import seaborn as sns
import matplotlib.pyplot as plt
from loader import load_training, pull_features, pull_lots
training_data = load_training()
dataset = pull_lots(training_data[training_data["era"] == "era1"])
sns.pairplot(dataset, aspect=1)
plt.savefig("images/scatter/scatter_matrix_seaborn.png")
sns.pairplot(dataset, aspect=1, hue="target_bernie")
plt.savefig("images/scatter/scatter_matrix_seaborn_hue.png")
import matplotlib.pyplot as plt
from loader import load_training, pull_features, pull_lots
# selected_features = ['feature1', 'feature2', 'feature3', 'feature4', 'feature5', 'feature6',
# 'feature7', 'feature8', 'feature9', 'feature10', 'feature11', 'feature12']
def create_histogram(dataset, selected_features, group):
ax = (dataset[selected_features]).hist(ylabelsize=6, xlabelsize=5, bins=20)
for axnext in ax:
for x in axnext:
x.spines['right'].set_visible(False)
x.spines['top'].set_visible(False)
x.spines['left'].set_visible(False)
x.title.set_fontsize(7)
plt.subplots_adjust(hspace=0.5, wspace=0.5)
plt.savefig('images/other/histogram' + str(group) + '.png')
dataset = load_training()
for group in range(1, 50 - 9, 9):
selected_features = [
'feature' + str(feature) for feature in range(group, group + 9)
]
create_histogram(dataset, selected_features, group)
from loader import load_training, pull_features X = pull_features(load_training()) print(X.describe())
import pandas as pd
import matplotlib.pyplot as plt
from loader import load_training, pull_features
def create_correlation_matrix(dataset,
filename,
filepath='images/correlation/',
cmap=plt.cm.viridis):
correlation = dataset.corr()
fig, ax = plt.subplots()
matrix = ax.imshow(correlation, cmap=cmap, interpolation='nearest')
fig.colorbar(matrix)
tick_marks = [i for i in range(len(dataset.columns))]
plt.xticks(tick_marks, dataset.columns, rotation='vertical')
plt.yticks(tick_marks, dataset.columns)
# now make the axes legible - we don't need them all
for label in ax.xaxis.get_ticklabels()[1::2]:
label.set_visible(False)
for label in ax.yaxis.get_ticklabels()[1::2]:
label.set_visible(False)
plt.savefig(filepath + filename)
plt.clf()
create_correlation_matrix(pull_features(load_training()),
'correlation_matrix_all_eras.png')