quick plots. Change the `magnify` arguments to vary the size of the plot:
```python
>>> from ml_things import plot_confusion_matrix
>>> plot_confusion_matrix(y_true=[1,0,1,1,0,1], y_pred=[0,1,1,1,0,1], magnify=0.5, use_title='My Confusion Matrix', path='plot_confusion_matrix.png');
Confusion matrix, without normalization
array([[1, 1],
[1, 3]])
```
![plot_confusion_matrix](https://github.com/gmihaila/ml_things/raw/efb2574a9935c6a6ef62135efba2d965b2044175/tests/test_samples/plot_confusion_matrix.png)
### Text Functions
Text related function that can be useful when working with machine learning.
#### clean_text [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/text_functions.py#L22)
### plot_confusion_matrix [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/plot_functions.py#L360)
Clean text using various techniques:
```python
>>> from ml_things import clean_text
>>> clean_text("ThIs is $$$%. \t\t\n \\ so dirtyyy$$ text :'(. omg!!!", full_clean=True)
'this is so dirtyyy text omg'
```
### Web Related
### clean_text [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/text_functions.py#L22)
Web related function that can be useful when working with machine learning.
#### download_from [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/web_related.py#L21)
Download file from url. It will return the path of the downloaded file:
### download_from [[source]](https://github.com/gmihaila/ml_things/blob/efb2574a9935c6a6ef62135efba2d965b2044175/src/ml_things/web_related.py#L21)
```python
>>> from ml_things import download_from
>>> download_from(url='https://raw.githubusercontent.com/gmihaila/ml_things/master/setup.py', path='.')
'./setup.py'
```
<br>