Augment pandas DataFrame with methods for machine learning
Project description
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.
- install
- analyze your features
- find a model
- save and reuse your model
Or read the docs.
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')]
Fit a Model
Once you know your features you can start to try out different models i.e. a very basic Logistic Regression. It is also possible to search through a set of hyper parameters.
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
Once you are happy with your model you can save it and apply it on any DataFrame which serves the needed columns by your features.
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()
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
- add whatever you need for yourself and share it with us
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for pandas_ml_utils-0.0.15-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 365ce41597f0f86612df439ca5b38b42258c6ac68519e3cdc32afad0da12e224 |
|
MD5 | d9397ea3eb15cc4245a5f9bc51970cbb |
|
BLAKE2b-256 | eea977fb11afd987d0ec10ec4cfc2d127c9e157a503f24f93a980377aee771e4 |