Data Cleanup Operations¶

In this example we are going to perform cleanup operations on a real-world dataset.

In this example we will perform the following steps

Writing cleanup operations dataflow
Using merge command to see the preprocessed data
Training our model on the preprocessed data
Getting the accuracy of the model

First install the data cleanup operations and scikit models

$ python -m pip install dffml-operations-data dffml-model-scikit

Dataset¶

The dataset we will be using is available on kaggle https://www.kaggle.com/uciml/mushroom-classification

you may go ahead and download the dataset

$ curl -fLO https://github.com/intel/dffml/files/7040983/mushrooms.csv

Data Cleanup Operations¶

We will be performing two cleanup operations on our dataset - ordinal_encoder , As seen in the dataset all the values are categorical values haivng a similar relationship. So we can convert these categorical values into numerical values.

Data Cleanup¶

** cleanup_ops.sh **

$ dffml dataflow create \
    -config \
        "mushrooms.csv"=convert_records_to_list.source.config.filename \
        csv=convert_records_to_list.source.plugin \
    -inputs \
        '["records"]'=get_single_spec \
        '["class","cap-shape","cap-surface","cap-color","bruises","odor","gill-attachment","gill-spacing","gill-size","gill-color","stalk-shape","stalk-root","stalk-surface-above-ring","stalk-surface-below-ring","stalk-color-above-ring","stalk-color-below-ring","veil-type","veil-color","ring-number","ring-type","spore-print-color","population","habitat"]'=features \
        '[]'=predict_features \
    -flow \
        '[{"convert_records_to_list": "matrix"}]'=ordinal_encoder.inputs.data \
        '[{"ordinal_encoder": "result"}]'=convert_list_to_records.inputs.matrix \
        '[{"convert_records_to_list": "keys"}]'=convert_list_to_records.inputs.keys \
        --\
            convert_list_to_records \
            convert_records_to_list \
            ordinal_encoder \
            get_single | \
    tee clean_ops.json

Data Merge¶

To have a look at the preprocessed data, we can use the merge command.

** merge.sh **

$ dffml merge text=df temp=csv \
    -source-text-dataflow clean_ops.json \
    -source-text-features class:float:1 cap-shape:float:1 cap-surface:float:1 cap-color:float:1 bruises:float:1 odor:float:1 gill-attachment:float:1 gill-spacing:float:1 gill-size:float:1 gill-color:float:1 stalk-shape:float:1 stalk-root:float:1 stalk-surface-above-ring:float:1 stalk-surface-below-ring:float:1 stalk-color-above-ring:float:1 stalk-color-below-ring:float:1 veil-type:float:1 veil-color:float:1 ring-number:float:1 ring-type:float:1 spore-print-color:float:1 population:float:1 habitat:float:1 \
    -source-text-source csv \
    -source-text-source-filename mushrooms.csv \
    -source-temp-filename preprocessed.csv \
    -source-temp-allowempty \
    -source-temp-readwrite \
    -log debug

$ cat preprocessed.csv

Training¶

Now we will be training our model using the preprocessed dataset

** train.sh **

$ dffml train \
    -model scikitmnb \
    -model-features cap-shape:float:1 cap-surface:float:1 cap-color:float:1 bruises:float:1 odor:float:1 gill-attachment:float:1 gill-spacing:float:1 gill-size:float:1 gill-color:float:1 stalk-shape:float:1 stalk-root:float:1 stalk-surface-above-ring:float:1 stalk-surface-below-ring:float:1 stalk-color-above-ring:float:1 stalk-color-below-ring:float:1 veil-type:float:1 veil-color:float:1 ring-number:float:1 ring-type:float:1 spore-print-color:float:1 population:float:1 habitat:float:1 \
    -model-predict class:str:1 \
    -model-location tempdir \
    -sources f=csv \
    -source-filename preprocessed.csv \
    -log debug

Accuracy¶

After training the model we can now look for accuracy of the trained model

** accuracy.sh **

$ dffml accuracy \
    -model scikitmnb \
    -scorer logloss \
    -features class:str:1 \
    -model-features cap-shape:float:1 cap-surface:float:1 cap-color:float:1 bruises:float:1 odor:float:1 gill-attachment:float:1 gill-spacing:float:1 gill-size:float:1 gill-color:float:1 stalk-shape:float:1 stalk-root:float:1 stalk-surface-above-ring:float:1 stalk-surface-below-ring:float:1 stalk-color-above-ring:float:1 stalk-color-below-ring:float:1 veil-type:float:1 veil-color:float:1 ring-number:float:1 ring-type:float:1 spore-print-color:float:1 population:float:1 habitat:float:1 \
    -model-predict class:str:1 \
    -model-location tempdir \
    -sources f=csv \
    -source-filename preprocessed.csv \
    -log debug

Conclusion¶

Thus, we performed cleanup operations on a classfication dataset.