fast-ml 3.67

Package by the Data-Scientists for the Data Scientists ; with Scikit-learn type fit() transform() functionality

Fast-ML is a Python package with numerous inbuilt functionalities to make the life of a data scientist much easier

fast_ml follow Scikit-learn type functionality with fit() and transform() methods to first learn the transforming parameters from training dataset and then transforms the training/validation/test dataset

Important Note : You learn the parameter by applying fit() method ONLY on train method and then apply transform on train/valid/test dataset. Be it Missing Value Imputation, Outliers, Feature Engineering for Numerical/Categorical ... Parameters are learned from the training dataset on which the model trains.

Installing

pip install fast_ml

Table of Contents:

1. Utilities
2. Exploratory Data Analysis (EDA)
3. Missing Data Analysis
4. Missing Data Imputation
5. Outlier Treatment
6. Feature Engineering
7. Feature Selection
8. Model Development
9. Model Evaluation

Glossary

• df : Dataframe, refers to dataset used for analysis
• variable : str, refers to a single variable. As required in the function it has to be passed ex 'V1'
• variables : list type, refers to list of variables. Must be passed as list ex ['V1', 'V2]. Even a single variable has to be passed in list format. ex ['V1']
• target : str, refers to target variable
• model : str, ML problem type. use 'classification' or 'clf' for classification problems and 'regression' or 'reg' for regression problems
• method : str, refers to various techniques available for Missing Value Imputation, Feature Engieering... as available in each module

1. Utilities

from fast_ml.utilities import reduce_memory_usage, display_all

# reduces the memory usage of the dataset by optimizing for the datatype used for storing the data
train = reduce_memory_usage(train, convert_to_category=False)

1. reduce_memory_usage(df, convert_to_category = False)
   • This function reduces the memory used by dataframe
2. display_all(df)
   • Use this function to show all rows and all columns of dataframe. By default pandas only show top and bottom 20 rows, columns

2. Exploratory Data Analysis (EDA)

from fast_ml import eda

2.1) Overview

from fast_ml import eda

train = pd.read_csv('train.csv')

# One of the most useful dataframe summary view
summary_df = eda.df_info(train)
display_all(summary_df)

1. eda.df_info(df)
   • Returns a dataframe with useful summary - variables, datatype, number of unique values, sample of unique values, missing count, missing percent
2. eda.df_cardinality_info(df, raw_data = True)
   • Returns a dataframe with useful summary - variables, datatype, number of unique values, sample of unique values, missing count, missing percent
3. eda.df_missing_info(df, raw_data = True)
   • Returns a dataframe with useful summary - variables, datatype, number of unique values, sample of unique values, missing count, missing percent

2.2) Numerical Variables

from fast_ml import eda

train = pd.read_csv('train.csv')

#one line of command to get commonly used plots for all the variables provided to the function
eda.numerical_plots_with_target(train, num_vars, target, model ='clf')

1. eda.numerical_describe(df, variables=None, method='10p')
   • Dataframe with variouls count, mean, std and spread statistics for all the variables passed in input
2. eda.numerical_variable_detail(df, variable, model = None, target=None, threshold = 20)
   • Various summary statistics, spread statistics, outlier, missing values, transformation diagnostic... a detailed analysis for a single variable provided as input
3. eda.numerical_plots(df, variables, normality_check = False)
   • Uni-variate plots - Variable Distribution of all the numerical variables provided as input with target. Can also get the Q-Q plot for assessing the normality
4. eda.numerical_plots_with_target(df, variables, target, model)
   • Bi-variate plots - Scatter plot of all the numerical variables provided as input with target.
5. eda.numerical_check_outliers(df, variables=None, tol=1.5, print_vars = False)
6. eda.numerical_bins_with_target(df, variables, target, model='clf', create_buckets = True, method='5p', custom_buckets=None)
   • Useful for deciding the suitable binning for numerical variable. Displays 2 graphs 'overall event rate' & 'within category event rate'

2.3) Categorical Variables

from fast_ml import eda

train = pd.read_csv('train.csv')

#one line of command to get commonly used plots for all the variables provided to the function
eda.categorical_plots_with_target(train, cat_vars, target, add_missing=True, rare_tol=5)

1. eda.categorical_variable_detail(df, variable, model = None, target=None, rare_tol=5)
   • Various summary statistics, missing values, distributions ... a detailed analysis for a single variable provided as input
2. eda.categorical_plots(df, variables, add_missing = True, add_rare = False, rare_tol=5)
   • Uni-variate plots - distribution of all the categorical provided as input
3. eda.categorical_plots_with_target(df, variables, target, model='clf', add_missing = True, rare_tol1 = 5, rare_tol2 = 10)
   • Bi-variate plots - distribution of all the categorical provided as input with target
4. eda.categorical_plots_with_rare_and_target(df, variables, target, model='clf', add_missing=True, rare_tol1=5, rare_tol2=10)
   • Bi-variate plots - distribution of all the categorical provided as input with target with 2 inputs as rare threshold. Useful for deciding the rare bucketing
5. eda.categorical_plots_for_miss_and_freq(df, variables, target, model = 'reg')
   • Uni-variate plots - distribution of all the categorical provided as input with target with 2 inputs as rare threshold. Useful for deciding the rare bucketing

3. Missing Data Analysis

from fast_ml.missing_data_analysis import MissingDataAnalysis

2.1) Class MissingDataAnalysis

1. explore_numerical_imputation (variable)
2. explore_categorical_imputation (variable)

4. Missing Data Imputation

from fast_ml.missing_data_imputation import MissingDataImputer_Numerical, MissingDataImputer_Categorical

4.1) class MissingDataImputer_Numerical

from fast_ml.missing_data_imputation import MissingDataImputer_Numerical

train = pd.read_csv('train.csv')

num_imputer = MissingDataImputer_Numerical(df, method = 'median')

#Scikit-learn type fit() transform() functionality
# Use fit() only on the train dataset
num_imputer.fit(train, num_vars)

# Use transform() on train/test dataset
train = num_imputer.transform(train)
test = num_imputer.transform(test)

• Methods:
  • 'mean'
  • 'median'
  • 'mode'
  • 'custom_value'
  • 'random'

1. fit(df, num_vars)
2. transform(df)

4.2) class MissingDataImputer_Categorical

from fast_ml.missing_data_imputation import MissingDataImputer_Categorical

train = pd.read_csv('train.csv')

cat_imputer = MissingDataImputer_Categorical(df, method = 'frequent')

#Scikit-learn type fit() transform() functionality
# Use fit() only on the train dataset
cat_imputer.fit(train, cat_vars)

# Use transform() on train/test dataset
train = cat_imputer.transform(train)
test = cat_imputer.transform(test)

• Methods:
  • 'frequent' or 'mode'
  • 'custom_value'
  • 'random'

1. fit(df, cat_vars)
2. transform(df)

5. Outlier Treatment

from fast_ml.outlier_treatment import OutlierTreatment

5.1) class OutlierTreatment

• Methods:
  • 'iqr' or 'IQR'
  • 'gaussian'

1. fit(df, num_vars)
2. transform(df)

6. Feature Engineering

from fast_ml.feature_engineering import FeatureEngineering_Numerical, FeatureEngineering_Categorical, FeatureEngineering_DateTime

6.1) class FeatureEngineering_Numerical

from fast_ml.feature_engineering import FeatureEngineering_Categorical

num_binner = FeatureEngineering_Numerical(method = '10p', adaptive = True)

#Scikit-learn type fit() transform() functionality
# Use fit() only on the train dataset
num_binner.fit(train, num_vars)

# Use transform() on train/test dataset
train = num_binner.transform(train)
test = num_binner.transform(test)

• Methods:
  • '5p' : [0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100]
  • '10p' : [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
  • '20p' : [0, 20, 40, 60, 80, 100]
  • '25p' : [0, 25, 50, 75, 100]
  • '95p' : [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 100]
  • '98p' : [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 98, 100]
  • 'custom' : Custom Buckets

1. fit(df, num_vars)
2. transform(df)

6.2) class FeatureEngineering_Categorical(model=None, method='label', drop_last=False):

from fast_ml.feature_engineering import FeatureEngineering_Categorical

rare_encoder_5 = FeatureEngineering_Categorical(method = 'rare')

#Scikit-learn type fit() transform() functionality
# Use fit() only on the train dataset
rare_encoder_5.fit(train, cat_vars, rare_tol=5)

# Use transform() on train/test dataset
train = rare_encoder_5.transform(train)
test = rare_encoder_5.transform(test)

• Methods:
  • 'rare_encoding' or 'rare'
  • 'label' or 'integer'
  • 'count'
  • 'freq'
  • 'ordered_label'
  • 'target_ordered'
  • 'target_mean'
  • 'target_prob_ratio'
  • 'target_woe'

1. fit(df, cat_vars, target=None, rare_tol=5)
2. transform(df)

6.3) class FeatureEngineering_DateTime (drop_orig=True)

from fast_ml.feature_engineering import FeatureEngineering_DateTime

dt_encoder = FeatureEngineering_DateTime()

#Scikit-learn type fit() transform() functionality
# Use fit() only on the train dataset
dt_encoder.fit(train, datetime_vars, prefix = 'default')

# Use transform() on train/test dataset
train = dt_encoder.transform(train)
test = dt_encoder.transform(test)

1. fit(df, datetime_variables, prefix = 'default')
2. transform(df)

7. Feature Selection

from fast_ml.feature_selection import get_constant_features

constant_features = get_constant_features(df, threshold=0.99, dropna=False)
# constant_features is a dataframe
display_all(constant_features)

# to get list of constant features
constant_feats = (constant_features['Var'].to_list()
print(constant_feats)

1. get_constant_features(df, threshold=0.99, dropna=False)
2. get_duplicate_features(df)
3. get_correlated_pairs(df, threshold=0.9)
4. recursive_feature_elimination(model, X_train, y_train, X_valid, y_valid, X_test, y_test)
5. variables_clustering (df, variables, method)

8. Model Development

from fast_ml.model_development import train_valid_test_split

X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(df, target = target, 
                                                                            train_size=0.8, valid_size=0.1, test_size=0.1)

# Get the shape of all the datasets
print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)

1. train_valid_test_split(df, target, train_size=0.8, valid_size=0.1, test_size=0.1, method='random', sort_by_col = None, random_state=None)
2. all_classifiers(X_train, y_train, X_valid, y_valid, X_test=None, y_test=None, threshold_by = 'ROC AUC' ,verbose = True)

9. Model Evaluation

from fast_ml.model_evaluation import threshold_evaluation

threshold_df = threshold_evaluation(y_true, y_prob, start=0, end=1, step_size=0.1)

display_all(threshold_df)

1. model_save (model, model_name)
2. model_load (model_name)
3. plot_confidence_interval_for_data (model, X)
4. plot_confidence_interval_for_variable (model, X, y, variable)
5. threshold_evaluation(y_true, y_prob, start=0, end=1, step_size=0.1)
6. metrics_evaluation(y_true, y_pred_prob=None, y_pred=None, threshold=None, df_type='train')

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