pandas-ml-utils 0.0.13

Augment pandas DataFrame with methods for machine learning

Pandas ML Utils

Pandas ML Utils is intended to help you through your journey of applying statistical oder machine learning models to data while you never need to leave the world of pandas.

1. install
2. analyze your features
3. find a model
4. save and reuse your model

Install

pip install pandas-ml-utils

Analyze your Features

The feature_selection functionality helps you to analyze your features, filter out highly correlated once and focus on the most important features. This function also applies an auto regression and embeds and ACF plot.

import pandas_ml_utils as pmu
import pandas as pd

df = pd.read_csv('burritos.csv')[["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling", "Uniformity", "Salsa", "Synergy", "Wrap", "overall"]]
df.feature_selection(label_column="overall")

          Tortilla   overall   Synergy  Fillings      Temp     Salsa  \
Tortilla       1.0  0.403981  0.367575  0.345613  0.290702  0.267212   

              Meat  Uniformity  Meat:filling      Wrap  
Tortilla  0.260194    0.208666      0.207518  0.160831  
label is continuous: True

Feature ranking:
['Synergy', 'Meat', 'Fillings', 'Meat:filling', 'Wrap', 'Tortilla', 'Uniformity', 'Salsa', 'Temp']

TOP 5 features
         Synergy      Meat  Fillings  Meat:filling     Wrap
Synergy      1.0  0.601545  0.663328      0.428505  0.08685

filtered features with correlation < 0.5
           Synergy  Meat:filling      Wrap
Tortilla  0.367575      0.207518  0.160831

Synergy       1.000000
Synergy_0     1.000000
Synergy_1     0.147495
Synergy_56    0.128449
Synergy_78    0.119272
Synergy_55    0.111832
Synergy_79    0.086466
Synergy_47    0.085117
Synergy_53    0.084786
Synergy_37    0.084312
Name: Synergy, dtype: float64

Meat:filling       1.000000
Meat:filling_0     1.000000
Meat:filling_15    0.185946
Meat:filling_35    0.175837
Meat:filling_1     0.122546
Meat:filling_87    0.118597
Meat:filling_33    0.112875
Meat:filling_73    0.103090
Meat:filling_72    0.103054
Meat:filling_71    0.089437
Name: Meat:filling, dtype: float64

Wrap       1.000000
Wrap_0     1.000000
Wrap_63    0.210823
Wrap_88    0.189735
Wrap_1     0.169132
Wrap_87    0.166502
Wrap_66    0.146689
Wrap_89    0.141822
Wrap_74    0.120047
Wrap_11    0.115095
Name: Wrap, dtype: float64
best lags are
[(1, '-1.00'), (2, '-0.15'), (88, '-0.10'), (64, '-0.07'), (19, '-0.07'), (89, '-0.06'), (36, '-0.05'), (43, '-0.05'), (16, '-0.05'), (68, '-0.04'), (90, '-0.04'), (87, '-0.04'), (3, '-0.03'), (20, '-0.03'), (59, '-0.03'), (75, '-0.03'), (91, '-0.03'), (57, '-0.03'), (46, '-0.02'), (48, '-0.02'), (54, '-0.02'), (73, '-0.02'), (25, '-0.02'), (79, '-0.02'), (76, '-0.02'), (37, '-0.02'), (71, '-0.02'), (15, '-0.02'), (49, '-0.02'), (12, '-0.02'), (65, '-0.02'), (40, '-0.02'), (24, '-0.02'), (78, '-0.02'), (53, '-0.02'), (8, '-0.02'), (44, '-0.01'), (45, '0.01'), (56, '0.01'), (26, '0.01'), (82, '0.01'), (77, '0.02'), (22, '0.02'), (83, '0.02'), (11, '0.02'), (66, '0.02'), (31, '0.02'), (80, '0.02'), (92, '0.02'), (39, '0.03'), (27, '0.03'), (70, '0.04'), (41, '0.04'), (51, '0.04'), (4, '0.04'), (7, '0.05'), (13, '0.05'), (97, '0.06'), (60, '0.06'), (42, '0.06'), (96, '0.06'), (95, '0.06'), (30, '0.07'), (81, '0.07'), (52, '0.07'), (9, '0.07'), (61, '0.07'), (84, '0.07'), (29, '0.08'), (94, '0.08'), (28, '0.11')]

Find a Model

import pandas as pd
import pandas_ml_utils as pmu
from sklearn.linear_model import LogisticRegression

df = pd.read_csv('burritos.csv')
df["with_fires"] = df["Fries"].apply(lambda x: str(x).lower() == "x")
df["price"] = df["Cost"] * -1
df = df[["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling", "Uniformity", "Salsa", "Synergy", "Wrap", "overall", "with_fires", "price"]].dropna()
fit = df.fit_classifier(pmu.SkitModel(LogisticRegression(solver='lbfgs'),
                                      pmu.FeaturesAndLabels(["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling",
                                                             "Uniformity", "Salsa", "Synergy", "Wrap", "overall"],
                                                            ["with_fires"],
                                                            targets=("price", "price"))))

fit

Save and use your model

fit.save_model("/tmp/burrito.model")

df = pd.read_csv('burritos.csv')
df["price"] = df["Cost"] * -1
df = df[["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling", "Uniformity", "Salsa", "Synergy", "Wrap", "overall", "price"]].dropna()
df.classify(pmu.Model.load("/tmp/burrito.model")).tail()

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	price
	prediction	target
	value	value_proba	value
380	False	0.251311	-6.85
381	False	0.328659	-6.85
382	False	0.064751	-11.50
383	False	0.428745	-7.89
384	False	0.265546	-7.89

TODO

• allow multiple class for classification
• replace hard coded summary objects by a summary provider function
• add more tests
• add Proximity https://stats.stackexchange.com/questions/270201/pooling-levels-of-categorical-variables-for-regression-trees/275867#275867

Wanna help?

• currently I only need binary classification
  • maybe you want to add a feature for multiple classes
• for non classification problems you might want to augment the Summary
• write some tests
• add different more charts for a better understanding/interpretation of the models
• implement hyper parameter tuning
• add feature importance

Change Log

0.0.12

• added sphinx documentation
• added multi model as regular model which has quite a big impact
  • features and labels signature changed
  • multiple targets has now the consequence that a lot of things a returning a dict now
  • everything is using now DataFrames instead of arrays after plain model invoke
• added some tests
• fixed some bugs a long the way

0.0.11

• Added Hyper parameter tuning

from hyperopt import hp

fit = df.fit_classifier(
            pdu.SkitModel(MLPClassifier(activation='tanh', hidden_layer_sizes=(60, 50), random_state=42),
                          pdu.FeaturesAndLabels(features=['vix_Close'], labels=['label'],
                                                targets=("vix_Open", "spy_Volume"))),
            test_size=0.4,
            test_validate_split_seed=42,
            hyper_parameter_space={'alpha': hp.choice('alpha', [0.001, 0.1]), 'early_stopping': True, 'max_iter': 50,
                                   '__max_evals': 4, '__rstate': np.random.RandomState(42)})

NOTE there is currently a bug in hyperot module bson has no attribute BSON ! However there is a workaround:

sudo pip uninstall bson
pip install pymongo

0.0.10

• Added support for rescaling features within the auto regressive lags. The following example re-scales the domain of min/max(featureA and featureB) to the range of -1 and 1.

FeaturesAndLabels(["featureA", "featureB", "featureC"],
                  ["labelA"],
                  feature_rescaling={("featureA", "featureC"): (-1, 1)})

• added a feature selection functionality. When starting from scratch this just helps to analyze the data to find feature importance and feature (auto) correlation. I.e. df.filtration(label_column='delta') takes all columns as features exept for the delta column (which is the label) and reduces the feature space by some heuristics.