WineQualityClassifier/data_vis.py at main · anth0nyhle/WineQualityClassifier · GitHub

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#!/usr/bin/env python

import matplotlib.pyplot as plt
plt.switch_backend('agg')
import pandas as pd
import numpy as np
import seaborn as sns

# Read in white wine data
white = pd.read_csv("winequality-white.csv", sep=';')

# Read in red wine data
red = pd.read_csv("winequality-red.csv", sep=';')

######################################################################
# Alcohol plots
# fig, ax = plt.subplots(1, 2)
#
# ax[0].hist(red.alcohol, 10, facecolor='red', alpha=0.5, label="Red wine")
# ax[1].hist(white.alcohol, 10, facecolor='white', ec="black", lw=0.5, alpha=0.5, label="White wine")
#
# fig.subplots_adjust(left=0, right=1, bottom=0, top=0.5, hspace=0.05, wspace=1)
# ax[0].set_ylim([0, 1000])
# ax[0].set_xlabel("Alcohol in % Vol")
# ax[0].set_ylabel("Frequency")
# ax[1].set_xlabel("Alcohol in % Vol")
# ax[1].set_ylabel("Frequency")
# # ax[0].legend(loc='best')
# # ax[1].legend(loc='best')
# fig.suptitle("Distribution of Alcohol in % Vol")
#
# plt.savefig("alcohol_dist")
#
# print(np.histogram(red.alcohol, bins=[7, 8, 9, 10, 11, 12, 13, 14, 15]))
# print(np.histogram(white.alcohol, bins=[7, 8, 9, 10, 11, 12, 13, 14, 15]))

# Sulphates plots
# import matplotlib.pyplot as plt
#
# fig, ax = plt.subplots(1, 2, figsize=(8, 4))
#
# ax[0].scatter(red['quality'], red["sulphates"], color="red")
# ax[1].scatter(white['quality'], white['sulphates'], color="white", edgecolors="black", lw=0.5)
#
# ax[0].set_title("Red Wine")
# ax[1].set_title("White Wine")
# ax[0].set_xlabel("Quality")
# ax[1].set_xlabel("Quality")
# ax[0].set_ylabel("Sulphates")
# ax[1].set_ylabel("Sulphates")
# ax[0].set_xlim([0,10])
# ax[1].set_xlim([0,10])
# ax[0].set_ylim([0,2.5])
# ax[1].set_ylim([0,2.5])
# fig.subplots_adjust(wspace=0.5)
# fig.suptitle("Wine Quality by Amount of Sulphates")
#
# plt.savefig("sulphates_dist")

######################################################################
# Acidity plots
# np.random.seed(570)
#
# redlabels = np.unique(red['quality'])
# whitelabels = np.unique(white['quality'])
#
# import matplotlib.pyplot as plt
#
# fig, ax = plt.subplots(1, 2, figsize=(8, 4))
# redcolors = np.random.rand(6, 4)
# whitecolors = np.append(redcolors, np.random.rand(1, 4), axis=0)
#
# for i in range(len(redcolors)):
#     redy = red['alcohol'][red.quality == redlabels[i]]
#     redx = red['volatile acidity'][red.quality == redlabels[i]]
#     ax[0].scatter(redx, redy, c=redcolors[i])
# for i in range(len(whitecolors)):
#     whitey = white['alcohol'][white.quality == whitelabels[i]]
#     whitex = white['volatile acidity'][white.quality == whitelabels[i]]
#     ax[1].scatter(whitex, whitey, c=whitecolors[i])
#
# ax[0].set_title("Red Wine")
# ax[1].set_title("White Wine")
# ax[0].set_xlim([0, 1.7])
# ax[1].set_xlim([0, 1.7])
# ax[0].set_ylim([5, 15.5])
# ax[1].set_ylim([5, 15.5])
# ax[0].set_xlabel("Volatile Acidity")
# ax[0].set_ylabel("Alcohol")
# ax[1].set_xlabel("Volatile Acidity")
# ax[1].set_ylabel("Alcohol")
# # ax[0].legend(redlabels, loc='best', bbox_to_anchor=(1.3, 1))
# ax[1].legend(whitelabels, loc='best', bbox_to_anchor=(1.3, 1))
# # fig.suptitle("Alcohol - Volatile Acidity")
# fig.subplots_adjust(top=0.85, wspace=0.7)
#
# plt.savefig("alo_vs_acid")

######################################################################
# Preprocess data
# Add `type` column to `red` with value 1
red['type'] = 1

# Add `type` column to `white` with value 0
white['type'] = 0

# Append `white` to `red`
wines = red.append(white, ignore_index=True)

# Plot correlation matrix
corr = wines.corr()
sns.heatmap(corr,
            xticklabels=corr.columns.values,
            yticklabels=corr.columns.values)

plt.savefig("correlation_mat")