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End-to-end Machine Learning Toolkit (MLToolkit/mltk) for Python

Project description

MLToolKit Project

www.mltoolkit.org

Current release: PyMLToolkit [v0.1.9]

MLToolKit (mltk) is a Python package providing a set of user-friendly functions to help building end-to-end machine learning models in data science research, teaching or production focused projects.

Introduction

MLToolKit supports all stages of the machine learning application development process.

Installation

pip install pymltoolkit

If the installation failed with dependancy issues, execute the above command with --no-dependencies

pip install pymltoolkit --no-dependencies

Functions

  • Data Extraction (SQL, Flatfiles, Images, etc.)
  • Exploratory Data Analysis (statistical summary, univariate analysis, visulize distributions, etc.)
  • Feature Engineering (Supports numeric, text, date/time. Image data support will integrate in later releases of v0.1)
  • Model Building (Currently supported for binary classification and regression only)
  • Hyper Parameter Tuning [in development for v0.2]
  • Cross Validation (will integrate in later releases of v0.1)
  • Model Performance Analysis, Explain Predictions (LIME and SHAP) and Performance Comparison Between Models.
  • JSON input script for executing model building and scoring tasks.
  • Model Building UI [in development for v0.2]
  • ML Model Building Project [in development for v0.2]
  • Auto ML (automated machine learning) [in development for v0.2]
  • Model Deploymet and Serving [included, will be imporved for v0.2]

Supported Machine Learning Algorithms/Packages

  • RandomForestClassifier: scikit-learn
  • LogisticRegression: statsmodels
  • Deep Feed Forward Neural Network (DFF): tensorflow
  • Convlutional Neural Network (CNN): tensorflow
  • Gradient Boost : catboost
  • Linear Regression: statsmodels
  • RandomForestRegressor: scikit-learn
  • ... More models will be added in the future releases ...

Usage

import mltk

Warning: Python Variable, Function or Class names

The Python interpreter has a number of built-in functions. It is possible to overwrite thier definitions when coding without any rasing a warning from the Python interpriter. (https://docs.python.org/3/library/functions.html) Therfore, AVOID THESE NAMES as your variable, function or class names.

If you accedently overwrite any of the built-in function (e.g. list), execute the following to bring built-in defition.

del(list)

Similarly, avoid using special charcters and spaces in the column names of the DataFrames. Execute the following to remove special characters from the column names.

Data = mltk.clean_column_names(Data, replace='')

MLToolkit Example

Data Loading and exploration

import numpy as np
import pandas as pd
import mltk as mltk

Data = mltk.read_data_csv(file=r'C:\Projects\Data\incomedata.csv')
Data = mltk.clean_column_names(Data, replace='')
Data = mltk.add_identity_column(Data, id_label='ID', start=1, increment=1)
DataStats = mltk.data_description(Data)

Data Pre-processing and Feature Engineering

# Analyze Response Target
print(mltk.variable_frequency(DataFrame=Data, variable='income'))

# Set Target Variables
targetVariable = 'HighIncome'
targetCondition = "income=='>50K'" #For Binary Classification

Data=mltk.set_binary_target(Data, target_condition=targetCondition, target_variable=targetVariable)
print(mltk.variable_frequency(DataFrame=Data, variable=targetVariable))
        Counts  CountsFraction%
income                         
<=50K    24720         75.91904
>50K      7841         24.08096
TOTAL    32561        100.00000
# Flag Records to Exclude
excludeCondition="age < 18"
action = 'flag' # 'drop' #
excludeLabel = 'EXCLUDE'
Data=mltk.exclude_records(Data, exclude_ondition=excludeCondition, action=action, exclude_label=excludeLabel) # )#

# Get list of uniques values in categorical variables
categoryVariables = set({'sex', 'nativecountry', 'race', 'occupation', 'workclass', 'maritalstatus', 'relationship'})
print(mltk.category_lists(Data, list(categoryVariables)))

# Merge unique categorical values
category_merges = [{'variable':'maritalstatus', 'category_variable':'maritalstatus', 'group_value':'Married', 'values':["Married-civ-spouse", "Married-spouse-absent", "Married-AF-spouse"]}]
Data = mltk.merge_categories(Data, category_merges)

# Show Frequency distribution of categorical variable
sourceVariable='maritalstatus'
table = mltk.variable_frequency(Data, variable=sourceVariable, show_plot=False)
table.style.background_gradient(cmap='Greens').set_precision(3)

# Response Rate For Categorical Variables
mltk.variable_responses(Data, variables=categoryVariables, target_variable=targetVariable, show_output=False, show_plot=True)

Get numeric units list

mltk.get_number_units()

Variables Manipulations

# General form
{
	'type':'category'
	'out_type':'cat',
	'include':True,
	'operation':'bucket',
	'variables': {
		'source':'age',
		'destination': None  # None for mult-variable operations, variable1 (for pair operations), variable1a (for pair sequence operation)
	},
        'parameters': {
        'labels_str': ['0', '20', '30', '40', '50', '60', 'INF'],
        'right_inclusive':True,
        "default":'OTHER',
        "null": 'NA'
    }
}
List of Avaiable Transformation
 |- Date/Numeric Transformations (transform)
 | |- normalize
 | |- datepart
 | |- dateadd
 | |- log
 | |- exponent
 | |- segment (piecewise functions)
 |- String Transformation (str_transform)
 | |- normalize
 | |- strcount
 | |- extract
 |- Multi-variable Operations (operation_mult)
 | |- expression
 |- Sequence Order Check (seq_order)
 | |- seqorder
 |- Numeric/Date Comparison* (comparison)
 | |- numdiff
 | |- ratio
 | |- datediff
 | |- rowmin (pair)
 | |- rowmax (pair)
 |- String Comparison* (str_comparison)
 | |- levenshtein
 | |- jaccard
 | |- ..more to add ..
 |- Pair comparison

List of Avaiable Discrete Feature Transforms
 |- Binary Variable (condition)
 |- Numeric to Catergory (buckets)
 |- Entity Grouping (dictionary)
 |- Pair Equality/Existance (pair_equality)
 |- Category Merge(category_merge)
# Transform numeric variable
rule_set = {
    "operation":"normalize", 
    'variables': {
        'source':'age', 
        'destination':'normalizedage'
    },
    "parameters":{"method":"zscore"}
}
Data, transformed_variable = mltk.create_transformed_variable_task(Data, rule_set, return_variable=True)

# Create Categorical Variables from continious variables
sourceVariable='age'
table = mltk.histogram(Data, sourceVariable, n_bins=10, orientation='vertical', density=True, show_plot=True)
print(table)

# Divide to categories
rule_set = {   
    'operation':'bucket',
    'variables': {
        'source':'age', 
        'destination':None
    },
    'parameters': {
        'labels_str': ['0', '20', '30', '40', '50', '60', 'INF'],
        'right_inclusive':True,
        "default":'OTHER',
        "null": 'NA'
    }
}
Data, categoryVariable = mltk.create_categorical_variable_task(Data, rule_set, return_variable=True)
mltk.variable_response(DataFrame=Data, variable=categoryVariable, target_variable=targetVariable, show_plot=True)
            Counts  HighIncome  CountsFraction%  ResponseFraction%  ResponseRate%
ageGRP                                                                           
1_(0,20]      2410           2          7.40149            0.02551        0.08299
2_(20,30]     8162         680         25.06680            8.67236        8.33129
3_(30,40]     8546        2406         26.24612           30.68486       28.15352
4_(40,50]     6983        2655         21.44590           33.86048       38.02091
5_(50,60]     4128        1547         12.67774           19.72963       37.47578
6_(60,INF)    2332         551          7.16194            7.02716       23.62779
TOTAL        32561        7841        100.00000          100.00000        0.24081
# Create One Hot Encoded Variables
Data, featureVariables, targetVariable = mltk.to_one_hot_encode(Data, category_variables=categoryVariables, binary_variables=binaryVariables, target_variable=targetVariable)
Data[identifierColumns+featureVariables+[targetVariable]].sample(5).transpose()

Correlation

correlation=mltk.correlation_matrix(Data, featureVariables+[targetVariable], target_variable=targetVariable, method='pearson', return_type='list', show_plot=False)

Split Train, Validate Test datasets

TrainDataset, ValidateDataset, TestDataset = mltk.train_validate_test_split(Data, ratios=(0.6,0.2,0.2))

Model Building

sample_attributes = {
						'SampleDescription':'Adult Census Income Dataset',
						'NumClasses':2,
						'RecordIdentifiers':identifierColumns
                }

score_parameters = {
					'Edges':[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
					'Percentiles':[0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0],
					'Threshold':0.5,
					'Quantiles':10,
					'ScoreVariable':'Probability',
					'ScoreLabel':'Score',
					'QuantileLabel':'Quantile',
					'PredictedLabel':'Predicted'
                }

Classification Models

Model Attributes

model_attributes = {
					'ModelID': None,
					'ModelType':'classification',
					'ModelName': 'IncomeLevel',
					'Version':'0.1',
                }

Losgistic Regression

model_parameters = {
					'MLAlgorithm':'LGR', # 'RF', #  'NN', # 'CATBST', (# 'CNN',  # 'XGBST')
					'MaxIterations':50
				}  

Random Forest

model_parameters = {
					'MLAlgorithm':'RF', # 'LGR', #  'NN', # 'CATBST', (# 'CNN',  # 'XGBST')
					'NTrees':500,
					'MaxDepth':100,
					'MinSamplesToSplit':10,
					'Processors':2
				} 

Neural Networks

# Setup Architecture
# Binary classification (L1 'units': 2), 32 variables ('input_shape':(48,))
SimpleDFF_architecture = {
        'L1':{'type': 'Dense', 'position':'input', 'units': 512, 'activation':'relu', 'input_shape':(48,)},
        'L2':{'type': 'Dense', 'position':'hidden', 'units': 512, 'activation':'relu'},
        'L3':{'type': 'Dropout', 'position':'hidden', 'rate':0.5},
        'L4':{'type': 'Dense', 'position':'output', 'units': 2, 'activation':'softmax', 'output_shape':None},
       }

# Binary classification (L1 'units': 2), 32 variables ('input_shape':(32,))
LogisticRegressionNN_architecture = {
        'L1':{'type': 'Dense', 'position':'input', 'units': 2, 'activation':'softmax', 'input_shape':(32,)},
       }

# Binary classification (L8 'units': 2)
SimpleImageClassifier_architecture = {
        'L1':{'type': 'Conv2D', 'position':'input', 'filters': 32, 'kernel_size':(3,3), 'strides':(1,1), 'padding':'valid', 'activation':'relu', 'input_shape':(128, 128, 1)},
        'L2':{'type': 'Conv2D', 'position':'hidden', 'filters': 64, 'kernel_size':(3,3), 'strides':(1,1), 'padding':'valid', 'activation':'relu'},
        'L3':{'type': 'MaxPooling2D', 'position':'hidden', 'pool_size': (2,2), 'padding':'valid'},   
        'L4':{'type': 'Dropout', 'position':'hidden', 'rate':0.25},
        'L5':{'type': 'Flatten', 'position':'hidden'},        
        'L6':{'type': 'Dense', 'position':'hidden', 'units': 128, 'activation':'relu'},
        'L7':{'type': 'Dropout', 'position':'hidden', 'rate':0.5},
        'L8':{'type': 'Dense', 'position':'output', 'units': 2, 'activation':'softmax', 'output_shape':None},
       }

model_parameters = {
				'MLAlgorithm':'NN',
				'BatchSize':512,
				'InputShape':InputShape,
				'num_classes':2,
				'Epochs':10,
				'metrics':['accuracy'],
				'architecture':SimpleDFF_architecture
				} 

CatBoost

model_parameters = {
					'MLAlgorithm':'CBST',
					'NTrees': 500,
					'MaxDepth':10,
					'LearningRate':0.7,
					'LossFunction':'Logloss',#crossEntropy
					'EvalMatrics':'Accuracy',
					'Imbalanced':False,
					'TaskType':'GPU',
					'Processors':2,
					'UseBestModel':True
				}

Build Model

XModel = mltk.build_ml_model(TrainDataset, ValidateDataset, TestDataset, 
                                  model_variables=modelVariables,
                                  variable_setup = None,
                                  target_variable=targetVariable,
                                  model_attributes=model_attributes, 
                                  sample_attributes=sample_attributes, 
                                  model_parameters=model_parameters, 
                                  score_parameters=score_parameters, 
                                  return_model_object=True, 
                                  show_results=False, 
                                  show_plot=True
                                  )

print(XModel.model_attributes['ModelID'])
print(XModel.model_interpretation['ModelSummary'])
print('ROC AUC: ', XModel.get_auc(curve='roc'))
print('PRC AUC: ', XModel.get_auc(curve='prc'))
print(XModel.model_evaluation['RobustnessTable'])

XModel.plot_eval_matrics(comparison=False)
          minProbability  maxProbability  meanProbability  BucketCount  ResponseCount  BucketFraction  ResponseFraction  BucketPrecision  CumulativeBucketFraction  CumulativeResponseFraction  CumulativePrecision
Quantile                                                                                                                                                                                                           
1                0.00000         0.00008      3.85729e-06          652            3         0.10011           0.00192          0.00460                   1.00000                     1.00000              0.23967
2                0.00008         0.00432      1.52655e-03          651            9         0.09995           0.00577          0.01382                   0.89989                     0.99808              0.26582
3                0.00435         0.02042      1.10941e-02          652           14         0.10011           0.00897          0.02147                   0.79994                     0.99231              0.29731
4                0.02049         0.05702      3.58648e-02          650           20         0.09980           0.01281          0.03077                   0.69983                     0.98334              0.33677
5                0.05711         0.12075      8.51409e-02          652           65         0.10011           0.04164          0.09969                   0.60003                     0.97053              0.38767
6                0.12086         0.20457      1.63366e-01          651          109         0.09995           0.06983          0.16743                   0.49992                     0.92889              0.44533
7                0.20469         0.31870      2.61577e-01          651          190         0.09995           0.12172          0.29186                   0.39997                     0.85906              0.51478
8                0.31895         0.46840      4.03550e-01          666          259         0.10226           0.16592          0.38889                   0.30002                     0.73735              0.58905
9                0.46854         0.66965      5.68083e-01          641          377         0.09842           0.24151          0.58814                   0.19776                     0.57143              0.69255
10               0.66994         0.99967      8.06834e-01          647          515         0.09934           0.32992          0.79598                   0.09934                     0.32992              0.79598
DataSet          0.00000         0.99967      2.33167e-01         6513         1561         1.00000           1.00000          0.23967                   1.00000                     1.00000              0.23967

Evaluate Model

Plot model performance curves

RFModel.plot_eval_matrics(comparison=True)
LGRModel.plot_eval_matrics(comparison=True)
NNModel.plot_eval_matrics(comparison=True)
CBSTModel.plot_eval_matrics(comparison=True)

Area Under Curve (AUC) Comparison

Models = [LGRModel, RFModel, CBSTModel, NNModel]
ModelsComp = mltk.model_guages_comparison(Models)
print(ModelsComp)
                           Model  PRC_AUC  ROC_AUC
0   INCOMELEVELLGR20190728113633  0.71971  0.88926
1    INCOMELEVELRF20190728113635  0.69348  0.88113
2  INCOMELEVELCBST20190728113703  0.71507  0.88975
3    INCOMELEVELNN20190728113641  0.71396  0.88890

Test Model

score_variable = RFModel.get_score_variable()
score_label = RFModel.get_score_label()

TestDataset = mltk.score_processed_dataset(TestDataset, RFModel, edges=None, score_label=None, fill_missing=0)

threshold = 0.8
TestDataset = mltk.set_predicted_columns(TestDataset, score_variable, threshold=threshold)
ConfusionMatrix = mltk.confusion_matrix(TestDataset, actual_variable=targetVariable, predcted_variable='Predicted', labels=[0,1], sample_weight=None, totals=True)
print(ConfusionMatrix)

Comparing Models and Probability Thresholds

Models = [LGRModel, RFModel, CBSTModel, NNModel]
thresholds=[0.7, 0.8, 0.9]
ConfusionMatrixComparison = mltk.confusion_matrix_comparison(TestDataset, Models, thresholds, score_variable=None, show_plot=True)
ConfusionMatrixComparison.style.background_gradient(cmap='RdYlGn').set_precision(3)

Comparing Models and Threshold Score (1-10 Scale)

Models = [LGRModel, RFModel, CBSTModel, NNModel]
thresholds=[7, 8, 9]
ConfusionMatrixComparison = mltk.confusion_matrix_comparison(TestDataset, Models, thresholds, score_variable=score_label, show_plot=True)
ConfusionMatrixComparison.style.background_gradient(cmap='RdYlGn').set_precision(3)

Set Custom Score Edges

RobustnessTable, ROCCurve, PrecisionRecallCurve, roc_auc, prc_auc = mltk.model_performance_matrics(ResultsSet=TestDataset, target_variable=targetVariable, score_variable=score_variable, quantile_label='Quantile',  quantiles=100, show_plot=True)
print('ROC AUC', roc_auc)
print('PRC AUC', prc_auc)

print(RobustnessTable)

# Examine cutoffs
quantiles=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
edges, threshold = mltk.get_score_cutoffs(ResultsSet=TestDataset, quantiles=quantiles, target_variable=targetVariable, score_variable=scoreVariable)
print('Threshold', threshold)
print('Edges', edges)

# Re-bin score buckets
edges = [0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.75, 0.95, 1.0]
LGRModel.set_score_edges(edges)

Regression Models

Model Attributes

model_attributes = {
					'ModelID': None,   
					'ModelType':'regression',
					'ModelName': 'Income',
					'Version':'0.1',
                   }
model_parameters = {
					'MLAlgorithm':'RFREG', # 'RFREG'
					'NTrees':200,
					'MaxDepth':10,
					'MinSamplesToSplit':6,
					'Processors':2
				   } 
model_parameters = {'MLAlgorithm':'LREG', 'MaxIterations':100}
REGModel = mltk.build_ml_model(TrainDataset, ValidateDataset, TestDataset, 
                                  model_variables=modelVariables,
                                  variable_setup = None,
                                  target_variable=targetVariable,
                                  model_attributes=model_attributes, 
                                  sample_attributes=sample_attributes, 
                                  model_parameters=model_parameters, 
                                  score_parameters=score_parameters, 
                                  return_model_object=True, 
                                  show_results=False, 
                                  show_plot=False
                                  )
print(REGModel.model_attributes['ModelID'])
print(REGModel.model_interpretation['ModelSummary'])
print('RMSE =', SelectModel.get_rmse())
print('R^2 =', SelectModel.get_r2())
REGModel.plot_eval_matrics(comparison=True)
SelectModel.plot_eval_matrics(comparison=True)

Save model

saveFilePath = '{}.pkl'.format(XModel.get_model_id())
mltk.save_model(XModel, saveFilePath)

Deployment

Simplified MLToolkit ETL pipeline for scoring and model re-building (Need to customize based on the project).

Define ETL Function

def ETL(DataFrame, variables_setup_dict=None):
    # Add ID column
    DataFrame = mltk.add_identity_column(DataFrame, id_label='ID', start=1, increment=1)

    # Clean column names
    DataFrame = mltk.clean_column_names(DataFrame, replace='')
    input_columns = list(DataFrame.columns)

	if variables_setup_dict==None:
		variables_setup_dict = """   
		{
			"setting":"score",

			"variables": {            
					"category_variables" : ["sex", "race", "occupation", "workclass", "maritalstatus", "relationship"],
					"binary_variables": [],
					"target_variable":"HighIncome"
			},

			"preprocess_tasks": [
				{
					"type": "transform",
					"out_type":"cnt",
					"include": false,
					"operation": "normalize",
					"variables": {
						"source": "age",
						"destination": "normalizedage"
					},
					"parameters": {
						"method": "zscore"
					}
				},
				{
					"type": "category_merge",
					"out_type":"cat",
					"include": true,
					"operation": "catmerge",
					"variables": {
						"source": "maritalstatus",
						"destination": "maritalstatus"
					},
					"parameters": {
						"group_value": "Married",
						"values": [ "Married-civ-spouse", "Married-spouse-absent", "Married-AF-spouse" ]
					}
				},
				{
					"type": "entity",
					"out_type":"cat",
					"include": true,
					"operation": "dictionary",
					"variables": {
						"source": "nativecountry",
						"destination": "nativecountryGRP"
					},
					"parameters": {
						"match_type": null,
						"dictionary": [
							{
								"entity": "USA",
								"values": [ "United-States" ],
								"case": true
							},
							{
								"entity": "Canada",
								"values": [ "Canada" ],
								"case": true
							},
							{
								"entity": "OtherAmericas",
								"values": [ "South", "Mexico", "Trinadad&Tobago", "Jamaica", "Peru", "Nicaragua", "Dominican-Republic", "Haiti", "Ecuador", "El-Salvador", "Columbia", "Honduras", "Guatemala", "Puerto-Rico", "Cuba", "Outlying-US(Guam-USVI-etc)"],
								"case": true
							},
							{
								"entity": "Europe-Med",
								"values": [ "Greece", "Holand-Netherlands", "Poland", "Iran", "England", "Germany", "Italy", "Ireland", "Hungary", "France", "Yugoslavia", "Scotland", "Portugal" ],
								"case": true
							},
							{
								"entity": "Asia",
								"values": [ "Vietnam", "China", "Taiwan", "India", "Philippines", "Japan", "Hong", "Cambodia", "Laos", "Thailand" ],
								"case": true
							},
							{
								"entity": "Other",
								"values": [ "?" ],
								"case": true
							}
						],
						"null": "NA",
						"default": "OTHER"
					}
				},
				{
					"type": "category",
					"out_type":"cat",
					"include": true,
					"operation": "bucket",
					"variables": {
						"source": "age",
						"destination": null
					},
					"parameters": {
						"labels_str": [ "0", "20", "30", "40", "50", "60", "INF" ],
						"right_inclusive": true,
						"default": "OTHER",
						"null": "NA"
					}
				},
				{
					"type": "category",
					"out_type":"cat",
					"include": true,
					"operation": "bucket",
					"variables": {
						"source": "educationnum",
						"destination": null
					},
					"parameters": {
						"labels_str": [ "1", "5", "8", "9", "12", "16" ],
						"right_inclusive": true,
						"default": "OTHER",
						"null": "NA"
					}
				},
				{
					"type": "category",
					"out_type":"cat",
					"include": true,
					"operation": "bucket",
					"variables": {
						"source": "hoursperweek",
						"destination": null
					},
					"parameters": {
						"labels_str": [ "0", "20", "35", "40", "60", "INF" ],
						"right_inclusive": true,
						"default": "OTHER",
						"null": "NA"
					}
				}
			]
		}
		"""

    DataFrame, categoryVariables, binaryVariables, targetVariable = mltk.setup_variables_task(DataFrame, variables_setup_dict)

    # Create One Hot Encoded Variables
    DataFrame, featureVariables, targetVariable = mltk.to_one_hot_encode(DataFrame, category_variables=categoryVariables, binary_variables=binaryVariables, target_variable=targetVariable)

    return DataFrame, input_columns

Scoring/Ranking

MLModelObject = mltk.load_model(saveFilePath)
SampleDataset = pd.read_csv(r'test.csv')
SampleDataset = ETL(SampleDataset)

SampleDataset = mltk.score_processed_dataset(SampleDataset, MLModelObject, edges=None, score_label=None, fill_missing=0)
Robustnesstable1 = mltk.robustness_table(ResultsSet=SampleDataset, class_variable=targetVariable, score_variable=score_variable,  score_label=score_label, show_plot=True)
MLModelObject = mltk.load_model(saveFilePath)

TestInput = """
{
      "ID": "A001",
      "age": 32,
      "workclass": "Private",
      "education": "Doctorate",
      "education-num": 16,
      "marital-status": "Married-civ-spouse",
      "occupation": "Prof-specialty",
      "relationship": "Husband",
      "race": "Asian-Pac-Islander",
      "sex": "Male",
      "capital-gain": 0,
      "capital-loss": 0,
      "hours-per-week": 40,
      "native-country": "?"
}
"""
output = mltk.score_records(TestInput, MLModelObject, edges=None, ETL=ETL, return_type='dict') # Other options for return_type, {'json', 'frame'}

Output

[{'ID': 'A001',
 'age': 32,
 'capitalgain': 0,
 'capitalloss': 0,
 'education': 'Doctorate',
 'educationnum': 16,
 'hoursperweek': 40,
 'maritalstatus': 'Married',
 'nativecountry': '?',
 'occupation': 'Prof-specialty',
 'race': 'Asian-Pac-Islander',
 'relationship': 'Husband',
 'sex': 'Male',
 'workclass': 'Private',
 'Probability': 0.6790258814478549,
 'Score': 7}]

Model Output Explanation (Using SHAP and LIME Python libraries)

# Create Explainer
Explainer = mltk.build_explainer(MLModelObject, explainer_config={'IdColumns':['ID'], 'Method':'shap', 'ClassNumber':1, 'FillMissing':0})

save_file_path = '{}_Explainer.pkl'.format(MLModelObject.get_model_id())
mltk.save_explainer(Explainer, save_file_path)

Explainer = mltk.load_explainer(save_file_path)
# Calculate Impact Values
ImpactValues, VariableValues = mltk.get_explainer_values_task(DataFrame, Explainer=Explainer, verbose=False)

# Plot Variable Impact
# force_plot
explainer_visual = mltk.get_explainer_visual(ImpactValues, VariableValues, Explainer, visual_config={'figsize':[20,4], 'text_rotation':90})

# Generate Explain Summary
explainer_summary = mltk.get_shap_impact_summary(ImpactValues, VariableValues, Explainer.get_model_variables(), iloc=0, top_n=5, show_plot=True)

explainer_report, explain_plot = mltk.get_explainer_report(DataFrame, Explainer, top_n=10, show_plot=True, return_type='frame')

JSON Input for scoring

Records Format for single or fewer number of records

[{
	"ID": "A001",
	"age": 32,
	"workclass": "Private",
	"education": "Doctorate",
	"occupation": "Prof-specialty",
	"sex": "Female",
	"hoursperweek": 40,
	"nativecountry": "USA"
}]

Split Format for mulltiple records

{
	"columns":["ID","age","education","hoursperweek","nativecountry","occupation","sex","workclass"],
	"data":[["A001",32,"Doctorate",40,"USA","Prof-specialty","Female","Private"]]
}

Using Model Chest to Deploy Models

MyModelChest = mltk.ModelChest()
MyModelChest.add_model(model_key='test', model_file=None, model_object=MLModelObject, replace=False)
MyModelChest.save_model_chest()
MyModelChest.get_model_chest_json()

load Models from Model Chest

lodedModel = MyModelChest.get_model_object('test')
lodedModel.get_model_manifest()

Working with Image Data

size=(96, 64)
file_folder_path = r'C:\Projects\Data\images\train'
ImagesDataFrame = mltk.read_image_folder(file_folder_path, size=size, show_image=False)
ImagesDataFrame, input_shape = mltk.prepare_image_dataset_to_model(ImagesDataFrame, 
                                                             image_column='Image', 
                                                             processed_image_column='ImageToModel',
                                                             label_column='Label',
                                                             image_data_format='channels_last', 
                                                             size=size)

Building CNN Model

sample_attributes = {'SampleDescription':'Image CLassification Example',
                    'NumClasses':NClasses,
                    'RecordIdentifiers':identifierColumns,
                    'ModelDataStats':modelDataStats
                    }

score_parameters = {'Edges':[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
                    'Percentiles':[0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 1.0],
                    'Threshold':0.5,
                   'Quantiles':10,
                   'ScoreVariable':'Probability',
                   'ScoreLabel':'Score',
                   'QuantileLabel':'Quantile',
                   'PredictedLabel':'Predicted'
                   }

model_attributes = {
                    'ModelID': None,   
                    'ModelType':'classification',
                    'ModelName': 'IncomeLevel',
                    'Version':'0.1',
                   }

architecture = {
        'L1':{'type': 'Conv2D', 'position':'input', 'filters':32, 'kernel_size':(3,3), 'padding':'same', 'strides':(1,1), 'activation':'relu', 'input_shape':input_shape},
        'L2':{'type': 'MaxPooling2D', 'pool_size': (2,2), 'padding':'same'},
        'L3':{'type': 'Dropout', 'position':'hidden', 'rate':0.2},
        'L4':{'type': 'Conv2D', 'position':'hidden', 'filters':64, 'kernel_size':(3,3), 'padding':'same', 'strides':(1,1), 'activation':'relu'},
        'L5':{'type': 'MaxPooling2D', 'pool_size': (2,2), 'padding':'same'},
        'L6':{'type': 'Dropout', 'position':'hidden', 'rate':0.2},
        'L7':{'type': 'Flatten'},
        'L8':{'type': 'Dense', 'position':'hidden', 'units': 256, 'activation':'softmax', 'output_shape':None},
        'L9':{'type': 'Dropout', 'position':'hidden', 'rate':0.2},
        'L10':{'type': 'Dense', 'position':'output', 'units': n_classes, 'activation':'softmax', 'output_shape':None},
       }	

model_parameters = {'MLAlgorithm':'CNN',
                    'BatchSize':128,
                   'InputShape':inputShape,
                   'NumClasses':NClasses,
                   'Epochs':50,
                   'EvalMatrics':['accuracy'],
                   'Architecture':architecture} 

CNNModel = mltk.build_ml_model(TrainDataset, ValidateDataset, TestDataset, 
                                  model_variables=modelVariables,
                                  variable_setup = None,
                                  target_variable=targetVariable,
                                  model_attributes=model_attributes, 
                                  sample_attributes=sample_attributes, 
                                  model_parameters=model_parameters, 
                                  score_parameters=score_parameters, 
                                  return_model_object=True, 
                                  show_results=False, 
                                  show_plot=True
                                  )

CNNModel.plot_eval_matrics()						  

License

Copyright 2019 Sumudu Tennakoon

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License 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.

Cite as

@misc{mltk2019,
  author =  "Sumudu Tennakoon",
  title = "MLToolKit(mltk): A Simplified Toolkit for End-To-End Machine Learing Projects",
  year = 2019,
  publisher = "GitHub",
  howpublished = {\url{https://mltoolkit.github.io/mltk/}},
  version = "0.1.9"
}

MLToolKit Project Timeline

  • 2018-07-02 [v0.0.1]: Initial set of functions for data exploration, model building and model evaluation was published to Github. (https://github.com/sptennak/MachineLearning).
  • 2018-01-03 [v0.0.2]: Created more functions for data exploration including web scraping and geo spacial data analysis for for IBM Coursera Data Science Capstone Project was published to Github. (https://github.com/sptennak/Coursera_Capstone).
  • 2019-03-20 [v0.1.0]: Developed and published initial version of model building and serving framework for IBM Coursera Advanced Data Science Professional Certificate Capstone Project. (https://github.com/sptennak/IBM-Coursera-Advanced-Data-Science-Capstone).
  • 2019-07-02 [v0.1.2]: First release of the PyMLToolkit Python package, a collection of clases and functions facilitating end-to-end machine learning model building and serving over RESTful API.
  • 2019-07-04 [v0.1.3]: Minor bug fixes.
  • 2019-07-14 [v0.1.4]: Improved documentation, Integrated TensorFlow Models, Enhancements and Minor bug fixes.
  • 2019-07-28 [v0.1.5]: Integrated CatBoost Models, Improved model building and serving frameework, text analytics functions, support JSON input/output to the ML model bulding and scoring processes, Enhancements and bug fixes.
  • 2019-08-12 [v0.1.6]: Improved Features, Bug Fixes, Enhanced JSON input/output to the ML model bulding and scoring processes (JSON-MLS) and bug fixes.
  • 2019-08-31 [v0.1.7] : Added more data processing functions, Enhanced output formats, Enhanced model deployment, Overall improvements and bug fixes.
  • 2019-09-28 [v0.1.8] : Improved Documentation, Enhancements and bug fixes.
  • 2019-12-07 [v0.1.9] : Added model explainability, Integrate image classification model Deployment, Enhancements and bug fixes.

Future Release Plan

  • TBD [v0.1.10] : Working with Imbalanced Samples, Integrate Cross-validation, Post additional tutorials and examples, Improved Documentation, Enhancements and bug fixes.
  • TBD [v0.1.11] : Building Ensamble Models, UI Preview, Improved Feature Selection, Cross-validation and Hyper parameter tuning functionality, Enhancements and bug fixes.
  • TBD [v0.1.12]: ML Model Building Projects, Enhancements and bug fixes.
  • 2019-12-31 [v0.1.13]:Comprehensive documentation, Post implementation evaluation functions, Enhanced Data Input and Output functios, Major bug-fix version of the initial release with finalized enhancements.
  • TBD [v0.2.0]: Imporved model building and serving frameework and UI, Support more machine learning algorithms, Support multi-class classification and enhanced text analytics functions.
  • TBD [v0.3.0]: Imporved scalability and performance, Automated Machine Learning.
  • TBD [v0.4.0]: Building continious learning models.

Acknowledgement and Remarks

Some functions of MLToolKit depends on number of Open Source Python Libraries such as

  • Data Manipulation : Pandas
  • Machine Learning: Statsmodels, Scikit-learn, Catboost
  • Deep Learning: Tensorflow,
  • Model Interpretability: Shap, Lime
  • Server Framework: Flask
  • Text Processing: BeautifulSoup, TextLab
  • Database Connectivity: SQLAlchemy, PyODBC MLToolkit Project acknowledge the creators and contributors of the above libraries for their contribution to the Open Source Community.

MLToolKit library and some novel concepts introduced with original ideas of the author implemented as an effort of putting together the lifetime learning and experience working on multiple data science projects to a good use and as a contribution back to the Open Source Community.

Author would like to thank number of content creators in the data science and machine learning topics not limited to online learning platforms and blogs for making aviable insightful resources to explore and learn the subject. A complete reference list will be published with a future version...

As a Free and Open Source initiative and a independent R&D project, author has no conflict of interest or, financial interest to the MLToolKit library. However, proper mention of the source abiding the License Terms is highly appreciated when the library itself or any useful concepts or parts are used.

MLToolKit is set to evolve with adding more features and functionality, and interoperability with more standard data science and machine learning libraries. MLToolKit will always be available as Free and Open Source Python library in the future.

References

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