seaborn-analyzer 0.3.6

seaborn-analyzer: data visualization of regression, classification and distribution

seaborn-analyzer

A data analysis and visualization tool using Seaborn library.

Usage

An example of using CustomPairPlot class

from seaborn_analyzer import CustomPairPlot
import seaborn as sns

titanic = sns.load_dataset("titanic")
cp = CustomPairPlot()
cp.pairanalyzer(titanic, hue='survived')

If you want to know the usage of the other classes, see API Reference and Examples

Requirements

seaborn-analyzer 0.3.6 requires

• Python >=3.6

• Numpy >=1.20.3

• Pandas >=1.2.4

• Matplotlib >=3.1.3

• Seaborn >=0.11.1

• Scipy >=1.6.3

• Scikit-learn >=0.24.2

• LightGBM >=3.3.2

Installing seaborn-analyzer

Use pip to install the binary wheels on PyPI

$ pip install seaborn-analyzer

Support

Bugs may be reported at https://github.com/c60evaporator/seaborn-analyzer/issues

API Reference

The following classes and methods are included in seaborn-analyzer

CustomPairPlot class

Method name	Summary	API Documentation	Example
pairanalyzer	Plot pair plot including scatter plot and correlation coefficient matrix simultaneously.	CustomPairPlot.pairanalyzer	example

hist class

Method name	Summary	API Documentation	Example
plot_normality	Plot normality test result and QQ plot.	hist.plot_normality	example
fit_dist	Fit distributions by maximum likelihood estimation and calculate fitting scores.	hist.fit_dist	example

classplot class

Method name	Summary	API Documentation	Example
class_separator_plot	Plot class separation lines of any scikit-learn classifier.	hist.class_separator_plot	example
class_proba_plot	Plot class prediction probability of any scikit-learn classifier.	hist.class_proba_plot	example
plot_roc_curve_multiclass	Plot ROC curve in multiclass classifier.	hist.class_separator_plot	example
roc_plot	Plot ROC curve with cross validation.	hist.class_proba_plot	example

regplot class

Method name	Summary	API Documentation	Example
linear_plot	Plot linear regression line and calculate Pearson correlation coefficient.	regplot.linear_plot	example
regression_pred_true	Plot prediction vs. true scatter plots of any scikit-learn regressor.	regplot.regression_pred_true	example
regression_plot_1d	Plot regression lines of any scikit-learn regressor with 1D explanatory variable.	regplot.regression_plot_1d	example
regression_heat_plot	Plot regression heatmaps of any scikit-learn regressor with 2 to 4D explanatory variables.	regplot.regression_heat_plot	example
average_plot	Plot relationship between one explanatory variable and predicted value by line graph.	regplot.average_plot	example

Examples

CustomPairPlot.pairanalyzer

from seaborn_analyzer import CustomPairPlot
import seaborn as sns
titanic = sns.load_dataset("titanic")
cp = CustomPairPlot()
cp.pairanalyzer(titanic, hue='survived')

hist.plot_normality

from seaborn_analyzer import hist
from sklearn.datasets import load_boston
import pandas as pd
df = pd.DataFrame(load_boston().data, columns= load_boston().feature_names)
hist.plot_normality(df, x='LSTAT', norm_hist=False, rounddigit=5)

hist.fit_dist

from seaborn_analyzer import hist
from sklearn.datasets import load_boston
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
df = pd.DataFrame(load_boston().data, columns= load_boston().feature_names)
all_params, all_scores = hist.fit_dist(df, x='LSTAT', dist=['norm', 'gamma', 'lognorm', 'uniform'])
df_scores = pd.DataFrame(all_scores).T
df_scores

classplot.class_separator_plot

import seaborn as sns
from sklearn.svm import SVC
from seaborn_analyzer import classplot
iris = sns.load_dataset("iris")
clf = SVC()
classplot.class_separator_plot(clf, ['petal_width', 'petal_length'], 'species', iris)

classplot.class_proba_plot

import seaborn as sns
from sklearn.svm import SVC
from seaborn_analyzer import classplot
iris = sns.load_dataset("iris")
clf = SVC()
classplot.class_proba_plot(clf, ['petal_width', 'petal_length'], 'species', iris,
                           proba_type='imshow')

classplot.plot_roc_curve_multiclass

import seaborn as sns
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
from seaborn_analyzer import classplot
# Load dataset
iris = sns.load_dataset("iris")
OBJECTIVE_VARIALBLE = 'species'  # Objective variable
USE_EXPLANATORY = ['petal_width', 'petal_length', 'sepal_width', 'sepal_length']  # Explantory variables
y = iris[OBJECTIVE_VARIALBLE].values
X = iris[USE_EXPLANATORY].values
# Add random noise features
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 10 * n_features)]
# Plot ROC curve in multiclass classification
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, random_state=42)
estimator = SVC(probability=True, random_state=42)
classplot.plot_roc_curve_multiclass(estimator, X_train, y_train,
                                    X_test=X_test, y_test=y_test)
plt.plot([0, 1], [0, 1], label='Chance', alpha=0.8,
        lw=2, color='red', linestyle='--')
plt.legend(loc='lower right')

classplot.roc_plot

from lightgbm import LGBMClassifier
import seaborn as sns
import matplotlib.pyplot as plt
from seaborn_analyzer import classplot
# Load dataset
iris = sns.load_dataset("iris")
OBJECTIVE_VARIALBLE = 'species'  # Objective variable
USE_EXPLANATORY = ['petal_width', 'petal_length', 'sepal_width', 'sepal_length']  # Explantory variables
y = iris[OBJECTIVE_VARIALBLE].values
X = iris[USE_EXPLANATORY].values
fit_params = {'verbose': 0,
            'early_stopping_rounds': 10,
            'eval_metric': 'rmse',
            'eval_set': [(X, y)]
            }
# Plot ROC curve with cross validation in multiclass classification
estimator = LGBMClassifier(random_state=42, n_estimators=10000)
fig, axes = plt.subplots(4, 1, figsize=(6, 24))
classplot.roc_plot(estimator, X, y, ax=axes, cv=3, fit_params=fit_params)

regplot.linear_plot

from seaborn_analyzer import regplot
import seaborn as sns
iris = sns.load_dataset("iris")
regplot.linear_plot(x='petal_length', y='sepal_length', data=iris)

regplot.regression_pred_true

import pandas as pd
from seaborn_analyzer import regplot
import seaborn as sns
from sklearn.linear_model import LinearRegression
df_temp = pd.read_csv(f'./sample_data/temp_pressure.csv')
regplot.regression_pred_true(LinearRegression(), x=['altitude', 'latitude'], y='temperature', data=df_temp)

regplot.regression_plot_1d

from seaborn_analyzer import regplot
import seaborn as sns
from sklearn.svm import SVR
iris = sns.load_dataset("iris")
regplot.regression_plot_1d(SVR(), x='petal_length', y='sepal_length', data=iris)

regplot.regression_heat_plot

import pandas as pd
from sklearn.linear_model import LinearRegression
from seaborn_analyzer import regplot
df_temp = pd.read_csv(f'./sample_data/temp_pressure.csv')
regplot.regression_heat_plot(LinearRegression(), x=['altitude', 'latitude'], y='temperature', data=df_temp)

regplot.average_plot

import seaborn as sns
from sklearn.svm import SVR
from seaborn_analyzer import regplot
iris = sns.load_dataset("iris")
svr = SVR()
features = ['petal_width', 'petal_length', 'sepal_width']
X = iris[features].values
y = iris['sepal_length'].values
regplot.average_plot(svr, X, y, x_colnames=features, cv=3)

Contact

If you have any questions or comments about seaborn-analyzer, please feel free to contact me via eMail: [e-mail] or Twitter: https://twitter.com/c60evaporator This project is hosted at https://github.com/c60evaporator/seaborn-analyzer