Best Python Libraries For Machine Learning And Deep Learning

Cogitare is a Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python. A friendly interface for beginners and a powerful toolset for experts.

Cogitare is built on top of PyTorch.

1. About

It uses the best of PyTorch, Dask, NumPy, and others tools through a simple interface to train, to evaluate, to test models and more.

With Cogitare, you can use classical machine learning algorithms with high performance and develop state-of-the-art models quickly.

Check the tutorials at http://tutorials.cogitare-ai.org/

The primary objectives of Cogitare are:

• provide an easy-to-use interface to train and evaluate models;
• provide tools to debug and analyze the model;
• provide implementations of state-of-the-art models (models for common tasks, ready to train and ready to use);
• provide ready-to-use implementations of straightforward and classical models (such as LogisticRegression);
• be compatible with models for a broad range of problems;
• be compatible with other tools (scikit-learn, etcs);
• keep growing with the community: accept as many new features as possible;
• provide a friendly interface to beginners, and powerful features for experts;
• take the best of the hardware through multi-processing and multi-threading;
• and others.

Currently, it's a work in progress project that aims to provide a complete toolchain for machine learning and deep learning development, taking the best of cuda and multi-core processing.

2. Install

Install PyTorch from http://pytorch.org/

Install Cogitare from PIP:

pip install cogitare

Cogitare is in active development, so it's recommended to get the latest version from GitHub. To install directly from GitHub, use:

pip install -e git+https://github.com/cogitare-ai/cogitare#egg=cogitare

3. Quickstart

This is a simple tutorial to get started with Cogitare main functionalities.

In this tutorial, we will write a Convolutional Neural Network (CNN) to classify handwritten digits (MNIST).

3.1 Model

We start by defining our CNN model.

When developing a model with Cogitare, your model must extend the cogitare.Model class. This class provides the Model interface, which allows you to train and evaluate the model efficiently.

To implement a model, you must extend the cogitare.Model class and implement the forward() and loss() methods. The forward method will receive the batch. In this way, it is necessary to implement the forward pass through the network in this method, and then return the output of the net. The loss method will receive the output of the forward() and the batch received from the iterator, apply a loss function, compute and return it.

The Model interface will iterate over the dataset, and execute each batch on forward, loss, and backward.

# adapted from https://github.com/pytorch/examples/blob/master/mnist/main.py
from cogitare import Model
from cogitare import utils
from cogitare.data import DataSet, AsyncDataLoader
from cogitare.plugins import EarlyStopping
from cogitare.metrics.classification import accuracy
import cogitare

import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.nn.utils import clip_grad_norm
import torch.optim as optim

from sklearn.datasets import fetch_mldata

import numpy as np

CUDA = True


cogitare.utils.set_cuda(CUDA)

class CNN(Model):
    
    def __init__(self):
        super(CNN, self).__init__()
        
        # define the model
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
    
    def forward(self, batch):
        # in this sample, each batch will be a tuple containing (input_batch, expected_batch)
        # in forward in are only interested in input so that we can ignore the second item of the tuple
        input, _ = batch
        
        # batch X flat tensor -> batch X 1 channel (gray) X width X heigth
        input = input.view(32, 1, 28, 28)
        
        # pass the data in the net
        x = F.relu(F.max_pool2d(self.conv1(input), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)

        # return the model output
        return F.log_softmax(x, dim=1)
    
    def loss(self, output, batch):
        # in this sample, each batch will be a tuple containing (input_batch, expected_batch)
        # in loss in are only interested in expected so that we can ignore the first item of the tuple
        _, expected = batch
        
        return F.nll_loss(output, expected)

The model class is simple; it only requires de forward and loss methods. By default, Cogitare will backward the loss returned by the loss() method, and optimize the model parameters. If you want to disable the Cogitare backward and optimization steps, just return None in the loss function. If you return None, you are responsible by backwarding and optimizing the parameters.

3.2 Data Loading

In this step, we will load the data from sklearn package.

mnist = fetch_mldata('MNIST original')
mnist.data = (mnist.data / 255).astype(np.float32)

Cogitare provides a toolbox to load and pre-process data for your models. In this introduction, we will use the DataSet and the AsyncDataLoader as examples.

The DataSet is responsible by iterating over multiples data iterators (in our case, we'll have two data iterators: input samples, expected samples).

# as input, the DataSet is expected a list of iterators. In our case, the first iterator is the input 
# data and the second iterator is the target data

# also, we set the batch size to 32 and enable the shuffling

# drop the last batch if its size is different of 32
data = DataSet([mnist.data, mnist.target.astype(int)], batch_size=32, shuffle=True, drop_last=True)

# then, we split our dataset into a train and into a validation sets, by a ratio of 0.8
data_train, data_validation = data.split(0.8)

Notice that Cogitare accepts any iterator as input. Instead of using our DataSet, you can use the mnist.data itself, PyTorch's data loaders, or any other input that acts as an iterator.

In some cases, we can increase the model performance by loading the data using multiples threads/processes or by pre-loading the data before being requested by the model.

With the AsyncDataLoader, we can load N batches ahead of the model execution in parallel. We present this technique in this sample because it can increase performance in a wide range of models (when the data loading or pre-processing is slower than the model execution).

def pre_process(batch):
    input, expected = batch
    
    # the data is a numpy.ndarray (loaded from sklearn), so we need to convert it to Variable
    input = utils.to_variable(input, dtype=torch.FloatTensor)  # converts to a torch Variable of LongTensor
    expected = utils.to_variable(expected, dtype=torch.LongTensor)  # converts to a torch Variable of LongTensor
    return input, expected


# we wrap our data_train and data_validation iterators over the async data loader.
# each loader will load 16 batches ahead of the model execution using 8 workers (8 threads, in this case).
# for each batch, it will be pre-processed in parallel with the preprocess function, that will load the data
# on GPU
data_train = AsyncDataLoader(data_train, buffer_size=16, mode='threaded', workers=8, on_batch_loaded=pre_process)
data_validation = AsyncDataLoader(data_validation, buffer_size=16, mode='threaded', workers=8, on_batch_loaded=pre_process)

to cache the async buffer before training, we can:

data_train.cache()
data_validation.cache()

3.3 Training

Now, we can train our model.

First, lets create the model instance and add the default plugins to watch the training status. The default plugin includes:

• Progress bar per batch and epoch
• Plot training and validation losses (if validation_dataset is present)
• Log training loss

model = CNN()
model.register_default_plugins()

Besides that, we may want to add some extra plugins, such as the EarlyStopping. So, if the model is not decreasing the loss after N epochs, the training stops and the best model is used.

To add the early stopping algorithm, you can use:

early = EarlyStopping(max_tries=10, path='/tmp/model.pt')
# after 10 epochs without decreasing the loss, stop the training and the best model is saved at /tmp/model.pt

# the plugin will execute in the end of each epoch
model.register_plugin(early, 'on_end_epoch')

Also, a common technique is to clip the gradient during training. If you want to clip the grad, you can use:

model.register_plugin(lambda *args, **kw: clip_grad_norm(model.parameters(), 1.0), 'before_step')
# will execute the clip_grad_norm before each optimization step

Now, we define the optimizator, and then start the model training:

optimizer = optim.Adam(model.parameters(), lr=0.001)

if CUDA:
    model = model.cuda()
model.learn(data_train, optimizer, data_validation, max_epochs=100)

2018-02-02 20:59:23 sprawl cogitare.core.model[2443] INFO Model: 

CNN(
  (conv1): Conv2d (1, 10, kernel_size=(5, 5), stride=(1, 1))
  (conv2): Conv2d (10, 20, kernel_size=(5, 5), stride=(1, 1))
  (conv2_drop): Dropout2d(p=0.5)
  (fc1): Linear(in_features=320, out_features=50)
  (fc2): Linear(in_features=50, out_features=10)
)

2018-02-02 20:59:23 sprawl cogitare.core.model[2443] INFO Training data: 

DataSet with:
    containers: [
        TensorHolder with 1750x32 samples
	TensorHolder with 1750x32 samples
    ],
    batch size: 32


2018-02-02 20:59:23 sprawl cogitare.core.model[2443] INFO Number of trainable parameters: 21,840
2018-02-02 20:59:23 sprawl cogitare.core.model[2443] INFO Number of non-trainable parameters: 0
2018-02-02 20:59:23 sprawl cogitare.core.model[2443] INFO Total number of parameters: 21,840
2018-02-02 20:59:23 sprawl cogitare.core.model[2443] INFO Starting the training ...
2018-02-02 21:02:04 sprawl cogitare.core.model[2443] INFO Training finished

Stopping training after 10 tries. Best score 0.0909
Model restored from: /tmp/model.pt

 

To check the model loss and accuracy on the validation dataset:

def model_accuracy(output, data):
    _, indices = torch.max(output, 1)
    
    return accuracy(indices, data[1])

# evaluate the model loss and accuracy over the validation dataset
metrics = model.evaluate_with_metrics(data_validation, {'loss': model.metric_loss, 'accuracy': model_accuracy})

# the metrics is an dict mapping the metric name (loss or accuracy, in this sample) to a list of the accuracy output
# we have a measurement per batch. So, to have a value of the full dataset, we take the mean value:

metrics_mean = {'loss': 0, 'accuracy': 0}
for loss, acc in zip(metrics['loss'], metrics['accuracy']):
    metrics_mean['loss'] += loss
    metrics_mean['accuracy'] += acc.item()

qtd = len(metrics['loss'])

print('Loss: {}'.format(metrics_mean['loss'] / qtd))
print('Accuracy: {}'.format(metrics_mean['accuracy'] / qtd))

Loss: 0.10143917564566948
Accuracy: 0.9846252860411899

One of the advantages of Cogitare is the plug-and-play APIs, which let you add/remove functionalities easily. With this sample, we trained a model with training progress bar, error plotting, early stopping, grad clipping, and model evaluation easily.

4. Contribution

Cogitare is a work in progress project, and any contribution is welcome.

You can contribute testing and providing bug reports, proposing feature ideas, fixing bugs, pushing code, etcs.

1. You want to propose a new Feature and implement it
   • post about your intended feature, and we shall discuss the design and implementation. Once we agree that the plan looks good, go ahead and implement it.
2. You want to implement a feature or bug-fix for an outstanding issue
   • Look at the outstanding issues here: https://github.com/cogitare-ai/cogitare/issues
   • Pick an issue and comment on the task that you want to work on this feature
   • If you need more context on a particular issue, please ask and we shall provide.

Once you finish implementing a feature or bugfix, please send a Pull Request to https://github.com/cogitare-ai/cogitare

If you are not familiar with creating a Pull Request, here are some guides:

• http://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request
• https://help.github.com/articles/creating-a-pull-request/

---

Author: cogitare-ai
Source Code: https://github.com/cogitare-ai/cogitare
License: MIT License

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Introduction

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If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

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The Iris dataset is easily one of the most popular machine learning projects in Python. It is relatively small, but its simplicity and compact size make it perfect for beginners. If you haven’t worked on any machine learning projects in Python, you should start with it. The Iris dataset is a collection of flower sepal and petal sizes of the flower Iris. It has three classes, with 50 instances in every one of them.

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