(Local) Iris Dataset
Introduction
This example will build out
features and train a simple logistic regression to classify flowers. We will use
the Iris data set to this purpose. We will highlight how StreamSQL can be used
locally. The local version will store and process all data on the machine it is
run on.
The Iris
Dataset
The Iris dataset can be
found here. It contains 150 rows in a
CSV with five columns. The columns are: sepal length,
sepal width, petal length, pedal width, and species. The four length and width
columns are numerical, whereas the species column is a categorical string with
three possible values: setosa, versicolor, and virginica.
Strategy
We will use
z-score
normalization on each of the numerical
columns to turn them into features and then feed them into the logistic regression
implementation in scikit learn.
The Model
Set up the
Environment
Make sure that Python and
PIP are installed on your local machine. Next, install StreamSQL's Python
client using pip. Check out the Getting
Started section for a more
thorough walk though in setting up StreamSQL on your machine.
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pip install
streamsql
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The iris dataset can be
downloaded here. The downloaded file is
named
iris.data and should be moved to
your working directory.
Load the data into
StreamSQL
We can initialize a local
feature store instance and then connect the iris dataset to it. The
LocalFeatureStore does not require an API
key and does not upload to a server, as its made for local development. When
uploading the file, we have to specify the format as CSV since it's not
implied by the .data ending.
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from
streamsql.local import
LocalFeatureStore
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​
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feat =
LocalFeaturestore()
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cols = ["sepal_w", "sepal_l", "petal_w", "petal_l", "species"]
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table = feat.upload_file(
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"./iris.data",
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name="iris",
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format="csv",
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columns=cols,
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)
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upload_file makes a
local copy of the file to guarantee immutability. That means that changes to
the file will not be reflected in the new table.
Define the Model
Features
Our model features are the
same as the first four columns in the CSV with z-score normalization
applied.
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from
streamsql.operation
import ZScore
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from
streamsql.feature import
NumericFeature
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​
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#
The features are the same as the CSV columns, excluding
species.
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features = cols[:-1]
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for col in features:
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feat.register_feature(
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NumericFeature(
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name=col,
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table=table,
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column=col,
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normalization=ZScore(),
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),
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)
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Generate a Training
Dataset
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dataset = feat.register_training_dataset(
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name="iris",
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label_table=table,
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label_field="species",
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features=features,
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)
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train, test = dataset.generate_training_data(
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training_size=0.9,
test_size=0.1, shuffle=True,
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)
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Train and Validate
Model
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from sklearn.linear_model
import
LogisticRegression
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​
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model =
LogisticRegression().fit(train.X, train.y)
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#
Validate that the model's performance against the training and
testing data.
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print(model.score(train.X, train.y))
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print(model.score(test.X, test.y))
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Full
Example
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from
streamsql.local import
LocalFeatureStore
2
from
streamsql.operation
import ZScore
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from
streamsql.feature import
NumericFeature
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from sklearn.linear_model
import
LogisticRegression
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​
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feat =
LocalFeaturestore()
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cols = ["sepal_w", "sepal_l", "petal_w", "petal_l", "species"]
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table = feat.upload_file(
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"./iris.data",
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name="iris",
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format="csv",
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columns=cols,
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)
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features = cols[:-1]
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for col in features:
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feat.register_feature(
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NumericFeature(
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name=col,
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table=table,
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column=col,
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normalization=ZScore(),
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),
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)
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dataset = feat.register_training_dataset(
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name="iris",
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label_table=table,
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label_field="species",
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features=features,
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)
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train, test = dataset.generate_training_data(
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training_size=0.9,
test_size=0.1, shuffle=True,
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)
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modified 1yr
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