Introduction to Typer

typer-doc.py

@app.command()
def elbow(file_path: str, max_clusters: int = 10):
    """
    This command will run K-Means algorithm many times and print the inertia values to the console.
    """
    ...

typer-elbow-progress.py

@app.command()
def elbow(file_path: str, max_clusters: int = 10):
    errors = []  # Create an empty list to collect inertias

    df = pd.read_csv(file_path)

    # Loop through some desirable number of clusters
    # The KMeans algorithm's fit method returns a property called inertia_ that has the information we need
    with typer.progressbar(range(2, max_clusters)) as progress:
        for k in progress:
            time.sleep(1)
            errors.append(
                {
                    "inertia": KMeans(n_clusters=k, random_state=0)
                    .fit(df[["Age", "Income", "Debt"]])
                    .inertia_,
                    "num_clusters": k,
                }
            )

    # for convenience convert the list to a pandas dataframe
    df_inertia = pd.DataFrame(errors)

    typer.echo(df_inertia)

typer-elbow.py

@app.command()
def elbow(file_path: str, max_clusters:int=10):
    errors = []  # Create an empty list to collect inertias

    df = pd.read_csv(file_path)

    # Loop through some desirable number of clusters
    # The KMeans algorithm's fit method returns a property called inertia_ that has the information we need
    for k in range(2, max_clusters):
        time.sleep(1)
        errors.append(
            {
                "inertia": KMeans(n_clusters=k, random_state=0)
                .fit(df[["Age", "Income", "Debt"]])
                .inertia_,
                "num_clusters": k,
            }
        )

    # for convenience convert the list to a pandas dataframe
    df_inertia = pd.DataFrame(errors)

    typer.echo(df_inertia)

typer-hello-world.py

import typer


def say_hello():
    typer.secho(f"Hello World!", fg=typer.colors.WHITE, bg=typer.colors.BLUE)


if __name__ == "__main__":
    typer.run(say_hello)

typer-kmeans.py

import typer

import pandas as pd  # Pandas for reading data
from sklearn.cluster import KMeans  # KMeans Clustering itself
from joblib import dump, load  # Required to persist the trained model

app = typer.Typer()


@app.command()
def train(file_path: str):
    typer.secho(f"Training K-Means with file {file_path}")

    # Code for training K-Means clustering
    df = pd.read_csv(file_path)
    kmeans = KMeans(n_clusters=2, random_state=0).fit(df[["Age", "Income"]])

    typer.secho("CLUSTER CENTERS")
    typer.echo(kmeans.cluster_centers_)

    dump(
        kmeans, "model.joblib"
    )  # This line is to store the model to use in predictions later.


@app.command()
def predict():
    typer.secho("Will be implemented soon")
    pass


if __name__ == "__main__":
    app()

typer-multiple.py

import typer

app = typer.Typer()


@app.command()
def say_hello():
    typer.secho(f"Hello World!", fg=typer.colors.WHITE, bg=typer.colors.BLUE)


@app.command()
def say_hello_in_red():
    typer.secho(f"Hello World!", fg=typer.colors.WHITE, bg=typer.colors.RED)


if __name__ == "__main__":
    app()