pandas, scikit-learn, xgboost and seaborn integration

Related tags

Machine Learningpandas-ml
Overview

pandas-ml

Latest Docs https://travis-ci.org/pandas-ml/pandas-ml.svg?branch=master

Overview

pandas, scikit-learn and xgboost integration.

Installation

$ pip install pandas_ml

Documentation

http://pandas-ml.readthedocs.org/en/stable/

Example

>>> import pandas_ml as pdml
>>> import sklearn.datasets as datasets

# create ModelFrame instance from sklearn.datasets
>>> df = pdml.ModelFrame(datasets.load_digits())
>>> type(df)
<class 'pandas_ml.core.frame.ModelFrame'>

# binarize data (features), not touching target
>>> df.data = df.data.preprocessing.binarize()
>>> df.head()
   .target  0  1  2  3  4  5  6  7  8 ...  54  55  56  57  58  59  60  61  62  63
0        0  0  0  1  1  1  1  0  0  0 ...   0   0   0   0   1   1   1   0   0   0
1        1  0  0  0  1  1  1  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
2        2  0  0  0  1  1  1  0  0  0 ...   1   0   0   0   0   1   1   1   1   0
3        3  0  0  1  1  1  1  0  0  0 ...   1   0   0   0   1   1   1   1   0   0
4        4  0  0  0  1  1  0  0  0  0 ...   0   0   0   0   0   1   1   1   0   0
[5 rows x 65 columns]

# split to training and test data
>>> train_df, test_df = df.model_selection.train_test_split()

# create estimator (accessor is mapped to sklearn namespace)
>>> estimator = df.svm.LinearSVC()

# fit to training data
>>> train_df.fit(estimator)

# predict test data
>>> test_df.predict(estimator)
0     4
1     2
2     7
...
448    5
449    8
Length: 450, dtype: int64

# Evaluate the result
>>> test_df.metrics.confusion_matrix()
Predicted   0   1   2   3   4   5   6   7   8   9
Target
0          52   0   0   0   0   0   0   0   0   0
1           0  37   1   0   0   1   0   0   3   3
2           0   2  48   1   0   0   0   1   1   0
3           1   1   0  44   0   1   0   0   3   1
4           1   0   0   0  43   0   1   0   0   0
5           0   1   0   0   0  39   0   0   0   0
6           0   1   0   0   1   0  35   0   0   0
7           0   0   0   0   2   0   0  42   1   0
8           0   2   1   0   1   0   0   0  33   1
9           0   2   1   2   0   0   0   0   1  38

Supported Packages

  • scikit-learn
  • patsy
  • xgboost
Comments
  • Fixed imports of deprecated modules which were removed in pandas 0.24.0

    Fixed imports of deprecated modules which were removed in pandas 0.24.0

    Certain functions were deprecated in a previous version of pandas and moved to a different module (see #117). This PR fixes the imports of those functions.

    opened by kristofve 8
  • REL: v0.4.0

    REL: v0.4.0

    • [x] Compat/test for sklearn 0.18.0 (#81)
      • [x] initial fix (#81)
      • [x] wrapper for cross validation classes (re-enable skipped tests) (#85)
      • [x] tests for multioutput (#86)
      • [x] Update doc
    • [x] Compat/test for pandas 0.19.0 (#83)
    • [x] Update release note (#88)
    opened by sinhrks 4
  • Importation error

    Importation error

    I tried to import pandas_ml but it gave the error :

    AttributeError: type object 'NDFrame' has no attribute 'groupby'

    I'm running python3.8.1 and I installed pandas_ml via pip (version 20.0.2)

    I dig in the code, error is l.80 of file series.py

    @Appender(pd.core.generic.NDFrame.groupby.__doc__)

    Here pandas is imported at the top of the file with a classic import pandas as pd

    I guess there is a problem with the versions...

    Thanks in advance for any help

    opened by ierezell 2
  • Confusion Matrix no accessible

    Confusion Matrix no accessible

    Hi,

    I've been using confusion_matrix since it was an independent package. I've installed pandas_ml to continue using the package, but it seems that the setup.py script does not install the package.

    Could it be an issue with the find_packages function?

    opened by mmartinortiz 2
  • Seaborn Scatterplot matrix / pairplot integration

    Seaborn Scatterplot matrix / pairplot integration 

    import seaborn as sns
sns.set()

df = sns.load_dataset("iris")
sns.pairplot(df, hue="species")

    displays

    iris_scatter_matrix

    but pairplot doesn't work the same way with ModelFrame

    import pandas as pd
pd.set_option('max_rows', 10)
import sklearn.datasets as datasets
import pandas_ml as pdml  # https://github.com/pandas-ml/pandas-ml
import seaborn as sns
import matplotlib.pyplot as plt
df = pdml.ModelFrame(datasets.load_iris())
sns.pairplot(df, hue=".target")

    iris_modelframe

    There is some useless subplots

    opened by scls19fr 2
  • Error while running train.py from speech commands in tensorflow examples.

    Error while running train.py from speech commands in tensorflow examples.

    Have the following error: File "train.py", line 27, in <module> from callbacks import ConfusionMatrixCallback File "/home/tesseract/ayush_workspace/NLP/WakeWord/tensorflow_trainer/ml/callbacks.py", line 21, in <module> from pandas_ml import ConfusionMatrix File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/__init__.py", line 3, in <module> from pandas_ml.core import ModelFrame, ModelSeries # noqa File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/__init__.py", line 3, in <module> from pandas_ml.core.frame import ModelFrame # noqa File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/frame.py", line 18, in <module> from pandas_ml.core.series import ModelSeries File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/series.py", line 11, in <module> class ModelSeries(ModelTransformer, pd.Series): File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/series.py", line 80, in ModelSeries @Appender(pd.core.generic.NDFrame.groupby.__doc__) AttributeError: type object 'NDFrame' has no attribute 'groupby' Happening with both version 5 and 6.1

    opened by ayush7 1
  • error for example https://pandas-ml.readthedocs.io/en/latest/xgboost.html

    error for example https://pandas-ml.readthedocs.io/en/latest/xgboost.html

    code from example https://pandas-ml.readthedocs.io/en/latest/xgboost.html '''import pandas_ml as pdml import sklearn.datasets as datasets df = pdml.ModelFrame(datasets.load_digits()) train_df, test_df = df.cross_validation.train_test_split() estimator = df.xgboost.XGBClassifier() train_df.fit(estimator) predicted = test_df.predict(estimator) q=1 test_df.metrics.confusion_matrix() train_df.xgboost.plot_importance()

    tuned_parameters = [{'max_depth': [3, 4]}] cv = df.grid_search.GridSearchCV(df.xgb.XGBClassifier(), tuned_parameters, cv=5)

    df.fit(cv) df.grid_search.describe(cv) q=1

    '''

    gives error ''' File "E:\Pandas\my_code\S_pandas_ml_feb27.py", line 10, in train_df.xgboost.plot_importance() File "C:\Users\sndr\Anaconda3\Lib\site-packages\pandas_ml\xgboost\base.py", line 61, in plot_importance return xgb.plot_importance(self._df.estimator.booster(),

    builtins.TypeError: 'str' object is not callable ''' I use Windows and 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 10:22:32) [MSC v.1900 64 bit (AMD64)] Python Type "help", "copyright", "credits" or "license" for more information.

    opened by Sandy4321 1
  • pandas 0.24.0 has deprecated pandas.util.decorators

    pandas 0.24.0 has deprecated pandas.util.decorators

    See https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.24.0.html#deprecations

    This causes the import statement in https://github.com/pandas-ml/pandas-ml/blob/master/pandas_ml/core/frame.py to break.

    Looks like just need to change it to 'from pandas.utils'

    opened by usul83 1
  • 'mean_absoloute_error

    'mean_absoloute_error

    from sklearn import metrics print('MAE:',metrics.mean_absoloute_error(y_test,y_pred)) module 'sklearn.metrics' has no attribute 'mean_absoloute_error This error is occurred..any solution

    opened by vikramk1507 0
  • AttributeError: type object 'NDFrame' has no attribute 'groupby'

    AttributeError: type object 'NDFrame' has no attribute 'groupby'

    AttributeError: type object 'NDFrame' has no attribute 'groupby'

    from pandas_ml import ConfusionMatrix cm = ConfusionMatrix(actu, pred) cm.print_stats()

    AttributeError Traceback (most recent call last) in ----> 1 from pandas_ml import confusion_matrix 2 3 cm = ConfusionMatrix(actu, pred) 4 cm.print_stats()

    /usr/local/lib/python3.8/site-packages/pandas_ml/init.py in 1 #!/usr/bin/env python 2 ----> 3 from pandas_ml.core import ModelFrame, ModelSeries # noqa 4 from pandas_ml.tools import info # noqa 5 from pandas_ml.version import version as version # noqa

    /usr/local/lib/python3.8/site-packages/pandas_ml/core/init.py in 1 #!/usr/bin/env python 2 ----> 3 from pandas_ml.core.frame import ModelFrame # noqa 4 from pandas_ml.core.series import ModelSeries # noqa

    /usr/local/lib/python3.8/site-packages/pandas_ml/core/frame.py in 16 from pandas_ml.core.accessor import _AccessorMethods 17 from pandas_ml.core.generic import ModelPredictor, _shared_docs ---> 18 from pandas_ml.core.series import ModelSeries 19 20

    /usr/local/lib/python3.8/site-packages/pandas_ml/core/series.py in 9 10 ---> 11 class ModelSeries(ModelTransformer, pd.Series): 12 """ 13 Wrapper for pandas.Series to support sklearn.preprocessing

    /usr/local/lib/python3.8/site-packages/pandas_ml/core/series.py in ModelSeries() 78 return df 79 ---> 80 @Appender(pd.core.generic.NDFrame.groupby.doc) 81 def groupby(self, by=None, axis=0, level=None, as_index=True, sort=True, 82 group_keys=True, squeeze=False):

    AttributeError: type object 'NDFrame' has no attribute 'groupby'

    opened by gfranco008 5
  • AttributeError: module 'sklearn.metrics' has no attribute 'jaccard_similarity_score'

    AttributeError: module 'sklearn.metrics' has no attribute 'jaccard_similarity_score'

    I am using scikit-learn version 0.23.1 and I get the following error: AttributeError: module 'sklearn.metrics' has no attribute 'jaccard_similarity_score' when calling the function ConfusionMatrix.

    opened by petraknovak 11
  • Error while running train.py from speech commands in tensorflow examples. AttributeError: type object 'NDFrame' has no attribute 'groupby'

    Error while running train.py from speech commands in tensorflow examples. AttributeError: type object 'NDFrame' has no attribute 'groupby'

    Have the following error: File "train.py", line 27, in <module> from callbacks import ConfusionMatrixCallback File "/home/tesseract/ayush_workspace/NLP/WakeWord/tensorflow_trainer/ml/callbacks.py", line 21, in <module> from pandas_ml import ConfusionMatrix File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/__init__.py", line 3, in <module> from pandas_ml.core import ModelFrame, ModelSeries # noqa File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/__init__.py", line 3, in <module> from pandas_ml.core.frame import ModelFrame # noqa File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/frame.py", line 18, in <module> from pandas_ml.core.series import ModelSeries File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/series.py", line 11, in <module> class ModelSeries(ModelTransformer, pd.Series): File "/home/tesseract/anaconda3/envs/ciao/lib/python3.6/site-packages/pandas_ml/core/series.py", line 80, in ModelSeries @Appender(pd.core.generic.NDFrame.groupby.__doc__) AttributeError: type object 'NDFrame' has no attribute 'groupby' Happening with both version 5 and 6.1

    opened by ayush7 3
  • Pandas 1.0.0rc0/0.6.1 module 'sklearn.preprocessing' has no attribute 'Imputer'

    Pandas 1.0.0rc0/0.6.1 module 'sklearn.preprocessing' has no attribute 'Imputer'

    SKLEARN

    sklearn.preprocessing.Imputer Warning DEPRECATED

    class sklearn.preprocessing.Imputer(*args, **kwargs)[source] Imputation transformer for completing missing values.

    Read more in the User Guide.

    
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-1-e0471065d85c> in <module>
      1 import pandas as pd
      2 import numpy as np
----> 3 import pandas_ml as pdml
      4 a1 = np.random.randint(0,2,size=(100,2))
      5 df = pd.DataFrame(a1,columns=['i1','i2'])

C:\g\test\lib\pandas_ml\__init__.py in <module>
      1 #!/usr/bin/env python
      2 
----> 3 from pandas_ml.core import ModelFrame, ModelSeries       # noqa
      4 from pandas_ml.tools import info                         # noqa
      5 from pandas_ml.version import version as __version__     # noqa

C:\g\test\lib\pandas_ml\core\__init__.py in <module>
      1 #!/usr/bin/env python
      2 
----> 3 from pandas_ml.core.frame import ModelFrame       # noqa
      4 from pandas_ml.core.series import ModelSeries     # noqa

C:\g\test\lib\pandas_ml\core\frame.py in <module>
      8 
      9 import pandas_ml.imbaccessors as imbaccessors
---> 10 import pandas_ml.skaccessors as skaccessors
     11 import pandas_ml.smaccessors as smaccessors
     12 import pandas_ml.snsaccessors as snsaccessors

C:\g\test\lib\pandas_ml\skaccessors\__init__.py in <module>
     17 from pandas_ml.skaccessors.neighbors import NeighborsMethods                      # noqa
     18 from pandas_ml.skaccessors.pipeline import PipelineMethods                        # noqa
---> 19 from pandas_ml.skaccessors.preprocessing import PreprocessingMethods              # noqa
     20 from pandas_ml.skaccessors.svm import SVMMethods                                  # noqa

C:\g\test\lib\pandas_ml\skaccessors\preprocessing.py in <module>
     11     _keep_col_classes = [pp.Binarizer,
     12                          pp.FunctionTransformer,
---> 13                          pp.Imputer,
     14                          pp.KernelCenterer,
     15                          pp.LabelEncoder,

AttributeError: module 'sklearn.preprocessing' has no attribute 'Imputer'
    opened by apiszcz 11
Releases(v0.6.1)
Owner
GitHub Repository
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.4k Jan 15, 2022
Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai) Programming assignments from all courses i

Aman Chadha 173 Jan 05, 2023
Lingtrain Alignment Studio is an ML based app for texts alignment on different languages.

Lingtrain Alignment Studio Intro Lingtrain Alignment Studio is the ML based app for accurate texts alignment on different languages. Extracts parallel

Sergei Averkiev 186 Jan 03, 2023
Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning

Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning My

3 Apr 10, 2022
Toolss - Automatic installer of hacking tools (ONLY FOR TERMUKS!)

Tools Автоматический установщик хакерских утилит (ТОЛЬКО ДЛЯ ТЕРМУКС!) Оригиналь

14 Jan 05, 2023
Forecast dynamically at scale with this unique package. pip install scalecast

🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels

Michael Keith 158 Jan 03, 2023
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
Automated Machine Learning with scikit-learn

auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here

AutoML-Freiburg-Hannover 6.7k Jan 07, 2023
Practical Time-Series Analysis, published by Packt

Practical Time-Series Analysis This is the code repository for Practical Time-Series Analysis, published by Packt. It contains all the supporting proj

Packt 325 Dec 23, 2022
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM

Joaquín Amat Rodrigo 297 Jan 09, 2023
Stacked Generalization (Ensemble Learning)

Stacking (stacked generalization) Overview ikki407/stacking - Simple and useful stacking library, written in Python. User can use models of scikit-lea

Ikki Tanaka 192 Dec 23, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022
A data preprocessing and feature engineering script for a machine learning pipeline is prepared.

FEATURE ENGINEERING Business Problem: A data preprocessing and feature engineering script for a machine learning pipeline needs to be prepared. It is

Pinar Oner 7 Dec 18, 2021
An implementation of Relaxed Linear Adversarial Concept Erasure (RLACE)

Background This repository contains an implementation of Relaxed Linear Adversarial Concept Erasure (RLACE). Given a dataset X of dense representation

Shauli Ravfogel 4 Apr 13, 2022
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows.

An open-source, low-code machine learning library in Python 🚀 Version 2.3.5 out now! Check out the release notes here. Official • Docs • Install • Tu

PyCaret 6.7k Jan 08, 2023
ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T

ClearML 4k Jan 09, 2023
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models. Solve a variety of tasks with pre-trained models or finetune them in

Backprop 227 Dec 10, 2022
Distributed scikit-learn meta-estimators in PySpark

sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn

Ibotta 282 Dec 09, 2022
Pytools is an open source library containing general machine learning and visualisation utilities for reuse

pytools is an open source library containing general machine learning and visualisation utilities for reuse, including: Basic tools for API developmen

BCG Gamma 26 Nov 06, 2022
Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks.

Databricks Certification Spark Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along

19 Dec 13, 2022