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plot.py
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plot.py
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import matplotlib.pyplot as plt
from collections import Counter
from missingno import matrix, heatmap, dendrogram
from seaborn import set, boxplot, distplot, despine, pairplot
from sklearn import decomposition
def hist(series):
try:
label = series.name.capitalize()
fig = plt.figure()
plt.hist(series, 20, normed = 1, facecolor = 'blue', alpha = 0.75)
plt.xlabel(label)
plt.ylabel('Probability')
plt.title('Histogram of ' + label)
fig.savefig('datascience/' + label + '_hist.png')
plt.close(fig)
except:
pass
def boxplot(series):
try:
fig = plt.figure()
plt.boxplot(series, 0, 'rs', 0)
label = series.name.capitalize()
plt.title('Boxplot of ' + label)
fig.savefig('datascience/' + label + '_box.png')
plt.close(fig)
except:
pass
def counts_bargraph(series):
try:
fig = plt.figure()
counts = Counter(series)
length = range(len(counts))
values = list(counts.values())
keys = list(counts.keys())
label = series.name.capitalize()
plt.bar(length, values, align = 'center')
plt.xticks(length, keys)
plt.title('Bar graph of ' + label)
fig.savefig('datascience/' + label + '_bar.png')
plt.close(fig)
except:
pass
def missing_df(df):
try:
fig = plt.figure()
mat = matrix(df)
ax = plt.gca()
plt.savefig('datascience/' + 'missing_df.png')
plt.close(fig)
except:
pass
def missing_heatmap(df):
try:
fig = plt.figure()
hm = heatmap(df)
ax = plt.gca()
plt.savefig('datascience/' + 'missing_heatmap.png')
plt.close(fig)
except:
pass
def missing_dendrogram(df):
try:
fig = plt.figure()
dg = dendrogram(df)
ax = plt.gca()
plt.savefig('datascience/' + 'missing_dendrogram.png')
plt.close(fig)
except:
pass
def hist_with_boxplot_density(series):
try:
label = series.name.capitalize()
fig = plt.figure()
set(style = "ticks")
f, (ax_box, ax_hist) = plt.subplots(2, sharex = True, gridspec_kw = {"height_ratios": (.15, .85)})
boxplot(series, ax = ax_box)
distplot(series, ax = ax_hist)
ax_box.set(yticks = [])
despine(ax = ax_hist)
despine(ax=ax_box, left = True)
ax = plt.gca()
plt.savefig('datascience/' + label + '_hist_box_density.png')
plt.close(fig)
except:
pass
def vis_corr_mat(corr_mat):
try:
fig = plt.figure()
plt.matshow(corr_mat)
plt.title('Correlation Matrix')
ax = plt.gca()
plt.savefig('datascience/' + 'corr_mat.png')
plt.close(fig)
except:
pass
def pairwise_scatter(data, continuous = None):
try:
fig = plt.figure()
if continuous is not None:
data = data[continuous]
set(style="ticks", color_codes=True)
pairplot(data)
ax = plt.gca()
plt.savefig('datascience/' + 'scatter_matrix.png')
plt.close(fig)
except:
pass
def plot_raw_data(data, corr_matrix, transforms):
data[transforms["continuous"]].apply(hist)
data[transforms["continuous"]].apply(boxplot)
data[transforms["continuous"]].apply(hist_with_boxplot_density)
data[transforms["categorical"]].apply(counts_bargraph)
missing_df(data)
missing_heatmap(data)
missing_dendrogram(data)
vis_corr_mat(corr_matrix)
pairwise_scatter(data, transforms["continuous"])
def plot_processed_data(data):
visualize_truncated_svd(data, transforms["continuous"])
def visualize_truncated_svd(data, continuous):
try:
tsvd = decomposition.TruncatedSVD()
tsvd.fit(data[continuous])
data2 = tsvd.transform(data[continuous])
pairwise_scatter(data2)
except:
pass