model: scratch: Create Linear Regression model

[pdxjohnny]

Assignee: @yashlamba

./scripts/create.sh model scratch

Then fill out model with your implementation of linear regression. (Maybe linear.py)

[yashlamba]

dffml/model/tensorflow/dffml_model_tensorflow/model/dnn.py

Lines 149 to 187 in b6e45e8

	async def features(self, features: Features):
	'''
	Converts repos into training data
	'''
	cols: Dict[str, Any] = {}
	for feature in features:
	col = self.feature_feature_column(feature)
	if not col is None:
	cols[feature.NAME] = col
	return cols
	def feature_feature_column(self, feature: Feature):
	'''
	Creates a feature column for a feature
	'''
	dtype = feature.dtype()
	if not inspect.isclass(dtype):
	LOGGER.warning('Unknown dtype %r. Cound not create column' % (dtype))
	return None
	if dtype is int or issubclass(dtype, int) \
	or dtype is float or issubclass(dtype, float):
	return tensorflow.feature_column.numeric_column(feature.NAME,
	shape=feature.length())
	LOGGER.warning('Unknown dtype %r. Cound not create column' % (dtype))
	return None
	def model_dir_path(self, features: Features):
	'''
	Creates the path to the model dir by using the provided model dir and
	the sha384 hash of the concatenated feature names.
	'''
	if self.parent.config.directory is None:
	return None
	model = hashlib.sha384(''.join(features.names()).encode('utf-8'))\
	.hexdigest()
	if not os.path.isdir(self.parent.config.directory):
	raise NotADirectoryError('%s is not a directory' % (self.parent.config.directory))
	return os.path.join(self.parent.config.directory, model)

@pdxjohnny I was implementing applicable features and found it leaded to the following functions. Do they need to be re-implemented or can I pick them up from dnn and change the conditions (for starters the feature length should be 2 and so on)

[yashlamba]

And can you suggest me how to debug my code like how can I go about testing (for now checking that whether I have received data successfully or not)?

[pdxjohnny]

Here's an outline for 2.a.iv and 2.a.v from https://docs.google.com/document/d/16u9Tev3O0CcUDe2nfikHmrO3Xnd4ASJ45myFgQLpvzM/edit#heading=h.s3lkoesyhz9v

Is this what you're talking about?

class SimpleLinearRegression(Model):
    async def applicable_features(self, features):
        if len(features) != 1:
            raise ValueError("simple LR only supports a single feature")
        if features[0].dtype() != int and features[0].dtype() != float:
            raise ValueError("simple LR only supports int or float feature")
        if features[0].length() != 1:
            raise ValueError("simple LR only supports single values (non-matrix / array)")
        features_we_care_about = [features[0].NAME]
        return features_we_care_about

    async def train(self, sources, features):
        features_we_care_about = self.applicable_features(features)
        async for repo in sources.with_features(features_we_care_about):
            # Grab a subset of the feature data being stored within the repo
            # The subset is the feature_we_care_about and the feature we are want to predict
            feature_data = repo.features(features_we_care_about + [self.parent.config.predict])
            xData.append(feature_data[features_we_care_about[0]])
            yData.append(feature_data[self.parent.config.predict])

[yashlamba]

Here's an outline for 2.a.iv and 2.a.v from https://docs.google.com/document/d/16u9Tev3O0CcUDe2nfikHmrO3Xnd4ASJ45myFgQLpvzM/edit#heading=h.s3lkoesyhz9v

Is this what you're talking about?

class SimpleLinearRegression(Model):
    async def applicable_features(self, features):
        if len(features) != 1:
            raise ValueError("simple LR only supports a single feature")
        if features[0].dtype() != int and features[0].dtype() != float:
            raise ValueError("simple LR only supports int or float feature")
        if features[0].length() != 1:
            raise ValueError("simple LR only supports single values (non-matrix / array)")
        features_we_care_about = [features[0].NAME]
        return features_we_care_about

    async def train(self, sources, features):
        features_we_care_about = self.applicable_features(features)
        async for repo in sources.with_features(features_we_care_about):
            # Grab a subset of the feature data being stored within the repo
            # The subset is the feature_we_care_about and the feature we are want to predict
            feature_data = repo.features(features_we_care_about + [self.parent.config.predict])
            xData.append(feature_data[features_we_care_about[0]])
            yData.append(feature_data[self.parent.config.predict])

well, this is pretty simple and understandable, I was using dnn as my reference wherein applicable features lead to self.features which in turn lead to feature_feature_column (which ultimately checked the types).

[pdxjohnny]

ya dnn is complicated by tensorflow APIs :)