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visualization.py
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visualization.py
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import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
class Views:
def __init__(self, config, df, results_df):
self.cfg = config
self.df = df
self.results_df=results_df
self.correlation()
self.toPandas()
def toPandas(self):
self.df_Pandas_25 = self.df.sample(False, 0.1, 42).toPandas()
self.Results_DF = pd.DataFrame(self.results_df)
def correlation(self):
corr_matrix = self.df.select([x[0] for x in self.df.dtypes if 'int' in x])
corr_matrix.show(5)
[(c[0], self.df.corr("ArrDelay", c[0])) for c in corr_matrix.dtypes]
def correlation_matrix(self):
fig, ax = plt.subplots(figsize=(11,9)) # Sample figsize in inches
sns.heatmap(self.df_Pandas_25.corr(), annot = True, ax=ax,cbar_kws={"shrink": .5}, vmax = 1, vmin = -1, center = 0, cmap='coolwarm', linewidth =.5, linecolor ='black', square = True)
plt.show()
#plt.show(block=False)
def scatterPlot(self):
features = ['TaxiOut','DepDelay']#, 'TaxiOut', 'HotIndOrigDest', 'Speed', 'HotCRSCatDepTime', 'HotCRSCatArrTime', 'HotDepTime']
for feature in features:
sns.regplot(x=self.df_Pandas_25["ArrDelay"], y=self.df_Pandas_25[feature])
plt.show()
#comparing R2 per each model
def BarChart_R2(self):
fig, axes = plt.subplots(1, 4, figsize=(15, 5), sharey=True)
sns.barplot(ax=axes[0], x=self.Results_DF.name, y=self.Results_DF.R2LR)
axes[0].set_title('Linear Regression')
axes[0].set(xlabel='Variables', ylabel='R2')
sns.barplot(ax=axes[1], x=self.Results_DF.name, y=self.Results_DF.R2RF)
axes[1].set_title('Random Forest')
axes[1].set(xlabel='Variables',ylabel='R2')
sns.barplot(ax=axes[2], x=self.Results_DF.name, y=self.Results_DF.R2DT)
axes[2].set_title('Decision Tree Regression')
axes[2].set(xlabel='Variables',ylabel='R2')
sns.barplot(ax=axes[3], x=self.Results_DF.name, y=self.Results_DF.R2GBR)
axes[3].set_title('Gradient Booster Tree Regression')
axes[3].set(xlabel='Variables',ylabel='R2')
plt.show()
#comparing MAE per each model
def BarChart_MAE(self):
fig, axes = plt.subplots(1, 4, figsize=(15, 5), sharey=True)
sns.barplot(ax=axes[0], x=self.Results_DF.name, y=self.Results_DF.maeLR)
axes[0].set_title('Linear Regression')
axes[0].set(xlabel='Variables', ylabel='MAE')
sns.barplot(ax=axes[1], x=self.Results_DF.name, y=self.Results_DF.maeRF)
axes[1].set_title('Random Forest')
axes[1].set(xlabel='Variables',ylabel='MAE')
sns.barplot(ax=axes[2], x=self.Results_DF.name, y=self.Results_DF.maeDT)
axes[2].set_title('Decision Tree Regression')
axes[2].set(xlabel='Variables',ylabel='MAE')
sns.barplot(ax=axes[3], x=self.Results_DF.name, y=self.Results_DF.maeGBR)
axes[3].set_title('Gradient Booster Tree Regression')
axes[3].set(xlabel='Variables',ylabel='MAE')
plt.show()
#comparing RMSE per each model
def BarChart_RMSE(self):
fig, axes = plt.subplots(1, 4, figsize=(15, 5), sharey=True)
sns.barplot(ax=axes[0], x=self.Results_DF.name, y=self.Results_DF.rmseLR)
axes[0].set_title('Linear Regression')
axes[0].set(xlabel='Variables', ylabel='RMSE')
sns.barplot(ax=axes[1], x=self.Results_DF.name, y=self.Results_DF.rmseRF)
axes[1].set_title('Random Forest')
axes[1].set(xlabel='Variables',ylabel='RMSE')
sns.barplot(ax=axes[2], x=self.Results_DF.name, y=self.Results_DF.rmseDT)
axes[2].set_title('Decision Tree Regression')
axes[2].set(xlabel='Variables',ylabel='RMSE')
sns.barplot(ax=axes[3], x=self.Results_DF.name, y=self.Results_DF.rmseGBR)
axes[3].set_title('Gradient Booster Tree Regression')
axes[3].set(xlabel='Variables',ylabel='RMSE')
plt.show()