BEAT - amohammadi/mnist

This algorithm is a sequential one. The platform will call its process() method once per data incoming on its inputs.

Endpoint Groups 1

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name	Data Format	Nature
image	system/array_2d_uint8/1	Input
label	system/uint64/1	Input
model	system/text/1	Output

import pickle
import base64
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.utils import check_array

def reshape(X):
    X = check_array(X, allow_nd=True)
    return X.reshape((X.shape[0], -1))

class Algorithm:
    def __init__(self):
        self.images = []
        self.labels = []
        self.estimator = make_pipeline(
            StandardScaler(),
            LogisticRegression(
                C=50. / 60000, penalty='l1', solver='saga', tol=0.1
            ),
        )

def process(self, inputs, data_loaders, outputs):
        # accumulate images and labels
        self.images.append(inputs["image"].data.value)
        self.labels.append(inputs["label"].data.value)

if not (inputs["image"].hasMoreData()):
            # train the estimator when all data is accumulated
            X = reshape(self.images)
            self.estimator.fit(X, self.labels)
            # pickle and output estimator
            out = pickle.dumps(self.estimator)
            # stringify the output since BEAT does not yet support byte type as output
            out = base64.b64encode(out).decode("ascii")
            outputs["model"].write({"text": out})
            self.images = []
            self.labels = []

return True

xxxxxxxxxx
 
import pickle
import base64
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.utils import check_array
​
def reshape(X):
    X = check_array(X, allow_nd=True)
    return X.reshape((X.shape[0], -1))
​
​
class Algorithm:
    def __init__(self):
        self.images = []
        self.labels = []
        self.estimator = make_pipeline(
            StandardScaler(),
            LogisticRegression(
                C=50. / 60000, penalty='l1', solver='saga', tol=0.1
            ),
        )
​
    def process(self, inputs, data_loaders, outputs):
        # accumulate images and labels
        self.images.append(inputs["image"].data.value)
        self.labels.append(inputs["label"].data.value)
​
        if not (inputs["image"].hasMoreData()):
            # train the estimator when all data is accumulated
            X = reshape(self.images)
            self.estimator.fit(X, self.labels)
            # pickle and output estimator
            out = pickle.dumps(self.estimator)
            # stringify the output since BEAT does not yet support byte type as output
            out = base64.b64encode(out).decode("ascii")
            outputs["model"].write({"text": out})
            self.images = []
            self.labels = []
​
        return True
​

The code for this algorithm in Python
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Experiments

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Status:

			Updated	Name	Databases/Protocols	Analyzers
				amohammadi/amohammadi/mnist_simple/1/mnist1	mnist/5@idiap	amohammadi/accuracy_analyzer/1

Python (2.1.0) 1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

algorithms amohammadi mnist_trainer 1

Endpoint Groups 1

Group: main

Experiments

algorithms

amohammadi

mnist_trainer

1