simpleimputer_intro.py

# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
#     http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is distributed on
# an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.

from datawig import SimpleImputer
from datawig.utils import random_split
from sklearn.metrics import f1_score, classification_report
import pandas as pd

"""
Load Data
"""
df = pd.read_csv('mae_train_dataset.csv').sample(n=1000)
df_train, df_test = random_split(df, split_ratios=[0.8, 0.2])

# ------------------------------------------------------------------------------------

"""
Run default SimpleImputer
"""
# Initialize a SimpleImputer model
imputer = SimpleImputer(
    input_columns=['title', 'text'],  # columns containing information about the column we want to impute
    output_column='finish',  # the column we'd like to impute values for
    output_path='imputer_model'  # stores model data and metrics
)

# Fit an imputer model on the train data
imputer.fit(train_df=df_train, num_epochs=5)

# Impute missing values and return original dataframe with predictions
predictions = imputer.predict(df_test)

# Calculate f1 score for true vs predicted values
f1 = f1_score(predictions['finish'], predictions['finish_imputed'], average='weighted')

# Print overall classification report
print(classification_report(predictions['finish'], predictions['finish_imputed']))

# ------------------------------------------------------------------------------------

"""
Run SimpleImputer with hyperparameter optimization
"""
# Initialize a SimpleImputer model
imputer = SimpleImputer(
    input_columns=['title', 'text'],
    output_column='finish',
    output_path='imputer_model'
)

# Fit an imputer model with default list of hyperparameters
imputer.fit_hpo(train_df=df_train)

# Fit an imputer model with customized HPO
imputer.fit_hpo(
    train_df=df_train,
    num_epochs=5,
    patience=3,
    learning_rate_candidates=[1e-3, 1e-4],
    num_hash_bucket_candidates=[2 ** 15],
    tokens_candidates=['words', 'chars']
)

# ------------------------------------------------------------------------------------

"""
Load saved model and get metrics from SimpleImputer
"""
# Load saved model
imputer = SimpleImputer.load('./imputer_model')

# Load a dictionary of metrics from the validation set
metrics = imputer.load_metrics()
weighted_f1 = metrics['weighted_f1']
avg_precision = metrics['avg_precision']
# ... explore other metrics stored in this dictionary!