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A library for machine learning utilities

Project description

Model Tooling library

Build Status codecov Python 3 Updates

Installation

Use pip to install: pip install ml-tooling

Contents

  • Transformers

    • A library of transformers for use with Scikit-learn pipelines
  • Model base classes

    • Production baseclasses for subclassing - guarantees interface for use in API
  • Plotting functions

    • Functions for producing nice, commonly used plots such as roc_curves and confusion matrices

BaseClassModel

A base Class for defining your model. Your subclass must define two methods:

  • get_prediction_data()

    Function that, given an input, fetches corresponding features. Used for predicting an unseen observation

  • get_training_data()

    Function that retrieves all training data. Used for training and evaluating the model

Example usage

Define a class using BaseClassModel and implement the two required methods. Here we simply implement a linear regression on the Boston dataset using sklearn.datasets

from ml_tooling import BaseClassModel
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, Ridge, LassoLars

# Define a new class

class BostonModel(BaseClassModel):
    def get_prediction_data(self, idx):
        x, _ = load_boston(return_X_y=True)
        return x[idx] # Return given observation

    def get_training_data(self):
        return load_boston(return_X_y=True)

# Use our new class to implement a given model - any sklearn compatible estimator
linear_boston = BostonModel(LinearRegression())

results = linear_boston.score_model()

# Visualize results
results.plot.residuals()
results.plot.prediction_error()

# Save our model
linear_boston.save_model()

# Recreate model
BostonModel.load_model('.')

# Train Different models and get the best performing
models_to_try = [LinearRegression(), Ridge(), LassoLars()]

# best_model will be BostonModel instantiated with the highest scoring model. all_results is a list of all results 
best_model, alL_results = BostonModel.test_models(models_to_try, metric='neg_mean_squared_error')
alL_results.to_dataframe(params=False)

Configuration

ml_tooling implements a number of default settings for modelling.

You can see the configuration by accessing the .config attribute. This config can be changed by simply modifying the relevant config.

BostonModel.config.RANDOM_STATE = 2

Config Options

VERBOSITY

Defaults to 0. The level of verbosity sklearn outputs

CLASSIFIER_METRIC

Defaults to 'accuracy'. The metric to use in classifier problems

REGRESSION_METRIC

Defaults to 'r2'. The metric to use in regression problems

CROSS_VALIDATION

Defaults to 10. Number of cross validation iterations to use

STYLE_SHEET

Defaults to built-in Alm Brand styling. Path to a matplotlib stylesheet to use in plots

N_JOBS

Defaults to -1. Number of cores to use in multiprocessing situations.

TEST_SIZE

Defaults to 0.25. Percentage of data taken out as test data.

RANDOM_STATE

Defaults to 42. Random state seed to use.

RUN_DIR

Defaults to './runs'. Folder to store run logging files

MODEL_DIR

Defaults to './models'. Folder to store pickled models

LOG

Defaults to False. Toggles whether or not to log to file. Set to True if you want every run to be logged, otherwise use the .log context manager to toggle for a single run.

Methods

The BaseClass implements a number of useful methods

save_model(path=None)

Saves the model as a binary file. Defaults to current working directory, with a filename of <class_name>_<model_name>_<git_hash>.pkl

load_model(path)

Instantiates the class with a joblib pickled model. If no path is given, searches path for the newest file that matches the pattern

score_model(metric='accuracy', cv=False)

Loads all training data and trains the model on it, using a train_test split. Returns a Result object containing all result parameters Defaults to non-cross-validated scoring. If you want to cross-validate, pass number of folds to cv

train_model()

Loads all training data and trains the model on all data. Typically used as the last step when model tuning is complete. Sets .result attribute to None

make_prediction(*args)

Makes a prediction given an input. For example a customer number. Passed to the implemented get_prediction_data() method and calls .predict() on the estimator

test_models([model1, model2], metric='accuracy')

Runs score_model() on each model, saving the result. Returns the best model as well as a ResultGroup of all results

gridsearch(param_grid)

Runs a gridsearch on the model with the passed in parameter grid. The function will ensure that it works inside a pipeline as well.

setup_model()

To be implemented by the user - setup_model is a classmethod which loads up an untrained model. Typically this would setup a pipeline and the selected model for easy training

Returning to our previous example of the BostonModel, let us implement a setup_model method

from ml_tooling import BaseClassModel
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
import pandas as pd

class BostonModel(BaseClassModel):
    def get_prediction_data(self, idx):
        data = load_boston()
        df = pd.DataFrame(data=data.data, columns=data.feature_names)
        return df.iloc[idx] # Return given observation

    def get_training_data(self):
        data = load_boston()
        return pd.DataFrame(data=data.data, columns=data.feature_names), data.target

    @classmethod
    def setup_model(cls):
        pipeline = Pipeline([
        ('scaler', StandardScaler()),
        ('clf', LinearRegression())
        ])
        return cls(pipeline)

Given this extra setup, it becomes easy to load the untrained model to train it:

model = BostonModel.setup_model()
model.train_model()

log(log_dir)

log() is a context manager that lets you turn on logging for any scoring methods that follow. You can pass a log_dir to specify a subfolder to store the model in. The output is a yaml file recording model parameters, package version numbers, metrics and other useful information

Usage example:

model = BostonModel.setup_model()

with model.log('score'):
    model.score_model()

This will save the results of model.score_model() to runs/score/

Visualizing results

When a model is trained, it returns a Result object. That object has number of visualization options depending on the type of model:

Any visualizer listed here also has a functional counterpart in ml_tooling.plots. E.g if you want to use the function for plotting a confusion matrix without using the ml_tooling BaseClassModel approach, you can instead do from ml_tooling.plots import plot_confusion_matrix

These functional counterparts all mirror sklearn metrics api, taking y_target and y_pred as arguments

from ml_tooling.plots import plot_confusion_matrix
import numpy as np

y_true = np.array([1, 0, 1, 0])
y_pred = np.array([1, 0, 0, 0])
plot_confusion_matrix(y_true, y_pred)

Classifiers

  • roc_curve(**kwargs):
    Visualize a ROC curve for a classification model. Model must implement a predict_proba method. Any kwargs are passed onto matplotlib.

  • confusion_matrix(normalized = True, **kwargs): Visualize a confusion matrix for a classification model. normalized determines whether or not to normalize annotated class counts. Any kwargs are passed onto matplotlib.

  • feature_importance(samples, values = True, top_n = None, bottom_n = None, n_jobs_overwrite=None, **kwargs):
    Calculates each features imporatance with permutation. Importance is measured in drop in model metric. samples determines the number of samples to use and must be set.

    If samples=None the original data set is used which is not recommended for small data sets.

    If samples is a float between 0 and 1 a new smaller data set is made from resampling with replacement form the original data set. This is not recommended for small data sets but could be suitable for very large data sets.

    If samples is set to an int a new data set of size samples is made from resampling with replacement form the original data. Recommended for small data sets to ensure stable estimates of feature importance.

    If top_n is an integer, return top_n features and if top_n is a float between (0, 1), return top_n percent features.

    If bottom_n is an integer, return bottom_n features and if bottom_n is a float between (0, 1), return bottom_n percent features.

    Setting n_jobs_overwrite to an int overwrites the settings of the model settings.

  • lift_curve(**kwargs):
    Visualize a Lift Curve for a classification model. Model must implement a predict_proba method. Any kwargs are passed onto matplotlib.

Regressors

  • prediction_error(**kwargs):
    Visualizes prediction error of a regression model. Any kwargs are passed onto matplotlib.

  • residuals(**kwargs):
    Visualizes residuals of a regression model. Any kwargs are passed onto matplotlib.

  • feature_importance(samples, values = True, top_n = None, bottom_n = None, n_jobs_overwrite=None, **kwargs):
    Calculates each features imporatance with permutation. Importance is measured in drop in model metric. samples determines the number of samples to use and must be set.

    If samples=None the original data set is used which is not recommended for small data sets.

    If samples is a float between 0 and 1 a new smaller data set is made from resampling with replacement form the original data set. This is not recommended for small data sets but could be suitable for very large data sets.

    If samples is set to an int a new data set of size samples is made from resampling with replacement form the original data. Recommended for small data sets to ensure stable estimates of feature importance.

    If top_n is an integer, return top_n features and if top_n is a float between (0, 1), return top_n percent features.

    If bottom_n is an integer, return bottom_n features and if bottom_n is a float between (0, 1), return bottom_n percent features.

    Setting n_jobs_overwrite to an int overwrites the settings of the model settings.

Transformers

The library also provides a number of transformers for working with DataFrames in a pipeline

Select

A column selector - Provide a list of columns to be passed on in the pipeline

Example

from ml_tooling.transformers import Select
import pandas as pd

df = pd.DataFrame({
    "id": [1, 2, 3, 4],
    "status": ["OK", "Error", "OK", "Error"],
    "sales": [2000, 3000, 4000, 5000] 

})

select = Select(['id', 'status'])
select.fit_transform(df)
Out[1]: 
   id status
0   1     OK
1   2  Error
2   3     OK
3   4  Error

FillNA

Fills NA values with given value or strategy. Either a value or a strategy has to be supplied.

Example for value

from ml_tooling.transformers import FillNA
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "id": [1, 2, 3, 4],
    "status": ["OK", "Error", "OK", "Error"],
    "sales": [2000, 3000, 4000, np.nan] 

})

fill_na = FillNA(value = 0)
fill_na.fit_transform(df)
Out[1]: 
   id status   sales
0   1     OK  2000.0
1   2  Error  3000.0
2   3     OK  4000.0
3   4  Error     0.0

Example for strategy

The built-in strategies are 'mean', 'median', 'most_freq', 'max' and 'min. An example of 'mean' would be:

fill_na = FillNA(value='mean')
fill_na.fit_transform(df)
Out[1]: 
   id status   sales
0   1     OK  2000.0
1   2  Error  3000.0
2   3     OK  4000.0
3   4  Error  3000.0

ToCategorical

Performs one-hot encoding of categorical values through pd.Categorical. All categorical values not found in training data will be set to 0

Example

from ml_tooling.transformers import ToCategorical
import pandas as pd

df = pd.DataFrame({
    "status": ["OK", "Error", "OK", "Error"] 

})

onehot = ToCategorical()
onehot.fit_transform(df)
Out[1]: 
   status_Error  status_OK
0             0          1
1             1          0
2             0          1
3             1          0

FuncTransformer

Applies a given function to each column

Example

from ml_tooling.transformers import FuncTransformer
import pandas as pd

df = pd.DataFrame({
    "status": ["OK", "Error", "OK", "Error"]
})

uppercase = FuncTransformer(lambda x: x.str.upper())
uppercase.fit_transform(df)
Out[1]: 
  status
0     OK
1  ERROR
2     OK
3  ERROR

Keyword arguments can be supplied to the function.

from ml_tooling.transformers import FuncTransformer
import pandas as pd

def custom_func(input, word1, word2):
   result = ""
   if input == "OK":
      result = word1
   elif input == "Error":
      result = word2
   return result

def wrapper(df, word1, word2):
   return df.apply(custom_func,args=(word1,word2))

df = pd.DataFrame({
    "status": ["OK", "Error", "OK", "Error"]
})

kwargs = {'word1': 'Okay','word2': 'Fail'}
wordchange = FuncTransformer(wrapper,**kwargs)
wordchange.fit_transform(df)
Out[2]: 
  status
0   Okay
1   Fail
2   Okay
3   Fail

Binner

Bins numerical data into supplied bins

Example

from ml_tooling.transformers import Binner
import pandas as pd

df = pd.DataFrame({
    "sales": [1500, 2000, 2250, 7830]
})

binned = Binner(bins=[0, 1000, 2000, 8000])
binned.fit_transform(df)
Out[1]: 
          sales
0  (1000, 2000]
1  (1000, 2000]
2  (2000, 8000]
3  (2000, 8000]

Renamer

Renames columns to be equal to the passed list - must be in order

Example

from ml_tooling.transformers import Renamer
import pandas as pd

df = pd.DataFrame({
    "Total Sales": [1500, 2000, 2250, 7830]
})

rename = Renamer(['sales'])
rename.fit_transform(df)
Out[1]: 
   sales
0   1500
1   2000
2   2250
3   7830

DateEncoder

Adds year, month, day, week columns based on a datefield. Each date type can be toggled in the initializer

Example

from ml_tooling.transformers import DateEncoder
import pandas as pd

df = pd.DataFrame({
    "sales_date": [pd.to_datetime('2018-01-01'), pd.to_datetime('2018-02-02')]
})

dates = DateEncoder(week=False)
dates.fit_transform(df)
Out[1]: 
   sales_date_day  sales_date_month  sales_date_year
0               1                 1             2018
1               2                 2             2018

FreqFeature

Converts a column into a normalized frequencies

Example

from ml_tooling.transformers import FreqFeature
import pandas as pd

df = pd.DataFrame({
    "sales_category": ['Sale', 'Sale', 'Not Sale']
})

freq = FreqFeature()
freq.fit_transform(df)
Out[1]: 
   sales_category
0        0.666667
1        0.666667
2        0.333333

DFFeatureUnion

A FeatureUnion equivalent for DataFrames. Concatenates the result of multiple transformers

Example

from ml_tooling.transformers import FreqFeature, Binner, Select, DFFeatureUnion
from sklearn.pipeline import Pipeline
import pandas as pd


df = pd.DataFrame({
    "sales_category": ['Sale', 'Sale', 'Not Sale', 'Not Sale'],
    "sales": [1500, 2000, 2250, 7830]
})


freq = Pipeline([
    ('select', Select('sales_category')), 
    ('freq', FreqFeature())
])

binned = Pipeline([
    ('select', Select('sales')), 
    ('bin', Binner(bins=[0, 1000, 2000, 8000]))
    ])


union = DFFeatureUnion([
    ('sales_category', freq), 
    ('sales', binned)
])
union.fit_transform(df)
Out[1]: 
   sales_category         sales
0             0.5  (1000, 2000]
1             0.5  (1000, 2000]
2             0.5  (2000, 8000]
3             0.5  (2000, 8000]

DFRowFunc

Row-wise operation on Pandas DataFrame. Strategy can either be one of the predefined or a callable. If some elements in the row are NaN these elements are ignored for the built-in strategies.

Example

from ml_tooling.transformers import DFRowFunc
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "number_1": [1, np.nan, 3, 4],
    "number_2": [1, 3, 2, 4]

})

rowfunc = DFRowFunc(strategy = 'sum')
rowfunc.fit_transform(df)
Out[1]: 
         0
0        2
1        3
2        5
3        8

The built-in strategies are 'sum', 'min' and 'max'. A strategy can also be a callable:

rowfunc = DFRowFunc(strategy = np.mean)
rowfunc.fit_transform(df)
Out[1]: 
         0
0        1
1        3
2        2.5
3        4

Binarize

Transformer which returns 1 if equal to given value else 0.

Example

from ml_tooling.transformers import Binarize
import pandas as pd
import numpy as np

df = pd.DataFrame({
    "number_1": [1, np.nan, 3, 4],
    "number_2": [1, 3, 2, 4]

})

binarize = Binarize(value = 3)
binarize.fit_transform(df)
Out[1]: 
         number_1    number_2
0               0           0
1               1           0
2               0           1
3               0           0

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