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import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import numpy as np
class VisualizationEngine:
def __init__(self):
plt.style.use('seaborn-v0_8')
self.color_palette = sns.color_palette("husl", 8)
def create_visualizations(self, df, selected_features):
"""Create various visualizations based on selected features"""
plots = []
if not selected_features:
selected_features = df.columns[:4] # Default to first 4 columns
for feature in selected_features:
if feature in df.columns and feature != 'ID':
if df[feature].dtype in ['int64', 'float64']:
# Numerical feature visualizations
plots.extend(self._create_numerical_plots(df, feature))
else:
# Categorical feature visualizations
plots.extend(self._create_categorical_plots(df, feature))
# Create comparison plots
if len(selected_features) >= 2:
plots.extend(self._create_comparison_plots(df, selected_features))
return plots
def _create_numerical_plots(self, df, feature):
"""Create plots for numerical features"""
plots = []
# Histogram
plt.figure(figsize=(10, 6))
plt.hist(df[feature], bins=30, alpha=0.7, color=self.color_palette[0], edgecolor='black')
plt.title(f'{feature} Distribution')
plt.xlabel(feature)
plt.ylabel('Frequency')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plot_name = f'{feature.lower().replace(" ", "_")}_histogram.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
# Box plot
plt.figure(figsize=(8, 6))
plt.boxplot(df[feature], patch_artist=True,
boxprops=dict(facecolor=self.color_palette[1]))
plt.title(f'{feature} Box Plot')
plt.ylabel(feature)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plot_name = f'{feature.lower().replace(" ", "_")}_boxplot.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
# Density plot
plt.figure(figsize=(10, 6))
df[feature].plot(kind='density', color=self.color_palette[2], linewidth=2)
plt.title(f'{feature} Density Plot')
plt.xlabel(feature)
plt.ylabel('Density')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plot_name = f'{feature.lower().replace(" ", "_")}_density.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
return plots
def _create_categorical_plots(self, df, feature):
"""Create plots for categorical features"""
plots = []
value_counts = df[feature].value_counts()
# Bar plot
plt.figure(figsize=(12, 6))
bars = plt.bar(value_counts.index, value_counts.values,
color=self.color_palette[:len(value_counts)])
plt.title(f'{feature} Distribution')
plt.xlabel(feature)
plt.ylabel('Count')
plt.xticks(rotation=45)
# Add value labels on bars
for bar in bars:
height = bar.get_height()
plt.text(bar.get_x() + bar.get_width()/2., height,
f'{int(height)}', ha='center', va='bottom')
plt.tight_layout()
plot_name = f'{feature.lower().replace(" ", "_")}_barplot.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
# Pie chart
plt.figure(figsize=(10, 8))
plt.pie(value_counts.values, labels=value_counts.index, autopct='%1.1f%%',
colors=self.color_palette[:len(value_counts)])
plt.title(f'{feature} Distribution (Pie Chart)')
plt.tight_layout()
plot_name = f'{feature.lower().replace(" ", "_")}_piechart.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
return plots
def _create_comparison_plots(self, df, features):
"""Create comparison plots between features"""
plots = []
numeric_features = [f for f in features if df[f].dtype in ['int64', 'float64']]
categorical_features = [f for f in features if df[f].dtype in ['object', 'category']]
# Scatter plots for numeric features
if len(numeric_features) >= 2:
for i in range(len(numeric_features)):
for j in range(i+1, len(numeric_features)):
plt.figure(figsize=(10, 8))
plt.scatter(df[numeric_features[i]], df[numeric_features[j]],
alpha=0.6, color=self.color_palette[0])
plt.xlabel(numeric_features[i])
plt.ylabel(numeric_features[j])
plt.title(f'{numeric_features[i]} vs {numeric_features[j]}')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plot_name = f'{numeric_features[i].lower().replace(" ", "_")}_vs_{numeric_features[j].lower().replace(" ", "_")}_scatter.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
# Box plots for numeric vs categorical
if numeric_features and categorical_features:
for num_feat in numeric_features[:2]: # Limit to avoid too many plots
for cat_feat in categorical_features[:2]:
plt.figure(figsize=(12, 8))
df.boxplot(column=num_feat, by=cat_feat, ax=plt.gca())
plt.title(f'{num_feat} by {cat_feat}')
plt.suptitle('') # Remove default title
plt.xticks(rotation=45)
plt.tight_layout()
plot_name = f'{num_feat.lower().replace(" ", "_")}_by_{cat_feat.lower().replace(" ", "_")}_boxplot.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
# Correlation heatmap for numeric features
if len(numeric_features) >= 2:
plt.figure(figsize=(10, 8))
correlation_matrix = df[numeric_features].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0,
square=True, linewidths=0.5)
plt.title('Feature Correlation Matrix')
plt.tight_layout()
plot_name = 'selected_features_correlation.png'
plt.savefig(plot_name, dpi=300, bbox_inches='tight')
plots.append(plot_name)
plt.close()
return plots
def create_interactive_plots(self, df, features):
"""Create interactive Plotly visualizations"""
plots = []
for feature in features:
if feature in df.columns and feature != 'ID':
if df[feature].dtype in ['int64', 'float64']:
# Interactive histogram
fig = px.histogram(df, x=feature, title=f'{feature} Distribution')
fig.write_html(f'{feature.lower().replace(" ", "_")}_interactive_hist.html')
plots.append(f'{feature.lower().replace(" ", "_")}_interactive_hist.html')
else:
# Interactive bar chart
value_counts = df[feature].value_counts()
fig = px.bar(x=value_counts.index, y=value_counts.values,
title=f'{feature} Distribution')
fig.write_html(f'{feature.lower().replace(" ", "_")}_interactive_bar.html')
plots.append(f'{feature.lower().replace(" ", "_")}_interactive_bar.html')
return plots |