def showCorrelation(gripperjack_nr, part):
data = DynamicCsvConverter(gripperjack_nr, part, '5min', 'max',
pd.read_csv(
'C:\\Users\\Lukassen\\PycharmProjects\\GelredomeVeldErrorVoorspellen\\Recources\\Volledige_Gelredome_Data_CSV.csv',
index_col=False))
data = data.make_file()
# to see correlation with the to be predicted remove 'to_be_predicted' from drop columns and put the 'to_be_predicted variable in the data.pop method'
data = data.drop(columns=['Timestamp'])
data = data.dropna()
y = data.pop('to_be_predicted')
X = data
# Create a list of the feature names
features = np.array(data.columns)
# Create a list of the discrete features
discrete = [False for _ in range(len(features))]
discrete[1] = True
# Instantiate the visualizer
visualizer = FeatureCorrelation(labels=features, size=(1200, 700))
visualizer.title = part
visualizer.fit(X, y)
values.append(visualizer.scores_)
visualizer.show()
from sklearn import datasets
from yellowbrick.target import FeatureCorrelation
# Load the regression dataset
data = datasets.load_diabetes()
X, y = data['data'], data['target']
# Create a list of the feature names
features = np.array(data['feature_names'])
# Instantiate the visualizer
visualizer = FeatureCorrelation(labels=features)
visualizer.fit(X, y) # Fit the data to the visualizer
visualizer.show()
## PCA - Principal Component Analysis https://www.kaggle.com/ryanholbrook/principal-component-analysis
from sklearn.decomposition import PCA
# Create principal components
pca = PCA()
X_pca = pca.fit_transform(X_scaled)
# Convert to dataframe
component_names = [f"PC{i+1}" for i in range(X_pca.shape[1])]
X_pca = pd.DataFrame(X_pca, columns=component_names)
## Target Encoding
from category_encoders import MEstimateEncoder
from yellowbrick.target import FeatureCorrelation
rc['xtick.labelsize'] = 15.0
rc['ytick.labelsize'] = 15.0
rc['xtick.direction'] = 'out'
rc['axes.labelsize'] = 15.0
rc['axes.titlesize'] = 18.0
rc['savefig.format'] = 'png'
rc['savefig.dpi'] = 600
rc['legend.fontsize'] = 15
x = df.drop('Death_Event', axis=1)
y = df['Death_Event']
fig = plt.figure(figsize=(8, 6))
corr = FeatureCorrelation(method='pearson', label=x.columns,
sort=True).fit(x, y)
plt.savefig('../../outputs/visuals/correlations')
corr.show()
fig, ax = plt.subplots(figsize=(20, 10))
sns.heatmap(df.corr(), annot=True, square=False, ax=ax)
ax.set_title('Correlations between features')
plt.savefig('../../outputs/visuals/correlations_all')
plt.show()
# Age distribution of Patients
fig, ax = plt.subplots(figsize=(8, 6))
sns.kdeplot(df['Age'], legend=False, shade=True, ax=ax)
plt.savefig('../../outputs/visuals/age_distribution')
ax.set_title('Age Distribution of Patients')
plt.show()
plt.figure()
# Instantiate the visualizer
visualizerFC = FeatureCorrelation(labels=features,
color="rebeccapurple",
title=' ')
visualizerFC.fit(X, y)
locationFileNameFC = os.path.join('/home/ak/Documents/Research/Papers/figures',str(symbols[symbolIdx])+'_idx_'+str(idx) \
+'_label_'+str(labelName)+'_date_'+str(dateIdx)+'_label_'+str(labelsIdx)+'_FeatureCorrelation_w_depn_var.png')
plt.xlabel('', fontsize=11)
plt.xticks(fontsize=14)
plt.yticks(fontsize=12)
visualizerFC.show(outpath=locationFileNameFC)
plt.show()
#
# # Instantiate the visualizer
set_palette('yellowbrick')
plt.figure()
classes = np.array([0, 1.])
plt.xticks(fontsize=9)
visualizerRadViz = RadViz(classes=classes,
features=features,
title=' ')
visualizerRadViz.fit(X, y) # Fit the data to the visualizer
visualizerRadViz.transform(X) # Transform the data
locationFileNameRVZ = os.path.join('/home/ak/Documents/Research/Papers/figures',str(symbols[symbolIdx]) \
+'_idx_'+str(idx)+'_label_'+str(labelsIdx)+'_date_'+str(dateIdx)+'_radviz.png')