6.5. Machine Learning

6.5.1. Scikit-LLM: Supercharge Text Analysis with ChatGPT and scikit-learn Integration

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!pip install scikit-llm

To integrate advanced language models with scikit-learn for enhanced text analysis tasks, use Scikit-LLM.

Scikit-LLM’s ZeroShotGPTClassifier enables text classification on unseen classes without requiring re-training.

from skllm.config import SKLLMConfig

SKLLMConfig.set_openai_key("<YOUR_KEY>")
SKLLMConfig.set_openai_org("<YOUR_ORGANISATION>")

from skllm.datasets import get_classification_dataset
from skllm import ZeroShotGPTClassifier

# demo sentiment analysis dataset
# labels: positive, negative, neutral
X, y = get_classification_dataset()

clf = ZeroShotGPTClassifier(openai_model="gpt-3.5-turbo")
clf.fit(X, y)
labels = clf.predict(X)

100%|██████████| 30/30 [00:36<00:00,  1.22s/it]

from sklearn.metrics import accuracy_score
print(f"Accuracy: {accuracy_score(y, labels):.2f}")

Accuracy: 0.93

Link to Scikit-LLM.

6.5.2. Create a Readable Machine Learning Pipeline in One Line of Code

If you want to create a readable machine learning pipeline in a single line of code, try the make_pipeline function in scikit-learn. make_pipeline is especially useful when working with complex pipelines that involve many different transformers and estimators.

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, y = make_classification(random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Create a pipeline that scales the data and fits a logistic regression model
pipeline = make_pipeline(StandardScaler(), LogisticRegression())

# Fit the pipeline to the training data
pipeline.fit(X_train, y_train)

# Evaluate the pipeline on the test data
pipeline.score(X_test, y_test)

0.96

6.5.3. Pipeline + GridSearchCV: Prevent Data Leakage when Scaling the Data

Scaling the data before using GridSearchCV can lead to data leakage since the scaling tells some information about the entire data. To prevent this, assemble both the scaler and machine learning models in a pipeline then use it as the estimator for GridSearchCV.

from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.datasets import load_iris

# load data
df = load_iris()
X = df.data
y = df.target

# split data into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Create a pipeline variable
make_pipe = make_pipeline(StandardScaler(), SVC())

# Defining parameters grid
grid_params = {"svc__C": [0.1, 1, 10, 100, 1000], "svc__gamma": [0.1, 1, 10, 100]}

# hypertuning
grid = GridSearchCV(make_pipe, grid_params, cv=5)
grid.fit(X_train, y_train)

# predict
y_pred = grid.predict(X_test)

The estimator is now the entire pipeline instead of just the machine learning model.

6.5.4. squared=False: Get RMSE from Sklearn’s mean_squared_error method

To obtain the root mean squared error (RMSE) using scikit-learn, use the squared=False parameter in the mean_squared_error method.

from sklearn.metrics import mean_squared_error

y_actual = [1, 2, 3]
y_predicted = [1.5, 2.5, 3.5]
rmse = mean_squared_error(y_actual, y_predicted, squared=False)
rmse

0.5

6.5.5. modelkit: Build Production ML Systems in Python

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!pip install modelkit textblob 

If you want your ML models to be fast, type-safe, testable, and fast to deploy to production, try modelkit. modelkit allows you to incorporate all of these features into your model in several lines of code.

from modelkit import ModelLibrary, Model 
from textblob import TextBlob, WordList
# import nltk
# nltk.download('brown')
# nltk.download('punkt')

To define a modelkit Model, you need to:

• create class inheriting from modelkit.Model

• implement a _predict method

class NounPhraseExtractor(Model):

    # Give model a name
    CONFIGURATIONS = {"noun_phrase_extractor": {}}

    def _predict(self, text):
        blob = TextBlob(text)
        return blob.noun_phrases

You can now instantiate and use the model:

noun_extractor = NounPhraseExtractor()
noun_extractor("What are your learning strategies?")

2021-11-05 09:55.55 [debug    ] Model loaded                   memory=0 Bytes memory_bytes=0 model_name=None time=0 microseconds time_s=4.232699939166196e-05

WordList(['learning strategies'])

You can also create test cases for your model and make sure all test cases are passed.

class NounPhraseExtractor(Model):

    # Give model a name
    CONFIGURATIONS = {"noun_phrase_extractor": {}}

    TEST_CASES = [
        {"item": "There is a red apple on the tree", "result": WordList(["red apple"])}
    ]

    def _predict(self, text):
        blob = TextBlob(text)
        return blob.noun_phrases

noun_extractor = NounPhraseExtractor()
noun_extractor.test()

2021-11-05 09:55.58 [debug    ] Model loaded                   memory=0 Bytes memory_bytes=0 model_name=None time=0 microseconds time_s=4.3191997974645346e-05

TEST 1: SUCCESS

modelkit also allows you to organize a group of models using ModelLibrary.

class SentimentAnalyzer(Model):
    
    # Give model a name
    CONFIGURATIONS = {"sentiment_analyzer": {}}

    def _predict(self, text):
        blob = TextBlob(text)
        return blob.sentiment

nlp_models = ModelLibrary(models=[NounPhraseExtractor, SentimentAnalyzer])

2021-11-05 09:50.13 [info     ] Instantiating AssetsManager    lazy_loading=False
2021-11-05 09:50.13 [info     ] No remote storage provider configured
2021-11-05 09:50.13 [debug    ] Resolving asset for Model      model_name=sentiment_analyzer
2021-11-05 09:50.13 [debug    ] Loading model                  model_name=sentiment_analyzer
2021-11-05 09:50.13 [debug    ] Instantiating Model object     model_name=sentiment_analyzer
2021-11-05 09:50.13 [debug    ] Model loaded                   memory=0 Bytes memory_bytes=0 model_name=sentiment_analyzer time=0 microseconds time_s=3.988200114690699e-05
2021-11-05 09:50.13 [debug    ] Done loading Model             model_name=sentiment_analyzer
2021-11-05 09:50.13 [info     ] Model and dependencies loaded  memory=0 Bytes memory_bytes=0 name=sentiment_analyzer time=0 microseconds time_s=0.00894871700074873
2021-11-05 09:50.13 [debug    ] Resolving asset for Model      model_name=noun_phrase_extractor
2021-11-05 09:50.13 [debug    ] Loading model                  model_name=noun_phrase_extractor
2021-11-05 09:50.13 [debug    ] Instantiating Model object     model_name=noun_phrase_extractor
2021-11-05 09:50.13 [debug    ] Model loaded                   memory=0 Bytes memory_bytes=0 model_name=noun_phrase_extractor time=0 microseconds time_s=2.751099964370951e-05
2021-11-05 09:50.13 [debug    ] Done loading Model             model_name=noun_phrase_extractor
2021-11-05 09:50.13 [info     ] Model and dependencies loaded  memory=0 Bytes memory_bytes=0 name=noun_phrase_extractor time=0 microseconds time_s=0.006440052002290031

Get and use the models from nlp_models.

noun_extractor = model_collections.get("noun_phrase_extractor")
noun_extractor("What are your learning strategies?")

WordList(['learning strategies'])

sentiment_analyzer = model_collections.get("sentiment_analyzer")
sentiment_analyzer("Today is a beautiful day!")

Sentiment(polarity=1.0, subjectivity=1.0)

Link to modelkit.

6.5.6. Decomposing High-Dimensional Data into Two or Three Dimensions

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!pip install yellowbrick

If you want to decompose high dimensional data into two or three dimensions to visualize it, what should you do? A common technique is PCA. Even though PCA is useful, it can be complicated to create a PCA plot.

Lucikily, Yellowbrick allows you visualize PCA in a few lines of code

from yellowbrick.datasets import load_credit
from yellowbrick.features import PCA

X, y = load_credit()
classes = ["account in defaut", "current with bills"]

visualizer = PCA(scale=True, classes=classes)
visualizer.fit_transform(X, y)
visualizer.show()

<AxesSubplot:title={'center':'Principal Component Plot'}, xlabel='$PC_1$', ylabel='$PC_2$'>

Link to Yellowbrick

6.5.7. Visualize Feature Importances with Yellowbrick

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!pip install yellowbrick

More features don’t always mean a better model. Too many features can lead to overfitting. To improve your model, you might need to identify and eliminate less important features. Yellowbrick’s FeatureImportances can help with this.

from sklearn.tree import DecisionTreeClassifier
from yellowbrick.datasets import load_occupancy
from yellowbrick.model_selection import FeatureImportances

X, y = load_occupancy()

model = DecisionTreeClassifier()

viz = FeatureImportances(model)
viz.fit(X, y)
viz.show();

From the plot above, it seems like the light is the most important feature to DecisionTreeClassifier, followed by CO2, temperature.

Link to Yellowbrick

6.5.8. Validation Curve: Determine if an Estimator Is Underfitting Over Overfitting

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!pip install yellowbrick

To determine if your model is overfitting or underfitting, use Yellowbrick’s validation curve. This technique helps find the optimal hyperparameter value where the model performs well on both training and validation sets.

from yellowbrick.datasets.loaders import load_occupancy
from yellowbrick.model_selection import validation_curve
from sklearn.tree import DecisionTreeClassifier

import numpy as np 

# Load data
X, y = load_occupancy()

In the code below, we choose the range of max_depth to be from 1 to 11.

viz = validation_curve(
    DecisionTreeClassifier(), X, y, param_name="max_depth",
    param_range=np.arange(1, 11), cv=10, scoring="f1",
)

As we can see from the plot above, although max_depth > 2 has a higher training score but a lower cross-validation score. This indicates that the model is overfitting.

Thus, the sweet spot will be where the cross-validation score neither increases nor decreases, which is 2.

Link to Yellowbrick.

6.5.9. Mlxtend: Plot Decision Regions of Your ML Classifiers

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!pip install mlxtend  

To understand how your classifier makes decisions, plotting decision regions can be insightful. Mlxtend’s plot_decision_regions function makes this easy.

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import EnsembleVoteClassifier
from mlxtend.data import iris_data
from mlxtend.plotting import plot_decision_regions

# Initializing Classifiers
clf1 = LogisticRegression(random_state=0)
clf2 = RandomForestClassifier(random_state=0)
clf3 = SVC(random_state=0, probability=True)
eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[2, 1, 1], voting='soft')

# Loading some example data
X, y = iris_data()
X = X[:,[0, 2]]

# Plotting Decision Regions
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10, 8))

for clf, lab, grd in zip([clf1, clf2, clf3, eclf],
                         ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble'],
                         itertools.product([0, 1], repeat=2)):
    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.

Find other useful functionalities of Mlxtend here.

6.5.10. imbalanced-learn: Deal with an Imbalanced Dataset

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!pip install imbalanced-learn==0.10.0 mlxtend==0.21.0 scikit-learn==1.2.2

In machine learning, imbalanced datasets can lead to biased models that perform poorly on minority classes. This is particularly problematic in critical applications like fraud detection or disease diagnosis.

With imbalanced-learn, you can rebalance your dataset using various sampling techniques that work seamlessly with scikit-learn.

To demonstrate this, let’s generate a sample dataset with 5000 samples, 2 features, and 4 classes:

# Libraries for plotting
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions

# Libraries for machine learning
from sklearn.datasets import make_classification
from sklearn.svm import LinearSVC
from imblearn.over_sampling import RandomOverSampler

X, y = make_classification(
    n_samples=5000,
    n_features=2,
    n_informative=2,
    n_redundant=0,
    n_repeated=0,
    n_classes=4,
    n_clusters_per_class=1,
    weights=[0.01, 0.04, 0.5, 0.90],
    class_sep=0.8,
    random_state=0,
)

Resample the dataset using the RandomOverSampler class from imbalanced-learn to balance the class distribution. This technique works by duplicating minority samples until they match the majority class.

ros = RandomOverSampler(random_state=1)
X_resampled, y_resampled = ros.fit_resample(X, y)

Plot the decision regions of the dataset before and after resampling using a LinearSVC classifier:

# Plotting Decision Regions
fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, sharey=True, figsize=(6, 10))

for Xi, yi, ax, title in zip(
    [X, X_resampled],
    [y, y_resampled],
    [ax0, ax1],
    ["Without resampling", "Using RandomOverSampler"],
):
    clf = LinearSVC()
    clf.fit(Xi, yi)
    fig = plot_decision_regions(X=Xi, y=yi, clf=clf, legend=2, ax=ax, colors='#E583B6,#72FCDB,#72BEFA,#FFFF99')
    plt.title(title)
    ax.set_title(title, color='#000000')

/Users/khuyentran/book/venv/lib/python3.11/site-packages/mlxtend/plotting/decision_regions.py:300: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.
  ax.scatter(
/Users/khuyentran/book/venv/lib/python3.11/site-packages/mlxtend/plotting/decision_regions.py:300: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.
  ax.scatter(

The plot reveals that the resampling process has added more data points to the minority class (green), effectively balancing the class distribution.

Link to imbalanced-learn.

6.5.11. Estimate Prediction Intervals in Scikit-Learn Models with MAPIE

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!pip install mapie

To estimate prediction intervals for scikit-learn models, use the MAPIE library:

In the code below, we use MapieRegressor to estimate prediction intervals for a scikit-learn regressor.

from mapie.regression import MapieRegressor
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

# Create data
X, y = make_regression(n_samples=200, n_features=1, noise=50, random_state=0)

# Train and predict
alpha = [0.05, 0.32]
mapie = MapieRegressor(LinearRegression())
mapie.fit(X, y)
y_pred, y_pis = mapie.predict(X, alpha=alpha)

# compute the coverage of the prediction intervals
from mapie.metrics import regression_coverage_score

coverage_scores = [
    regression_coverage_score(y, y_pis[:, 0, i], y_pis[:, 1, i])
    for i, _ in enumerate(alpha)
]

# Plot the estimated prediction intervals

from matplotlib import pyplot as plt
import numpy as np

plt.figure(figsize=(11, 7))
plt.xlabel("x", fontsize=20)
plt.ylabel("y", fontsize=20)
plt.xticks(fontsize=20) 
plt.yticks(fontsize=20) 
plt.scatter(X, y, alpha=0.3, c='#06B1CF', s=80)
plt.plot(X, y_pred, color="#E48789", linewidth=3)
order = np.argsort(X[:, 0])
plt.plot(X[order], y_pis[order][:, 0, 1], color="#E48789", ls="--", linewidth=3)
plt.plot(X[order], y_pis[order][:, 1, 1], color="#E48789", ls="--", linewidth=3)
plt.fill_between(
    X[order].ravel(),
    y_pis[order][:, 0, 0].ravel(),
    y_pis[order][:, 1, 0].ravel(),
    alpha=0.2,
    color="#E48789"
)
plt.title(
    f"Target coverages for "
    f"alpha={alpha[0]:.2f}: ({1-alpha[0]:.3f}, {coverage_scores[0]:.3f})\n"
    f"Target coverages for "
    f"alpha={alpha[1]:.2f}: ({1-alpha[1]:.3f}, {coverage_scores[1]:.3f})",
    fontsize=20,
)
plt.show()

Link to MAPIE.

6.5.12. mlforecast: Scalable Machine Learning for Time Series

If you want to perform time series forecasting using machine learning models and scale to massive amounts of data with distributed training, try mlforecast.

from mlforecast.distributed import DistributedMLForecast
from mlforecast.distributed.models.dask.lgb import DaskLGBMForecast
from mlforecast.target_transforms import Differences

# Create Dask Dataframe
series_ddf = ...

# Perform distributed training
fcst = DistributedMLForecast(
    models=DaskLGBMForecast(),
    freq='D', # daily frequency
    lags=[7], 
    target_transforms=[Differences([1])],
)
fcst.fit(series_ddf)

Full code of the example above.

Link to mlforecast.

6.5.13. MLEM: Capture Your Machine Learning Model’s Metadata

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!pip install mlem 

The metadata of a machine learning model provides important information about the model such as:

• Hash value

• Model methods

• Input data schema

• Python requirements used to train the model.

This information enables others to reproduce the model and its results.

With MLEM, you can save both the model and its metadata in a single line of code.

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Load the diabetes dataset
diabetes = datasets.load_diabetes()

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=0)

# Create a linear regression model
model = LinearRegression()

# Train the model on the training set
model.fit(X_train, y_train)

LinearRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

from mlem.api import save 

# Instead of joblib.dump(model, 'model/diabetes_model')
save(model, 'model/diabetes_model', sample_data=X_test)

MlemModel(location=Location(path='/Users/khuyentran/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter5/model/diabetes_model.mlem', project=None, rev=None, uri='file:///Users/khuyentran/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter5/model/diabetes_model.mlem', project_uri=None, fs=<fsspec.implementations.local.LocalFileSystem object at 0x16b631430>), params={}, artifacts={'data': LocalArtifact(uri='diabetes_model', size=563, hash='c57e456e8a0768326655a8b52cde4f47')}, requirements=Requirements(__root__=[InstallableRequirement(module='sklearn', version='1.2.1', package_name='scikit-learn', extra_index=None, source_url=None, vcs=None, vcs_commit=None), InstallableRequirement(module='numpy', version='1.24.2', package_name=None, extra_index=None, source_url=None, vcs=None, vcs_commit=None)]), processors_cache={'model': SklearnModel(model=LinearRegression(), io=SimplePickleIO(), methods={'predict': Signature(name='predict', args=[Argument(name='X', type_=NumpyNdarrayType(value=None, shape=(None, 10), dtype='float64'), required=True, default=None, kw_only=False)], returns=NumpyNdarrayType(value=None, shape=(None,), dtype='float64'), varargs=None, varargs_type=None, varkw=None, varkw_type=None)})}, call_orders={'predict': [('model', 'predict')]})

Running the code above will create two files: a model file and a metadata file.

model
 ├──  diabetes_model  
 └──  diabetes_model.mlem  

Here is what the metadata file looks like:

# model/diabetes_model.mlem

artifacts:
  data:
    hash: c57e456e8a0768326655a8b52cde4f47
    size: 563
    uri: diabetes_model
call_orders:
  predict:
  - - model
    - predict
object_type: model
processors:
  model:
    methods:
      predict:
        args:
        - name: X
          type_:
            dtype: float64
            shape:
            - null
            - 10
            type: ndarray
        name: predict
        returns:
          dtype: float64
          shape:
          - null
          type: ndarray
    type: sklearn
requirements:
- module: sklearn
  package_name: scikit-learn
  version: 1.2.1
- module: numpy
  version: 1.24.2

Link to MLEM.

How to deploy your model with MLEM.

6.5.14. Distributed Machine Learning with MLlib

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!pip install pyspark

If you want to perform distributed machine learning tasks and handle large-scale datasets, use MLlib. It’s designed to work seamlessly with Apache Spark, making it a powerful tool for scalable machine learning.

Similar to scikit-learn, MLlib provides the following tools:

• ML Algorithms: Classification, regression, clustering, and collaborative filtering

• Featurization: Feature extraction, transformation, dimensionality reduction, and selection

• Pipelines: Construction, evaluation, and tuning of ML Pipelines

from pyspark.ml.linalg import Vectors
from pyspark.ml.classification import LogisticRegression
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

# Prepare training data from a list of (label, features) tuples.
training = spark.createDataFrame(
    [
        (1.0, Vectors.dense([0.0, 1.1, 0.1])),
        (0.0, Vectors.dense([2.0, 1.0, -1.0])),
        (0.0, Vectors.dense([2.0, 1.3, 1.0])),
        (1.0, Vectors.dense([0.0, 1.2, -0.5])),
    ],
    ["label", "features"],
)

# Prepare test data
test = spark.createDataFrame(
    [
        (1.0, Vectors.dense([-1.0, 1.5, 1.3])),
        (0.0, Vectors.dense([3.0, 2.0, -0.1])),
        (1.0, Vectors.dense([0.0, 2.2, -1.5])),
    ],
    ["label", "features"],
)

# Create a LogisticRegression instance. This instance is an Estimator.
lr = LogisticRegression(maxIter=10, regParam=0.01)

# Learn a LogisticRegression model. This uses the parameters stored in lr.
model1 = lr.fit(training)

# Make predictions on test data using the Transformer.transform() method.
# LogisticRegression.transform will only use the 'features' column.
prediction = model1.transform(test)
result = prediction.select("features", "label", "probability", "prediction").collect()

for row in result:
    print(
        "features=%s, label=%s -> prob=%s, prediction=%s"
        % (row.features, row.label, row.probability, row.prediction)
    )

LogisticRegression parameters:
aggregationDepth: suggested depth for treeAggregate (>= 2). (default: 2)
elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. (default: 0.0)
family: The name of family which is a description of the label distribution to be used in the model. Supported options: auto, binomial, multinomial (default: auto)
featuresCol: features column name. (default: features)
fitIntercept: whether to fit an intercept term. (default: True)
labelCol: label column name. (default: label)
lowerBoundsOnCoefficients: The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. (undefined)
lowerBoundsOnIntercepts: The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must beequal with 1 for binomial regression, or the number oflasses for multinomial regression. (undefined)
maxBlockSizeInMB: maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0. (default: 0.0)
maxIter: max number of iterations (>= 0). (default: 100, current: 10)
predictionCol: prediction column name. (default: prediction)
probabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities. (default: probability)
rawPredictionCol: raw prediction (a.k.a. confidence) column name. (default: rawPrediction)
regParam: regularization parameter (>= 0). (default: 0.0, current: 0.01)
standardization: whether to standardize the training features before fitting the model. (default: True)
threshold: Threshold in binary classification prediction, in range [0, 1]. If threshold and thresholds are both set, they must match.e.g. if threshold is p, then thresholds must be equal to [1-p, p]. (default: 0.5)
thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold. (undefined)
tol: the convergence tolerance for iterative algorithms (>= 0). (default: 1e-06)
upperBoundsOnCoefficients: The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. (undefined)
upperBoundsOnIntercepts: The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression. (undefined)
weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0. (undefined)

features=[-1.0,1.5,1.3], label=1.0 -> prob=[0.0019392203169556147,0.9980607796830444], prediction=1.0
features=[3.0,2.0,-0.1], label=0.0 -> prob=[0.995731919571047,0.004268080428952992], prediction=0.0
features=[0.0,2.2,-1.5], label=1.0 -> prob=[0.01200463023637096,0.987995369763629], prediction=1.0

Link to MLlib.

6.5.15. Rapid Prototyping and Comparison of Basic Models with Lazy Predict

Lazy Predict enables rapid prototyping and comparison of multiple basic models without extensive manual coding or parameter tuning.

This helps data scientists identify promising approaches and iterate on them more quickly.

from lazypredict.Supervised import LazyClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X = data.data
y= data.target

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123)

clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)

print(models)

| Model                          |   Accuracy |   Balanced Accuracy |   ROC AUC |   F1 Score |   Time Taken |
|:-------------------------------|-----------:|--------------------:|----------:|-----------:|-------------:|
| LinearSVC                      |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0150008 |
| SGDClassifier                  |   0.989474 |            0.987544 |  0.987544 |   0.989462 |    0.0109992 |
| MLPClassifier                  |   0.985965 |            0.986904 |  0.986904 |   0.985994 |    0.426     |
| Perceptron                     |   0.985965 |            0.984797 |  0.984797 |   0.985965 |    0.0120046 |
| LogisticRegression             |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.0200036 |
| LogisticRegressionCV           |   0.985965 |            0.98269  |  0.98269  |   0.985934 |    0.262997  |
| SVC                            |   0.982456 |            0.979942 |  0.979942 |   0.982437 |    0.0140011 |
| CalibratedClassifierCV         |   0.982456 |            0.975728 |  0.975728 |   0.982357 |    0.0350015 |
| PassiveAggressiveClassifier    |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0130005 |
| LabelPropagation               |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0429988 |
| LabelSpreading                 |   0.975439 |            0.974448 |  0.974448 |   0.975464 |    0.0310006 |
| RandomForestClassifier         |   0.97193  |            0.969594 |  0.969594 |   0.97193  |    0.033     |
| GradientBoostingClassifier     |   0.97193  |            0.967486 |  0.967486 |   0.971869 |    0.166998  |
| QuadraticDiscriminantAnalysis  |   0.964912 |            0.966206 |  0.966206 |   0.965052 |    0.0119994 |
| HistGradientBoostingClassifier |   0.968421 |            0.964739 |  0.964739 |   0.968387 |    0.682003  |
| RidgeClassifierCV              |   0.97193  |            0.963272 |  0.963272 |   0.971736 |    0.0130029 |
| RidgeClassifier                |   0.968421 |            0.960525 |  0.960525 |   0.968242 |    0.0119977 |
| AdaBoostClassifier             |   0.961404 |            0.959245 |  0.959245 |   0.961444 |    0.204998  |
| ExtraTreesClassifier           |   0.961404 |            0.957138 |  0.957138 |   0.961362 |    0.0270066 |
| KNeighborsClassifier           |   0.961404 |            0.95503  |  0.95503  |   0.961276 |    0.0560005 |
| BaggingClassifier              |   0.947368 |            0.954577 |  0.954577 |   0.947882 |    0.0559971 |
| BernoulliNB                    |   0.950877 |            0.951003 |  0.951003 |   0.951072 |    0.0169988 |
| LinearDiscriminantAnalysis     |   0.961404 |            0.950816 |  0.950816 |   0.961089 |    0.0199995 |
| GaussianNB                     |   0.954386 |            0.949536 |  0.949536 |   0.954337 |    0.0139935 |
| NuSVC                          |   0.954386 |            0.943215 |  0.943215 |   0.954014 |    0.019989  |
| DecisionTreeClassifier         |   0.936842 |            0.933693 |  0.933693 |   0.936971 |    0.0170023 |
| NearestCentroid                |   0.947368 |            0.933506 |  0.933506 |   0.946801 |    0.0160074 |
| ExtraTreeClassifier            |   0.922807 |            0.912168 |  0.912168 |   0.922462 |    0.0109999 |
| CheckingClassifier             |   0.361404 |            0.5      |  0.5      |   0.191879 |    0.0170043 |
| DummyClassifier                |   0.512281 |            0.489598 |  0.489598 |   0.518924 |    0.0119965 |

Link to Lazy Predict.

6.5.16. AutoGluon: Fast and Accurate ML in 3 Lines of Code

The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

# Preprocessing Pipeline
numeric_transformer = SimpleImputer(strategy='mean')
categorical_transformer = OneHotEncoder(handle_unknown='ignore')

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numerical_columns),
        ('cat', categorical_transformer, categorical_columns)
    ])

# Machine Learning Pipeline
model = RandomForestClassifier()

pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('scaler', StandardScaler()),
    ('model', model)
])

# Hyperparameter Tuning
param_grid = {
    'model__n_estimators': [100, 200, 300],
    'model__max_depth': [5, 10, None],
    'model__min_samples_split': [2, 5, 10]
}

grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)
grid_search.predict(X_test)

In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models in 3 lines of code.

from autogluon.tabular import TabularPredictor

predictor = TabularPredictor(label="class").fit(train_data)
predictions = predictor.predict(test_data)

Link to AutoGluon.

6.5.17. Model Logging Made Easy: MLflow vs. Pickle

Here is why using MLflow to log model is superior to using pickle to save model:

1. Managing Library Versions:

• Problem: Different models may require different versions of the same library, which can lead to conflicts. Manually tracking and setting up the correct environment for each model is time-consuming and error-prone.

• Solution: By automatically logging dependencies, MLflow ensures that anyone can recreate the exact environment needed to run the model.

2. Documenting Inputs and Outputs:

• Problem: Often, the expected inputs and outputs of a model are not well-documented, making it difficult for others to use the model correctly.

• Solution: By defining a clear schema for inputs and outputs, MLflow ensures that anyone using the model knows exactly what data to provide and what to expect in return.

To demonstrate the advantages of MLflow, let’s implement a simple logistic regression model and log it.

import mlflow
from mlflow.models import infer_signature
import numpy as np
from sklearn.linear_model import LogisticRegression

with mlflow.start_run():
    X = np.array([-2, -1, 0, 1, 2, 1]).reshape(-1, 1)
    y = np.array([0, 0, 1, 1, 1, 0])
    lr = LogisticRegression()
    lr.fit(X, y)
    signature = infer_signature(X, lr.predict(X))

    model_info = mlflow.sklearn.log_model(
        sk_model=lr, artifact_path="model", signature=signature
    )

    print(f"Saving data to {model_info.model_uri}")

Saving data to runs:/f8b0fc900aa14cf0ade8d0165c5a9f11/model

The output indicates where the model has been saved. To use the logged model later, you can load it with the model_uri:

import mlflow
import numpy as np

model_uri = "runs:/1e20d72afccf450faa3b8a9806a97e83/model"
sklearn_pyfunc = mlflow.pyfunc.load_model(model_uri=model_uri)

data = np.array([-4, 1, 0, 10, -2, 1]).reshape(-1, 1)

predictions = sklearn_pyfunc.predict(data)

Let’s inspect the artifacts saved with the model:

%cd mlruns/0/1e20d72afccf450faa3b8a9806a97e83/artifacts/model
%ls

/Users/khuyentran/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter5/mlruns/0/1e20d72afccf450faa3b8a9806a97e83/artifacts/model
MLmodel           model.pkl         requirements.txt
conda.yaml        python_env.yaml

The MLmodel file provides essential information about the model, including dependencies and input/output specifications:

%cat MLmodel

artifact_path: model
flavors:
  python_function:
    env:
      conda: conda.yaml
      virtualenv: python_env.yaml
    loader_module: mlflow.sklearn
    model_path: model.pkl
    predict_fn: predict
    python_version: 3.11.6
  sklearn:
    code: null
    pickled_model: model.pkl
    serialization_format: cloudpickle
    sklearn_version: 1.4.1.post1
mlflow_version: 2.15.0
model_size_bytes: 722
model_uuid: e7487bc3c4ab417c965144efcecaca2f
run_id: 1e20d72afccf450faa3b8a9806a97e83
signature:
  inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1, 1]}}]'
  outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "int64", "shape": [-1]}}]'
  params: null
utc_time_created: '2024-08-02 20:58:16.516963'

The conda.yaml and python_env.yaml files outline the environment dependencies, ensuring that the model runs in a consistent setup:

%cat conda.yaml

channels:
- conda-forge
dependencies:
- python=3.11.6
- pip<=24.2
- pip:
  - mlflow==2.15.0
  - cloudpickle==2.2.1
  - numpy==1.23.5
  - psutil==5.9.6
  - scikit-learn==1.4.1.post1
  - scipy==1.11.3
name: mlflow-env

%cat python_env.yaml

python: 3.11.6
build_dependencies:
- pip==24.2
- setuptools
- wheel==0.40.0
dependencies:
- -r requirements.txt

%cat requirements.txt

mlflow==2.15.0
cloudpickle==2.2.1
numpy==1.23.5
psutil==5.9.6
scikit-learn==1.4.1.post1
scipy==1.11.3

Learn more about MLFlow Models.

6.5.18. Simplifying ML Model Integration with FastAPI

Show code cell content Hide code cell content

!pip3 install joblib "fastapi[standard]"

Imagine this scenario: You have just built a machine learning (ML) model with great performance, and you want to share this model with your team members so that they can develop a web application on top of your model.

One way to share the model with your team members is to save the model to a file (e.g., using pickle, joblib, or framework-specific methods) and share the file directly

import joblib

model = ...

# Save model
joblib.dump(model, "model.joblib")

# Load model
model = joblib.load(model)

However, this approach requires the same environment and dependencies, and it can pose potential security risks.

An alternative is creating an API for your ML model. APIs define how software components interact, allowing:

1. Access from various programming languages and platforms

2. Easier integration for developers unfamiliar with ML or Python

3. Versatile use across different applications (web, mobile, etc.)

This approach simplifies model sharing and usage, making it more accessible for diverse development needs.

Let’s learn how to create an ML API with FastAPI, a modern and fast web framework for building APIs with Python.

Before we begin constructing an API for a machine learning model, let’s first develop a basic model that our API will use. In this example, we’ll create a model that predicts the median house price in California.

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import joblib

# Load dataset
X, y = fetch_california_housing(as_frame=True, return_X_y=True)

# Split dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Initialize and train the logistic regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict and evaluate the model
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean squared error: {mse:.2f}")

# Save model
joblib.dump(model, "lr.joblib")

Mean squared error: 0.56

['lr.joblib']

Once we have our model, we can create an API for it using FastAPI. We’ll define a POST endpoint for making predictions and use the model to make predictions.

Here’s an example of how to create an API for a machine learning model using FastAPI:

%%writefile ml_app.py
from fastapi import FastAPI
import joblib
import pandas as pd 

# Create a FastAPI application instance
app = FastAPI()

# Load the pre-trained machine learning model
model = joblib.load("lr.joblib")

# Define a POST endpoint for making predictions
@app.post("/predict/")
def predict(data: list[float]):
    # Define the column names for the input features
    columns = [
        "MedInc",
        "HouseAge",
        "AveRooms",
        "AveBedrms",
        "Population",
        "AveOccup",
        "Latitude",
        "Longitude",
    ]
    
    # Create a pandas DataFrame from the input data
    features = pd.DataFrame([data], columns=columns)
    
    # Use the model to make a prediction
    prediction = model.predict(features)[0]
    
    # Return the prediction as a JSON object, rounding to 2 decimal places
    return {"price": round(prediction, 2)}

Overwriting ml_app.py

To run your FastAPI app for development, use the fastapi dev command:

$ fastapi dev ml_app.py

This will start the development server and open the API documentation in your default browser.

You can now use the API to make predictions by sending a POST request to the /predict/ endpoint with the input data. For example:

Running this cURL command on your terminal:

curl -X 'POST' \
  'http://127.0.0.1:8000/predict/' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '[
  1.68, 25, 4, 2, 1400, 3, 36.06, -119.01
]'

This will return the predicted price as a JSON object, rounded to 2 decimal places:

{"price":1.51}

6.5.19. imodels: Simplifying Machine Learning with Interpretable Models

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!pip install imodels

Interpreting decisions made by complex modern machine learning models can be challenging.

imodels, a Python package, replaces black-box models (e.g. random forests) with simpler and interpretable alternatives (e.g. rule lists) without losing accuracy.

imodels works like scikit-learn models, making it easy to integrate into existing workflows.

Here’s an example of fitting an interpretable decision tree to predict juvenile delinquency:

from sklearn.model_selection import train_test_split
from imodels import get_clean_dataset, HSTreeClassifierCV 

# Prepare data
X, y, feature_names = get_clean_dataset('juvenile')
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# Initialize a tree model and specify only 4 leaf nodes
model = HSTreeClassifierCV(max_leaf_nodes=4)  
model.fit(X_train, y_train, feature_names=feature_names)  

# Make predictions
preds = model.predict(X_test) 

# Print the model
print(model)

fetching juvenile_clean from imodels
> ------------------------------
> Decision Tree with Hierarchical Shrinkage
> 	Prediction is made by looking at the value in the appropriate leaf of the tree
> ------------------------------
|--- friends_broken_in_steal:1 <= 0.50
|   |--- physically_ass:0 <= 0.50
|   |   |--- weights: [0.71, 0.29] class: 0.0
|   |--- physically_ass:0 >  0.50
|   |   |--- weights: [0.95, 0.05] class: 0.0
|--- friends_broken_in_steal:1 >  0.50
|   |--- non-exp_past_year_marijuana:0 <= 0.50
|   |   |--- weights: [0.33, 0.67] class: 1.0
|   |--- non-exp_past_year_marijuana:0 >  0.50
|   |   |--- weights: [0.60, 0.40] class: 0.0

This tree structure clearly shows how predictions are made based on feature values, providing transparency into the model’s decision-making process. The hierarchical shrinkage technique also improves predictive performance compared to standard decision trees.

Link to imodels.