CustomPairPlot class

hist class

classplot class

regplot class

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: c60evaporator@gmail.com or Twitter: https://twitter.com/c60evaporator This project is hosted at https://github.com/c60evaporator/seaborn-analyzer